Auton Lab Publication Database

Articles

2019

  1. Miller, J. K; Chen, J.; Sundermann, A. et al. Statistical outbreak detection by joining medical records and pathogen similarity. In Journal of biomedical informatics: 103126, 2019.
  2. Sundermann, AJ; Miller, JK; Marsh, JW et al. Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks. In Infection Control & …, 2019.
  3. Chen, L; Dubrawski, A; Clermont, G et al. 1257. In Critical Care …, 2019.
  4. Chen, L; Dubrawski, A; Clermont, G et al. 1257: Binarized Severity Level Of Future Instability Risk In Continuously Monitored Patients. In Critical Care …, 2019.
  5. Sheng, J; Chen, L; Xu, Y et al. 1259: Using Comparisons To Reduce Cost Of Data Annotation Required To Train Models For Bedside Monitoring. In Critical Care Medicine, 2019.
  6. Pellathy, T; Chen, L; Clermont, G et al. 58: Identifying Time Interval Features Predictive Of Hospital-acquired Venous Thromboembolism. In Critical Care …, 2019.
  7. Sheng, J; Chen, L; Xu, Y et al. 1259: USING COMPARISONS TO REDUCE COST OF DATA ANNOTATION REQUIRED TO TRAIN MODELS FOR BEDSIDE MONITORING. In Critical Care Medicine, 2019.
  8. Yoon, JH; Mu, L; Chen, L et al. Predicting tachycardia as a surrogate for instability in the intensive care unit. In Journal of clinical …, 2019.
  9. De-Arteaga, M.; Chen, J.; Huggins, P. et al. Predicting neurological recovery with Canonical Autocorrelation Embeddings. In PloS one, 2019.
  10. Kandasamy, K; Vysyaraju, KR; Neiswanger, W et al. Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly. In arXiv preprint arXiv …, 2019.
  11. Chang, WC; Li, CL; Yang, Y et al. Kernel Change-point Detection with Auxiliary Deep Generative Models. In arXiv preprint arXiv:1901.06077, 2019.
  12. Neiswanger, W; Kandasamy, K; Poczos, B et al. ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization. In arXiv preprint arXiv …, 2019.
  13. Ntampaka, M; Avestruz, C; Boada, S et al. The Role of Machine Learning in the Next Decade of Cosmology. In arXiv preprint arXiv …, 2019.
  14. Platanios, EA; Stretcu, O; Neubig, G et al. Competence-based Curriculum Learning for Neural Machine Translation. In arXiv preprint arXiv …, 2019.
  15. Ho, M; Rau, MM; Ntampaka, M et al. A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters. In arXiv preprint arXiv …, 2019.
  16. Andrews, M; Alison, J; An, S et al. End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data. In arXiv preprint arXiv …, 2019.
  17. Li, CL; Chang, WC; Mroueh, Y et al. Implicit Kernel Learning. In arXiv preprint arXiv …, 2019.
  18. Pham, H; Liang, PP; Manzini, T et al. Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities: Supplementary Material. In cs.cmu.edu, 2019.

2018

  1. Bose, EL; Clermont, G; Chen, L et al. Cardiorespiratory instability in monitored step-down unit patients: using cluster analysis to identify patterns of change. In Journal of clinical …, 2018.
  2. Liu, C; Narasimhan, SG and .., Near-light photometric stereo using circularly placed point light sources. In 2018 IEEE International …, 2018.
  3. Hravnak, M; Pellathy, T; Chen, L et al. A call to alarms: Current state and future directions in the battle against alarm fatigue. In Journal of …, 2018.
  4. Xu, Y; Balakrishnan, S; Singh, A et al. Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information. In arXiv preprint arXiv …, 2018.
  5. De-Arteaga, M; Dubrawski, A and .., Learning under selective labels in the presence of expert consistency. In arXiv preprint arXiv …, 2018.
  6. Chen, L; Admoni, H and Dubrawski, A Toward a companion robot fostering perseverance in math-a pilot study. In Human Robot Interaction for Learning …, 2018.
  7. Rabbany, R; Bayani, D and Dubrawski, A Active search of connections for case building and combating human trafficking. In Proceedings of the 24th ACM …, 2018.
  8. Xu, Y.; Balakrishnan, S.; Singh, A. et al. Interactive Linear Regression with Pairwise Comparisons. In 2018 52nd Asilomar Conference on Signals, Systems, and Computers, 2018.
  9. Yoon, J; Jeanselme, V; Chen, Y et al. Prediction for Hypotension Episode with Multigranular Data in the Intensive Care Unit. In AMERICAN …, 2018.
  10. Wertz, A; Hravnak, M; Dubrawski, A et al. 38: Sufficient Sampling Frequency For Machine Learning To Separate Monitoring Artifact From Instability. In Critical Care …, 2018.
  11. Eswaran, D; Rabbany, R; Dubrawski, AW et al. Social-Affiliation Networks: Patterns and the SOAR Model. In … European Conference on …, 2018.
  12. Gisolfi, N and Dubrawski, A Revealing Actionable Simplicity in Data. In 2018 AAAI Spring Symposium Series, 2018.
  13. Boecking, B.; Miller, K.; Kennedy, E. et al. Quantifying the Relationship between Large Public Events and Escort Advertising Behavior. In Journal of Human Trafficking, 2018.
  14. Gutierrez, K; Li, J; Challu, C et al. Double Adaptive Stochastic Gradient Optimization. In stat, 2018.
  15. Barnes, M and Dubrawski, A Dependency Leakage: Analysis and Scalable Estimators. In arXiv preprint arXiv:1807.06713, 2018.
  16. Pellathy, T; Saul, M; Clermont, G et al. 205: Accuracy Of Identifying Venous Thromboembolism By Administrative Coding Compared To Manual Review. In Critical Care Medicine, 2018.
  17. Pellathy, T; Chen, L; Dubrawski, A et al. Prevalence of Venous Thromboembolism (VTE) in an Adult Step-Down Unit Population: A Proof-of-Concept Feasibility Study for Machine Learning …. In NURSING …, 2018.
  18. Chen, L and Dubrawski, A Accelerated apprenticeship: teaching data science problem solving skills at scale. In Proceedings of the Fifth Annual ACM Conference …, 2018.
  19. De-Arteaga, M; Herlands, W; Neill, DB et al. Machine Learning for the Developing World. In ACM Transactions on …, 2018.
  20. Miller, K and Dubrawski, A Gamma-Ray Source Detection With Small Sensors. In IEEE Transactions on Nuclear Science, 2018.
  21. Gitman, I; Chen, J; Lei, E et al. Novel Prediction Techniques Based on Clusterwise Linear Regression. In arXiv preprint arXiv:1804.10742, 2018.
  22. Oliva, J. B; Dubey, A.; Zaheer, M. et al. Transformation autoregressive networks. In arXiv preprint arXiv:1801.09819, 2018.
  23. Du, SS; Zhai, X; Poczos, B et al. Gradient descent provably optimizes over-parameterized neural networks. In arXiv preprint arXiv:1810.02054, 2018.
  24. Kandasamy, K; Neiswanger, W; Schneider, J et al. Neural architecture search with bayesian optimisation and optimal transport. In Advances in Neural …, 2018.
  25. Kandasamy, K; Krishnamurthy, A and .., Parallelised bayesian optimisation via thompson sampling. In International …, 2018.
  26. Vijayarangan, S; Sodhi, P; Kini, P et al. High-throughput robotic phenotyping of energy sorghum crops. In Field and Service …, 2018.
  27. Pham, H; Manzini, T; Liang, PP et al. Seq2seq2sentiment: Multimodal sequence to sequence models for sentiment analysis. In arXiv preprint arXiv:1807.03915, 2018.
  28. Oliva, JB; Dubey, A; Zaheer, M et al. Transformation autoregressive networks. In arXiv preprint arXiv …, 2018.
  29. Singh, S and Póczos, B Minimax distribution estimation in Wasserstein distance. In arXiv preprint arXiv:1802.08855, 2018.
  30. Singh, S; Uppal, A; Li, B et al. Nonparametric density estimation under adversarial losses. In Advances in Neural …, 2018.
  31. Li, CL; Zaheer, M; Zhang, Y et al. Point cloud gan. In arXiv preprint arXiv …, 2018.
  32. Liu, Y; Li, CL and Póczos, B Classifier two-sample test for video anomaly detections. In British Machine Vision Conference 2018, BMVC …, 2018.
  33. Singh, S; Sriperumbudur, BK and Póczos, B Minimax estimation of quadratic Fourier functionals. In arXiv preprint arXiv:1803.11451, 2018.
  34. Singh, S; Póczos, B and Ma, J Minimax reconstruction risk of convolutional sparse dictionary learning. In International Conference on …, 2018.
  35. Andrews, M; Paulini, M; Gleyzer, S et al. End-to-End Physics Event Classification with the CMS Open Data: Applying Image-based Deep Learning on Detector Data to Directly Classify Collision Events at the …. In arXiv preprint arXiv …, 2018.
  36. Póczos, B and Tibshirani, R Convex Optimization, Quasi Newton Methods. In Lecture, 2018.
  37. Paria, B; Kandasamy, K and Póczos, B A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations. In arXiv preprint arXiv:1805.12168, 2018.
  38. Kandasamy, K; Neiswanger, W; Zhang, R et al. Myopic bayesian design of experiments via posterior sampling and probabilistic programming. In arXiv preprint arXiv …, 2018.
  39. Wang, Z; Dai, Z; Póczos, B et al. Characterizing and avoiding negative transfer. In arXiv preprint arXiv:1811.09751, 2018.
  40. He, S; Li, Y; Feng, Y et al. Learning to Predict the Cosmological Structure Formation. In arXiv preprint arXiv …, 2018.
  41. Singla, S; Gong, M; Ravanbakhsh, S et al. Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector. In … Conference on Medical …, 2018.
  42. Huang, L; Jiang, Z; Sun, SC et al. Legend of Wrong Mountain: Full Generation of Traditional Chinese Opera Using Multiple Machine Learning Algorithms. In nips2018creativity.github.io, 2018.
  43. Menon, A; Childs, CM; Poczós, B et al. Molecular Engineering of Superplasticizers for Metakaolin‐Portland Cement Blends with Hierarchical Machine Learning. In Advanced Theory and …, 2018.
  44. Pham, H; Liang, PP; Manzini, T et al. Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities. In arXiv preprint arXiv …, 2018.
  45. Hechtlinger, Y; Póczos, B and Wasserman, L Cautious Deep Learning. In arXiv preprint arXiv:1805.09460, 2018.
  46. Paria, B; Kandasamy, K and Póczos, B A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations. In static.aixpaper.com, 2018.
  47. Sodhi, P; Sun, H; Póczos, B et al. Robust Plant Phenotyping via Model-Based Optimization. In 2018 IEEE/RSJ …, 2018.
  48. Singh, S; Yang, Y; Zhang, R et al. Predicting enhancer-promoter interaction using genomic sequence features. In contrib.andrew.cmu.edu, 2018.
  49. Singh, S; Uppal, A; Li, B et al. Nonparametric Density Estimation with Adversarial Losses. In papers.nips.cc, 2018.
  50. Wu, Y; Poczos, B and Singh, A Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent. In arXiv preprint arXiv:1802.04420, 2018.
  51. Wu, Y; Poczos, B and Singh, A Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent. In arXiv preprint arXiv:1802.04420, 2018.
  52. Singh, S; Póczos, B and Ma, J Minimax Reconstruction in (Convolutional) Sparse Dictionary Learning. In contrib.andrew.cmu.edu, 2018.
  53. Li, CL; Kang, E; Ge, S et al. Hallucinating Point Cloud into 3D Sculptural Object. In arXiv preprint arXiv …, 2018.
  54. Ntampaka, M; Trac, H; Sutherland, D et al. Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Support Distribution Machines. In American …, 2018.

2017

  1. De-Arteaga, M. and Dubrawski, A. Discovery of complex anomalous patterns of sexual violence in El Salvador. In arXiv preprint arXiv:1711.06538, 2017.
  2. Kandasamy, K.; Schneider, J. and P'oczos, B. Query efficient posterior estimation in scientific experiments via Bayesian active learning. In Artificial Intelligence, 243: 45-56, 2017.
  3. Chen, L; Ogundele, O; Clermont, G et al. Dynamic and personalized risk forecast in step-down units. Implications for monitoring paradigms. In Annals of the …, 2017.
  4. Guillame-Bert, M and Dubrawski, A Classification of time sequences using graphs of temporal constraints. In The Journal of Machine Learning …, 2017.
  5. Xu, Y.; Zhang, H.; Singh, A. et al. Noise-Tolerant Interactive Learning from Pairwise Comparisons. In arXiv preprint arXiv …, 2017.
  6. Xu, Y.; Zhang, H.; Miller, K. et al. Noise-tolerant interactive learning using pairwise comparisons. In Advances in Neural Information Processing Systems, 2017.
  7. Barnes, M and Dubrawski, A The Binomial Block Bootstrap Estimator for Evaluating Loss on Dependent Clusters. In UAI, 2017.
  8. Bose, E; Chen, L; Clermont, G et al. Risk for cardiorespiratory instability following transfer to a monitored step-down unit. In Respiratory …, 2017.
  9. Liu, C; Narasimhan, SG and .., Matting and Depth Recovery of Thin Structures using a Focal Stack. In Proceedings of the IEEE …, 2017.
  10. Rabbany, R; Eswaran, D; Dubrawski, AW et al. Beyond Assortativity: Proclivity Index for Attributed Networks (ProNe). In Pacific-Asia Conference …, 2017.
  11. Lei, E; Miller, K and Dubrawski, A Learning Mixtures of Multi-Output Regression Models by Correlation Clustering for Multi-View Data. In arXiv preprint arXiv:1709.05602, 2017.
  12. Chen, L and Dubrawski, A Learning from learning curves: discovering interpretable learning trajectories. In … of the Seventh International Learning Analytics & …, 2017.
  13. Nagpal, C.; Miller, K.; Pellathy, T. et al. Semi-Supervised Prediction of Comorbid Rare Conditions Using Medical Claims Data. In 2017 IEEE International Conference on Data Mining Workshops (ICDMW), 2017.
  14. De-Arteaga, M and Dubrawski, A Discovery of complex anomalous patterns of sexual violence in El Salvador. In arXiv preprint arXiv:1711.06538, 2017.
  15. Yoon, J; Mu, L; Chen, L et al. C102 CRITICAL CARE: PREDICTING AND IDENTIFYING ARDS DEVELOPMENT, SEPSIS AND CLINICAL DETERIORATION: Data-Driven Prediction And Risk Score …. In American Journal of …, 2017.
  16. Mu, L; Chen, L; Dubrawski, AW et al. Data-Driven Prediction And Risk Score Model For Tachycardia Event In The Intensive Care Unit. In Am J Respir Crit Care …, 2017.
  17. Chen, J and Dubrawski, A Identification of Sufferers of Rare Diseases Using Medical Claims Data. In Online journal of public health informatics, 2017.
  18. Venkatesan, S; Miller, JK; Schneider, J et al. Scaling active search using linear similarity functions. In arXiv preprint arXiv:1705.00334, 2017.
  19. Bourne, D; Dubrawski, A and Mason, MT Data-Driven Classification of Screwdriving Operations. In … International Symposium on …, 2017.
  20. Wang, X.; Chen, L.; Hravnak, M. et al. Utility of Anti-hypertension Prescription Orders in Predicting Future Hypertensive Instability Events. In AMIA, 2017.
  21. Dubrawski, A Urządzenia do sterowania i interakcji z użytkownikiem w inteligentnym budynku. In Elektro Info, 2017.
  22. Kandasamy, K.; Krishnamurthy, A.; Schneider, J. et al. Asynchronous parallel Bayesian optimisation via Thompson sampling. In arXiv preprint arXiv:1705.09236, 2017.
  23. Garnett, R.; Ho, S.; Bird, S. et al. Detecting damped Ly α absorbers with Gaussian processes. In Monthly Notices of the Royal Astronomical Society, 472 (2): 1850-1865, 2017. doi 
  24. Kandasamy, K.; Schneider, J. and P'oczos, B. Query efficient posterior estimation in scientific experiments via Bayesian active learning. In Artificial Intelligence, 243: 45-56, 2017.
  25. Zaheer, M; Kottur, S; Ravanbakhsh, S et al. Deep sets. In Advances in neural …, 2017.
  26. Li, CL; Chang, WC; Cheng, Y et al. Mmd gan: Towards deeper understanding of moment matching network. In Advances in Neural …, 2017.
  27. Chang, JH R.; Li, CL; Poczos, B et al. One Network to Solve Them All--Solving Linear Inverse Problems Using Deep Projection Models. In Proceedings of the …, 2017.
  28. Du, SS; Lee, JD; Tian, Y et al. Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima. In arXiv preprint arXiv …, 2017.
  29. Du, SS; Jin, C; Lee, JD et al. Gradient descent can take exponential time to escape saddle points. In Advances in neural …, 2017.
  30. Lanusse, F; Ma, Q; Li, N et al. CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding. In Monthly Notices of …, 2017.
  31. Reddi, SJ; Zaheer, M; Sra, S et al. A generic approach for escaping saddle points. In arXiv preprint arXiv …, 2017.
  32. Ravanbakhsh, S; Lanusse, F; Mandelbaum, R et al. Enabling dark energy science with deep generative models of galaxy images. In Thirty-First AAAI …, 2017.
  33. Kandasamy, K; Dasarathy, G; Schneider, J et al. Multi-fidelity bayesian optimisation with continuous approximations. In Proceedings of the 34th …, 2017.
  34. Ravanbakhsh, S; Schneider, J and Poczos, B Equivariance through parameter-sharing. In Proceedings of the 34th …, 2017.
  35. Oliva, JB; Póczos, B and Schneider, J The statistical recurrent unit. In Proceedings of the 34th International …, 2017.
  36. Chang, WC; Li, CL; Yang, Y et al. Data-driven random fourier features using stein effect. In arXiv preprint arXiv:1705.08525, 2017.
  37. Du, SS; Koushik, J; Singh, A et al. Hypothesis transfer learning via transformation functions. In Advances in Neural …, 2017.
  38. Singh, S and Póczos, B Nonparanormal information estimation. In Proceedings of the 34th International Conference on …, 2017.
  39. Kandasamy, K; Krishnamurthy, A; Schneider, J et al. Asynchronous parallel Bayesian optimisation via Thompson sampling. In arXiv preprint arXiv …, 2017.
  40. Xie, P; Poczos, B and Xing, EP Near-Orthogonality Regularization in Kernel Methods. In UAI, 2017.
  41. Fu, X; Huang, K; Stretcu, O et al. BrainZoom: High Resolution Reconstruction from Multi-modal Brain Signals. In Proceedings of the 2017 …, 2017.
  42. Kandasamy, K; Schneider, J and Póczos, B Query efficient posterior estimation in scientific experiments via Bayesian active learning. In Artificial Intelligence, 2017.
  43. Menon, A; Gupta, C; Perkins, KM et al. Elucidating multi-physics interactions in suspensions for the design of polymeric dispersants: a hierarchical machine learning approach. In … Systems Design & …, 2017.
  44. Zaheer, M; Kottur, S; Ravanbakhsh, S et al. Deep Sets. 2017. In URL http://arxiv. org/abs …, 2017.
  45. Singh, S; Póczos, B and Ma, J On the reconstruction risk of convolutional sparse dictionary learning. In arXiv preprint arXiv:1708.08587, 2017.
  46. Oliva, JB; Dubey, KA; Poczos, B et al. Recurrent Estimation of Distributions. In arXiv preprint arXiv …, 2017.
  47. Martin, D; Póczos, B and Hollifield, B Machine learning-aided modeling of fixed income instruments. In ml.cmu.edu, 2017.
  48. Zaheer, M; Li, CL; Póczos, B et al. GAN Connoisseur: Can GANs Learn Simple 1D Parametric Distributions?. In cs.cmu.edu, 2017.
  49. Schneider, J; Poczos, B; Xiong, L et al. Machine Learning to Recognize Phenomena in Large Scale Simulations. In csm.ornl.gov, 2017.
  50. Singh, S; Du, SS and Póczos, B Efficient Nonparametric Smoothness Estimation: Supplementary Information. In papers.nips.cc, 2017.
  51. Narasimhan, S; Cochran, JJ and Póczos, B PRINCIPAL COMPONENT ANALYSIS. In kocw.xcache.kinxcdn.com, 2017.
  52. Póczos, B; Kottur, S; Salakhutdinov, R et al. Deep Sets. In cs.ubc.ca, 2017.
  53. Mauter, SR and Póczos, B Assessing the Feasibility of Remote Sensing of Soil Salinity at Global Scale. In … Data for High …, 2017.
  54. Lanusse, F; Ravanbakhsh, S and .., Deep Generative Models of Galaxy Images for the Calibration of the Next Generation of Weak Lensing Surveys. In American …, 2017.

2016

  1. De Arteaga, M. Canonical Autocorrelation Analysis for Radiation Threat Detection. www 
  2. Yeh, F-C.; Vettel, J. M; Singh, A. et al. Quantifying differences and similarities in whole-brain white matter architecture using local connectome fingerprints. In PLoS computational biology, 12 (11): e1005203, 2016.
  3. Szab'o, Z.; Sriperumbudur, B. K; P'oczos, B. et al. Learning theory for distribution regression. In The Journal of Machine Learning Research, 17 (1): 5272-5311, 2016.
  4. Reddi, S. J; Sra, S.; P'oczos, B. et al. Stochastic frank-wolfe methods for nonconvex optimization. In arXiv preprint arXiv:1607.08254, 2016.
  5. Ntampaka, M.; Trac, Hy; Sutherland, D. J et al. Dynamical mass measurements of contaminated galaxy clusters using machine learning. In The Astrophysical Journal, 831 (2): 135, 2016.
  6. Chen, L; Dubrawski, A; Wang, D et al. Using supervised machine learning to classify real alerts and artifact in online multi-signal vital sign monitoring data. In Critical Care Medicine, 2016.
  7. Hravnak, M; Chen, L; Dubrawski, A et al. Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data. In Journal of clinical …, 2016.
  8. Tandon, P; Huggins, P; Maclachlan, R et al. Detection of radioactive sources in urban scenes using Bayesian Aggregation of data from mobile spectrometers. In Information Systems, 2016.
  9. Aronson, R. M; Bhatia, A.; Jia, Z. et al. Data-driven classification of screwdriving operations. In International Symposium …, 2016.
  10. Miller, K; Kennedy, E and Dubrawski, A Do public events affect sex trafficking activity?. In arXiv preprint arXiv:1602.05048, 2016.
  11. Miller, K.; Huggins, P.; Labov, S. et al. Evaluation of coded aperture radiation detectors using a Bayesian approach. In Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2016.
  12. Chen, L; Li, X; Xia, Z et al. Riding an emotional roller-coaster: A multimodal study of young child's math problem solving activities. In EDM, 2016.
  13. Guillame-Bert, M; Dubrawski, A; Wang, D et al. Learning temporal rules to forecast instability in continuously monitored patients. In Journal of the …, 2016.
  14. Guillame-Bert, M and Dubrawski, A Batched Lazy Decision Trees. In arXiv preprint arXiv:1603.02578, 2016.
  15. Chen, L; Clermont, G; Hravnak, M et al. 208: Personalized Cardiorespiratory Instability Risk Evolution Among Continuously Monitored Patients. In Critical Care Medicine, 2016.
  16. Tandon, P; Huggins, P; Dubrawski, A et al. Suppressing Background Radiation Using Poisson Principal Component Analysis. In arXiv preprint arXiv …, 2016.
  17. Hravnak, M; Chen, L; Dubrawski, A et al. 62: Machine Learning Can Pre-identify Instability Risk For A Medical Emergency Team Call. In Critical Care …, 2016.
  18. Barnes, M and Dubrawski, A Clustering on the Edge: Learning Structure in Graphs. In arXiv preprint arXiv:1605.01779, 2016.
  19. Wang, D; Fiterau, M and Dubrawski, A VIPR: An Interactive Tool for Meaningful Visualization of High-Dimensional Data. In IJCAI, 2016.
  20. Dubrawski, A Extracting Useful Information from Multivariate Temporal Data. In Big Data and Business Analytics, 2016.
  21. Chen, L.; Clermont, G.; Hravnak, M. et al. A Framework for Visual Tracking of Risk and its Drivers in Monitoring Patients Susceptible for Cardiorespiratory Instability. In AMIA, 2016.
  22. Dubrawski, A Zalety i wady instalacji inteligentnych. In Elektro Info, 2016.
  23. Ntampaka, M.; Trac, Hy; Sutherland, D. J et al. Dynamical mass measurements of contaminated galaxy clusters using machine learning. In The Astrophysical Journal, 831 (2): 135, 2016.
  24. Reddi, SJ; Hefny, A; Sra, S et al. Stochastic variance reduction for nonconvex optimization. In International conference on …, 2016.
  25. Reddi, SJ; Sra, S; Poczos, B et al. Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization. In Advances in Neural …, 2016.
  26. Ravanbakhsh, S; Schneider, J and Poczos, B Deep learning with sets and point clouds. In arXiv preprint arXiv:1611.04500, 2016.
  27. Szabó, Z; Sriperumbudur, BK; Póczos, B et al. Learning theory for distribution regression. In The Journal of Machine …, 2016.
  28. Yeh, FC; Vettel, JM; Singh, A et al. Quantifying differences and similarities in whole-brain white matter architecture using local connectome fingerprints. In PLoS computational …, 2016.
  29. Reddi, SJ; Sra, S; Póczos, B et al. Stochastic frank-wolfe methods for nonconvex optimization. In 2016 54th Annual Allerton …, 2016.
  30. Kandasamy, K; Dasarathy, G; Oliva, JB et al. Gaussian process bandit optimisation with multi-fidelity evaluations. In Advances in Neural …, 2016.
  31. Oliva, JB; Dubey, A; Wilson, AG et al. Bayesian nonparametric kernel-learning. In Artificial Intelligence and …, 2016.
  32. Reddi, SJ; Konečný, J; Richtárik, P et al. Aide: Fast and communication efficient distributed optimization. In arXiv preprint arXiv …, 2016.
  33. Ntampaka, M; Trac, H; Sutherland, DJ et al. Dynamical mass measurements of contaminated galaxy clusters using machine learning. In The Astrophysical …, 2016.
  34. Dubey, KA; Reddi, SJ; Williamson, SA et al. Variance reduction in stochastic gradient Langevin dynamics. In Advances in neural …, 2016.
  35. Li, CL; Kandasamy, K; Póczos, B et al. High dimensional bayesian optimization via restricted projection pursuit models. In Artificial Intelligence and …, 2016.
  36. Ravanbakhsh, S; Oliva, JB; Fromenteau, S et al. Estimating Cosmological Parameters from the Dark Matter Distribution. In ICML, 2016.
  37. Singh, S and Póczos, B Finite-sample analysis of fixed-k nearest neighbor density functional estimators. In Advances in neural information processing …, 2016.
  38. Singh, S; Yang, Y; Poczos, B et al. Predicting enhancer-promoter interaction from genomic sequence with deep neural networks. In bioRxiv, 2016.
  39. Singh, S and Póczos, B Analysis of k-nearest neighbor distances with application to entropy estimation. In arXiv preprint arXiv:1603.08578, 2016.
  40. Ravanbakhsh, S; Póczos, B and Greiner, R Boolean Matrix Factorization and Noisy Completion via Message Passing. In ICML, 2016.
  41. Reddi, SJ; Sra, S; Póczos, B et al. Fast incremental method for smooth nonconvex optimization. In 2016 IEEE 55th …, 2016.
  42. Reddi, SJ; Sra, S; Póczos, B et al. Fast incremental method for nonconvex optimization. In arXiv preprint arXiv:1603.06159, 2016.
  43. Kandasamy, K; Dasarathy, G; Oliva, JB et al. Multi-fidelity gaussian process bandit optimisation. In arXiv preprint arXiv …, 2016.
  44. Sutherland, DJ; Oliva, JB; Póczos, B et al. Linear-time learning on distributions with approximate kernel embeddings. In Thirtieth AAAI Conference …, 2016.
  45. Kandasamy, K; Dasarathy, G; Poczos, B et al. The multi-fidelity multi-armed bandit. In Advances in Neural …, 2016.
  46. Ravanbakhsh, S; Póczos, B; Schneider, J et al. Stochastic neural networks with monotonic activation functions. In Artificial Intelligence and …, 2016.
  47. Wang, X; Oliva, JB; Schneider, JG et al. Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems. In IJCAI, 2016.
  48. Li, CL and Póczos, B Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods. In UAI, 2016.
  49. Tallavajhula, A; Póczos, B and Kelly, A Nonparametric distribution regression applied to sensor modeling. In 2016 IEEE/RSJ International …, 2016.
  50. Singh, S; Du, SS and Póczos, B Efficient nonparametric smoothness estimation. In Advances in Neural Information …, 2016.
  51. Szabó, Z; Sriperumbudur, B and .., Minimax-optimal distribution regression. In International Society …, 2016.
  52. Yeh, FC; Vettel, JM; Singh, A et al. Local connectome fingerprinting reveals the uniqueness of individual white matter architecture. In bioRxiv, 2016.
  53. Szábo, Z; Sriperumbudur, BK; Póczos, B et al. Leraning theory for distribution regression. JMLR, 17 (152): 1–40, 2016. In arXiv preprint arXiv:1411.2066, 2016.
  54. Li, CL; Ravanbakhsh, S and Poczos, B Annealing Gaussian into ReLU: a new sampling strategy for leaky-ReLU RBM. In arXiv preprint arXiv:1611.03879, 2016.
  55. Du, SS; Koushik, J; Singh, A et al. Transformation Function Based Methods for Model Shift. In CoRR, 2016.
  56. Du, SS; Koushik, J; Singh, A et al. Rates of Convergence of Nonparametric Estimators for Model Shift. In cs.cmu.edu, 2016.
  57. Du, SS; Koushik, J; Singh, A et al. Rates of Convergence of Nonparametric Estimators for Model Shift. In cs.cmu.edu, 2016.
  58. Singh, S; Yang, Y; Póczos, B et al. Predicting Enhancer-Promoter Interaction from Genomic Sequence with Deep Learning. In andrew.cmu.edu, 2016.

2015

  1. Ntampaka, M.; Trac, Hy; Sutherland, D. J et al. A machine learning approach for dynamical mass measurements of galaxy clusters. In The Astrophysical Journal, 803 (2): 50, 2015.
  2. Herlands, W.; De-Arteaga, M.; Neill, D. et al. Lass-0: sparse non-convex regression by local search. In arXiv preprint arXiv:1511.04402, 2015.
  3. Jung, I-S.; Berges, M.; Garrett, J. H et al. Exploration and evaluation of AR, MPCA and KL anomaly detection techniques to embankment dam piezometer data. In Advanced Engineering Informatics, 29 (4): 902-917, 2015.
  4. Ntampaka, M.; Trac, Hy; Sutherland, D. J et al. A machine learning approach for dynamical mass measurements of galaxy clusters. In The Astrophysical Journal, 803 (2): 50, 2015.
  5. Dubrawski, A; Miller, K; Barnes, M et al. Leveraging publicly available data to discern patterns of human-trafficking activity. In Journal of Human Trafficking, 1: 65-85, 2015.
  6. Nagpal, C; Miller, K; Boecking, B et al. An entity resolution approach to isolate instances of human trafficking online. In arXiv preprint arXiv …, 2015.
  7. Hravnak, M; Chen, L; Dubrawski, A et al. Temporal distribution of instability events in continuously monitored step-down unit patients: implications for rapid response systems. In Resuscitation, 2015.
  8. Liu, C.; Gomez, H.; Narasimhan, S. et al. Real-time visual analysis of microvascular blood flow for critical care. In Proceedings of the …, 2015.
  9. Chen, L; Dubrawski, A; Clermont, G et al. Modelling risk of cardio-respiratory Instability as a heterogeneous process. In AMIA Annual …, 2015.
  10. Gisolfi, N; Fiterau, M and Dubrawski, A Finding meaningful gaps to guide data acquisition for a radiation adjudication system. In Twenty-Ninth AAAI Conference on …, 2015.
  11. Fiterau, M and Dubrawski, A Active learning for informative projection retrieval. In Twenty-Ninth AAAI Conference on Artificial …, 2015.
  12. Herlands, W.; De-Arteaga, M.; Neill, D. et al. Lass-0: sparse non-convex regression by local search. In arXiv preprint arXiv:1511.04402, 2015.
  13. Barnes, M; Miller, K and Dubrawski, A Performance Bounds for Pairwise Entity Resolution. In arXiv preprint arXiv:1509.03302, 2015.
  14. Fiterau, M; Dubrawski, A; Wang, D et al. Semi automated adjudication of vital sign alerts in step-down units. In Intensive care medicine …, 2015.
  15. Hravnak, M; Chen, L; Dubrawski, A et al. Machine learning can classify vital sign alerts as real or artifact in online continuous monitoring data. In Intensive care medicine experimental, 2015.
  16. Guillame-Bert, M; Dubrawski, A and .., Forecasting escalation of cardio-respiratory instability using noninvasive vital sign monitoring data. In Intensive care …, 2015.
  17. Zambrano, S R.; Guillame-Bert, M; Dubrawski, A et al. Detection of hemorrhage by analyzing shapes of the arterial blood pressure waveforms. In Intensive care medicine experimental, 2015.
  18. Hravnak, M; Chen, L; Dubrawski, A et al. alert subtypes in monitored step-down unit patients have low entropy. Critical Care. In Medicine, 2015.
  19. De-Arteaga, M; Dubrawski, A and Huggins, P Canonical Autocorrelation Analysis. In arXiv preprint arXiv:1511.06419, 2015.
  20. Fiterau, M; Wang, D; Dubrawski, A et al. 10: USING EXPERT REVIEW TO CALIBRATE SEMI-AUTOMATED ADJUDICATION OF VITAL SIGN ALERTS IN STEP-DOWN UNITS. In Critical care …, 2015.
  21. Barnes, M; Gisolfi, N; Fiterau, M et al. Leveraging common structure to improve prediction across related datasets. In Twenty-Ninth AAAI …, 2015.
  22. Bose, E; Clermont, G; Chen, L et al. 150: CHARACTERIZING INITIAL CARDIORESPIRATORY INSTABILITY PATTERNS IN MONITORED STEP-DOWN UNIT PATIENTS. In Critical care …, 2015.
  23. Chen, L; Dubrawski, A; Hravnak, M et al. 823: PREDICTING RISK PROGRESSION TRAJECTORY FOR CARDIORESPIRATORY INSTABILITY IN MONITORED PATIENTS. In Critical care …, 2015.
  24. Hravnak, M; Chen, L; Dubrawski, A et al. 88: CARDIORESPIRATORY INSTABILITY ALERT SUBTYPES IN MONITORED STEP-DOWN UNIT PATIENTS HAVE LOW ENTROPY. In Critical care …, 2015.
  25. Dubrawski, A Kryteria wyboru sterowania w inteligentnym budynku. In Elektro Info, 2015.
  26. Dubrawski, A Ewolucja magistrali komunikacji danych w pojazdach kolejowych. In TTS Technika Transportu Szynowego, 2015.
  27. Ntampaka, M.; Trac, Hy; Sutherland, D. J et al. A machine learning approach for dynamical mass measurements of galaxy clusters. In The Astrophysical Journal, 803 (2): 50, 2015.
  28. Boecking, B.; Hall, M. and Schneider, J. Event prediction with learning algorithms—A study of events surrounding the egyptian revolution of 2011 on the basis of micro blog data. In Policy & Internet, 7 (2): 159-184, 2015.
  29. Sutherland, D. J and Schneider, J. On the error of random fourier features. In arXiv preprint arXiv:1506.02785, 2015.
  30. Reddi, SJ; Hefny, A; Sra, S et al. On variance reduction in stochastic gradient descent and its asynchronous variants. In Advances in Neural …, 2015.
  31. Kandasamy, K; Schneider, J and Póczos, B High dimensional Bayesian optimisation and bandits via additive models. In International Conference on …, 2015.
  32. Ramdas, A; Reddi, SJ; Póczos, B et al. On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions. In Twenty-Ninth AAAI …, 2015.
  33. Szabó, Z; Gretton, A; Póczos, B et al. Two-stage sampled learning theory on distributions. In Artificial Intelligence and …, 2015.
  34. Kandasamy, K; Krishnamurthy, A; Poczos, B et al. Nonparametric von mises estimators for entropies, divergences and mutual informations. In Advances in Neural …, 2015.
  35. Ntampaka, M; Trac, H; Sutherland, DJ et al. A machine learning approach for dynamical mass measurements of galaxy clusters. In The Astrophysical …, 2015.
  36. Reddi, S; Ramdas, A; Póczos, B et al. On the high dimensional power of a linear-time two sample test under mean-shift alternatives. In Artificial Intelligence and …, 2015.
  37. Kandasamy, K; Schneider, J and Póczos, B Bayesian active learning for posterior estimation. In Twenty-Fourth International Joint …, 2015.
  38. Reddi, SJ; Póczos, B and Smola, AJ Communication Efficient Coresets for Empirical Loss Minimization. In UAI, 2015.
  39. Krishnamurthy, A; Kandasamy, K; Poczos, B et al. On Estimating L22 Divergence. In AISTATS, 2015.
  40. Reddi, SJ; Poczos, B and Smola, A Doubly robust covariate shift correction. In Twenty-Ninth AAAI Conference on Artificial …, 2015.
  41. Oliva, J; Neiswanger, W; Póczos, B et al. Fast function to function regression. In Artificial Intelligence and …, 2015.
  42. Ramdas, A; Reddi, SJ; Poczos, B et al. Adaptivity and computation-statistics tradeoffs for kernel and distance based high dimensional two sample testing. In arXiv preprint arXiv …, 2015.
  43. Jung, IS; Berges, M; Jr, JH G. et al. Exploration and evaluation of AR, MPCA and KL anomaly detection techniques to embankment dam piezometer data. In Advanced Engineering …, 2015.
  44. Oliva, JB; Sutherland, DJ; Póczos, B et al. Deep mean maps. In arXiv preprint arXiv …, 2015.
  45. Szabó, Z; Gretton, A; Póczos, B et al. Two-Stage Sampled Distribution Regression on Separable Topological Domains. In gatsby.ucl.ac.uk, 2015.
  46. Poczos, B; Schneider, J; Brandenburg, A et al. CDS&E: Collaborative Research: Machine Learning for Automated Discovery and Control in Turbulent Plasma. In lcd-www.colorado.edu, 2015.
  47. Ramdas, A; Póczos, B; Singh, A et al. An Analysis of Active Learning With Uniform Feature Noise. In arXiv preprint arXiv …, 2015.
  48. Poczos, B; Schneider, J; Brandenburg, A et al. CDS&E: Collaborative Research: Machine Learning for Automated Discovery and Control in Turbulent Plasma. In lcd-www.colorado.edu, 2015.
  49. Ramdas, A; Póczos, B; Singh, A et al. An Analysis of Active Learning With Uniform Feature Noise. In arXiv preprint arXiv …, 2015.

2014

  1. Ramdas, A.; Reddi, S. J; Poczos, B. et al. On the high-dimensional power of linear-time kernel two-sample testing under mean-difference alternatives. In arXiv preprint arXiv:1411.6314, 2014.
  2. Pinsky, MR and Dubrawski, A Gleaning knowledge from data in the intensive care unit. In American Journal of Respiratory and Critical Care Medicine, 2014.
  3. Guillame-Bert, M and Dubrawski, A Learning temporal rules to forecast events in multivariate time sequences. In 2nd Workshop on Machine Learning for Clinical Data …, 2014.
  4. Wang, D; Fiterau, M; Dubrawski, A et al. 797: INTERPRETABLE ACTIVE LEARNING IN SUPPORT OF CLINICAL DATA ANNOTATION. In Critical Care …, 2014.
  5. Guillame-Bert, M; Dubrawski, A and .., Utility of Empirical Models of Hemorrhage in Detecting and Quantifying Bleeding. In Intensive Care …, 2014.
  6. Chen, L; Dubrawski, A; Hravnak, M et al. 41: FORECASTING CARDIO-RESPIRATORY INSTABILITY IN MONITORED PATIENTS A MACHINE LEARNING APPROACH. In Critical Care …, 2014.
  7. Wang, D; Chen, L; Fiterau, M et al. Multi-tier ground truth elicitation framework with application to artifact classification for predicting patient instability. In J Intensive Care …, 2014.
  8. Fiterau, M; Dubrawski, A; Hravnak, M et al. 51: ARCHETYPING ARTIFACTS IN MONITORED NONINVASIVE VITAL SIGNS DATA. In Critical Care …, 2014.
  9. Chen, L; Fiterau, M; Dubrawski, A et al. Active machine learning to increase annotation efficiency in classifying vital sign events as artifact or real alerts in continuous noninvasive monitoring. In Am J Respir Crit Care …, 2014.
  10. Hravnak, M; Chen, L; Dubrawski, A et al. Supervised Machine Learning Can Classify Artifact In Multi-Signal Vital Sign Monitoring Data Erom Step-Down Unit (SDU) Patients. In Intensive Care Medicine, 2014.
  11. Gisolfi, N; Fiterau, M; Dubrawski, A et al. Finding gaps in data to guide development of a radiation threat adjudication system. In Symposium on …, 2014.
  12. Fiterau, M; Dubrawski, A; Chen, L et al. Artifact adjudication for vital sign step-‐down unit data can be improved using active learning with low-‐dimensional models. In Entropy, 2014.
  13. Hravnak, M; Chen, L; Fiterau, M et al. B94 PREDICTIONS AND OUTCOMES IN THE ICU: Active Machine Learning To Increase Annotation Efficiency In Classifying Vital Sign Events As Artifact Or Real Alerts In Continuous Noninvasive Monitoring. In American Journal of Respiratory and Critical Care Medicine, 2014.
  14. Dubrawski, A; Chen, L; Liu, F et al. PRE-OPERATIVE PROGNOSIS OF PREDISPOSITION FOR BLEEDING-INDUCED CRASH. In INTENSIVE …, 2014.
  15. Lucia, L; Dubrawski, A and Chen, L Utility of Potential Misdiagnoses in Predicting Foodborne Outbreaks. In Online journal of public health …, 2014.
  16. Wang, Y; Dubrawski, A; Chen, L et al. Patterns of Emergency Care Utilization by Chronically Ill. In Online journal of public …, 2014.
  17. Hravnak, M; Chen, L; Dubrawski, A et al. 42: RANDOM FOREST MODELS SEPARATE VITAL SIGN EVENTS AS REAL OR ARTIFACT IN CONTINUOUS MONITORING DATA. In Critical Care …, 2014.
  18. Dubrawski, A Mobilne sposoby sterowania w inteligentnym budynku. In Elektro Info, 2014.
  19. Oliva, J.; Neiswanger, W.; Poczos, B. et al. Fast Function to Function Regression. In arXiv preprint arXiv:1410.7414, 2014.
  20. Krishnamurthy, A; Kandasamy, K; Poczos, B et al. Nonparametric Estimation of Renyi Divergence and Friends. In ICML, 2014.
  21. Singh, S and Póczos, B Exponential concentration of a density functional estimator. In Advances in Neural Information Processing …, 2014.
  22. Singh, S and Póczos, B Generalized exponential concentration inequality for Rényi divergence estimation. In International Conference on Machine Learning, 2014.
  23. Oliva, J; Neiswanger, W; Póczos, B et al. Fast distribution to real regression. In Artificial Intelligence and …, 2014.
  24. Oliva, J; Póczos, B; Verstynen, T et al. Fusso: Functional shrinkage and selection operator. In Artificial Intelligence and …, 2014.
  25. Kandasamy, K; Krishnamurthy, A; Poczos, B et al. Influence functions for machine learning: Nonparametric estimators for entropies, divergences and mutual informations. In arXiv preprint arXiv …, 2014.
  26. Ramdas, A; Reddi, SJ; Poczos, B et al. On the high-dimensional power of linear-time kernel two-sample testing under mean-difference alternatives. In arXiv preprint arXiv …, 2014.
  27. Reddi, SJ; Ramdas, A; Póczos, B et al. Kernel MMD, the median heuristic and distance correlation in high dimensions. In stat, 2014.
  28. Ramdas, A; Poczos, B; Singh, A et al. An analysis of active learning with uniform feature noise. In Artificial Intelligence and …, 2014.
  29. Szabó, Z; Gretton, A; Póczos, B et al. Consistent, two-stage sampled distribution regression via mean embedding. In arXiv preprint arXiv …, 2014.
  30. Clute, M; Singh, A; Poczos, B et al. The Predictive Value of Functional Connectivity. In Annual Meeting of the …, 2014.
  31. Oliva, J; Neiswanger, W; Poczos, B et al. Fast Function to Function Regression. In arXiv preprint arXiv …, 2014.
  32. Poczos, B and Singh, A 10-701 Machine Learning: Assignment 2. In cs.cmu.edu, 2014.
  33. Poczos, B and Tibshirani, R Duality uses and correspondences. In stat.cmu.edu, 2014.

2013

  1. Fiterau, M; Dubrawski, A; Chen, L et al. Automatic identification of artifacts in monitoring critically ill patients. In Intensive care …, 2013.
  2. Fiterau, M and Dubrawski, A Informative projection recovery for classification, clustering and regression. In 2013 12th International Conference on …, 2013.
  3. Guillame-Bert, M; Dubrawski, A; Chen, L et al. Learning temporal rules to forecast instability in intensive care patients. In Intensive care …, 2013.
  4. Sabhnani, M; Dubrawski, A and .., Searching for Complex Patterns Using Disjunctive Anomaly Detection. In Online journal of public …, 2013.
  5. Tandon, P; Huggins, P; Dubrawski, A et al. Simultaneous detection of radioactive sources and inference of their properties. In IEEE Nuclear Science …, 2013.
  6. Hravnak, M; Chen, L; Bose, E et al. Artifact patterns in continuous noninvasive monitoring of patients. In Intensive care medicine, 2013.
  7. Hravnak, M.; Chen, L.; Bose, E. et al. 285: REAL ALERTS AND ARTIFACT IN CONTINUOUS NONINVASIVE VITAL SIGN MONITORING MONO-VS. MULTI-PROCESS. In Critical Care Medicine, 2013.
  8. Holder, AL; Guillame-Bert, M; Chen, K et al. Does advanced treatment of existing physiologic data allow for earlier detection of occult hemorrhage?. In Journal of Critical Care, 2013.
  9. Holder, A.; Guillame-Bert, M.; Chen, K. et al. Is there an information hierarchy among hemodynamic variables for early identification of occult hemorrhage?. In Journal of Critical Care, 2013.
  10. Dubrawski, A Zdalne i wygodne zarządzanie inteligentną instalacją. In Elektro Info, 2013.
  11. Xu, X.; Ho, S.; Trac, Hy et al. A first look at creating mock catalogs with machine learning techniques. In The Astrophysical Journal, 772 (2): 147, 2013.
  12. Oliva, J; Póczos, B and Schneider, J Distribution to distribution regression. In International Conference on Machine …, 2013.
  13. Sutherland, DJ; Póczos, B and Schneider, J Active learning and search on low-rank matrices. In Proceedings of the 19th ACM …, 2013.
  14. Xu, X; Ho, S; Trac, H et al. A first look at creating mock catalogs with machine learning techniques. In The Astrophysical …, 2013.
  15. Reddi, SJ and Póczos, B Scale invariant conditional dependence measures. In International Conference on Machine Learning, 2013.
  16. Xiong, L; Póczos, B and Schneider, J Efficient learning on point sets. In 2013 IEEE 13th International …, 2013.
  17. Poczos, B and Tibshirani, R Karush-Kuhn-Tucker conditions. In stat.cmu.edu, 2013.
  18. Oliva, JB; Póczos, B; Singh, A et al. Sparse Functional Regression. In cs.cmu.edu, 2013.
  19. Oliva, JB; Póczos, B; Singh, A et al. Sparse Functional Regression. In cs.cmu.edu, 2013.
  20. Poczos, B and Tibshirani, R Subgradient method. In stat.cmu.edu, 2013.
  21. M Dogar, M K. A T. and Srinivasa, S Object Search by Manipulation. In Autonomous Robots, 2013.

2012

  1. Sarkar, P. and Moore, A. A tractable approach to finding closest truncated-commute-time neighbors in large graphs. In arXiv preprint arXiv:1206.5259, 2012.
  2. Fiterau, M and Dubrawski, A Projection retrieval for classification. In Advances in Neural Information Processing …, 2012.
  3. Tandon, P; Huggins, P; Dubrawski, A et al. Source location via Bayesian aggregation of evidence with mobile sensor data. In IEEE Symposium on …, 2012.
  4. Dubrawski, A; Ray, S; Huggins, P et al. Diagnosing machine learning-based nuclear evaluation system. In Proceedings of the IEEE NSS, 2012.
  5. Ermakov, V; Dubrawski, A; Dohi, T et al. Mining sea turtle nests: An amplitude independent feature extraction method for GPR data. In 2012 14th …, 2012.
  6. Brunner, D.; Alexandrov, V.; Caldarone, B. et al. Behavior-Based Screening as an Approach to Polypharmacological Ligands. In Polypharmacology in Drug Discovery: 303-313, 2012.
  7. P'oczos, B.; Ghahramani, Z. and Schneider, J. Copula-based kernel dependency measures. In arXiv preprint arXiv:1206.4682, 2012.
  8. Garnett, R.; Krishnamurthy, Y.; Xiong, X. et al. Bayesian optimal active search and surveying. In arXiv preprint arXiv:1206.6406, 2012.
  9. Zhang, Yi and Schneider, J. Maximum margin output coding. In arXiv preprint arXiv:1206.6478, 2012.
  10. P'oczos, B.; Xiong, L. and Schneider, J. Nonparametric divergence estimation with applications to machine learning on distributions. In arXiv preprint arXiv:1202.3758, 2012.
  11. Póczos, B; Xiong, L and Schneider, J Nonparametric divergence estimation with applications to machine learning on distributions. In arXiv preprint arXiv:1202.3758, 2012.
  12. Póczos, B; Ghahramani, Z and Schneider, J Copula-based kernel dependency measures. In arXiv preprint arXiv:1206.4682, 2012.
  13. Póczos, B; Xiong, L; Sutherland, DJ et al. Nonparametric kernel estimators for image classification. In 2012 IEEE Conference …, 2012.
  14. Szabó, Z; Póczos, B and Lőrincz, A Separation theorem for independent subspace analysis and its consequences. In Pattern Recognition, 2012.
  15. Póczos, B and Schneider, JG Nonparametric Estimation of Conditional Information and Divergences. In AISTATS, 2012.
  16. Szabó, Z; Póczos, B and Lőrincz, A Collaborative filtering via group-structured dictionary learning. In … on Latent Variable Analysis and Signal …, 2012.
  17. Sutherland, DJ; Xiong, L; Póczos, B et al. Kernels on sample sets via nonparametric divergence estimates. In arXiv preprint arXiv …, 2012.
  18. Póczos, B; Xiong, L; Sutherland, D et al. Support distribution machines, 2012. In URL http://arxiv. org/abs/1202.0302, 2012.
  19. Poczos, A. Separation Theorem for Independent Subspace Analysis and its Consequences. In Pattern Recognition, 2012.
  20. Szabó, Z; Póczos, B and Lorincz, A Separation theorem for K-independent subspace analysis with sufficient conditions. In … , Budapest, 2006. http://arxiv. org/abs …, 2012.

2011

  1. Dubrawski, A Detection of events in multiple streams of surveillance data. In Infectious disease informatics and biosurveillance, 2011.
  2. Waidyanatha, N; Dubrawski, A; Ganesan, M et al. Affordable system for rapid detection and mitigation of emerging diseases. In International Journal of …, 2011.
  3. Lonkar, R; Dubrawski, A; Fiterau, M et al. Mining intensive care vitals for leading indicators of adverse health events. In Emerg Health Threats …, 2011.
  4. Dubrawski, A and Sondheimer, N Techniques for early warning of systematic failures of aerospace components. In 2011 Aerospace Conference, 2011.
  5. Chen, L; Dubrawski, A; Waidyanatha, N et al. Automated detection of data entry errors in a real time surveillance system. In … Health Threats J, 2011.
  6. Fiterau, M; Dubrawski, A and Ye, C Real-time adaptive monitoring of vital signs for clinical alarm preemption. In Emerging Health Threats Journal, 2011.
  7. Daniel, S. F; Connolly, A.; Schneider, J. et al. Classification of stellar spectra with local linear embedding. In The Astronomical Journal, 142 (6): 203, 2011.
  8. Póczos, B and Schneider, J On the estimation of alpha-divergences. In … of the Fourteenth International Conference on …, 2011.
  9. Xiong, L; Póczos, B; Schneider, J et al. Hierarchical probabilistic models for group anomaly detection. In Proceedings of the …, 2011.
  10. Szabó, Z; Póczos, B and Lőrincz, A Online group-structured dictionary learning. In CVPR 2011, 2011.
  11. Xiong, L; Póczos, B and Schneider, JG Group anomaly detection using flexible genre models. In Advances in neural information …, 2011.
  12. Póczos, B; Szabó, Z and Schneider, J Nonparametric divergence estimators for independent subspace analysis. In 2011 19th European Signal …, 2011.
  13. Szabó, Z and Póczos, B Nonparametric independent process analysis. In 2011 19th European Signal Processing …, 2011.
  14. Póczos, B; Xiong, L and Schneider, J Nonparametric divergence estimation for learning manifolds of distributions and group anomaly detection. In Proc.(Snowbird) Learn. Workshop, 2011.

2010

  1. Dubrawski, A and Zhang, X The role of data aggregation in public health and food safety surveillance'. In Biosurveillance: Methods and case …, 2010.
  2. Ramos, J; Siddiqi, S; Dubrawski, A et al. Automatic state discovery for unstructured audio scene classification. In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing: 2154-2157, 2010.
  3. Zhang, Y; Schneider, J and Dubrawski, A Learning compressible models. In Proceedings of the 2010 SIAM …, 2010.
  4. Waidyanatha, N.; Sampath, C.; Dubrawski, A. et al. T-Cube Web Interface as a tool for detecting disease outbreaks in real-time: A pilot in India and Sri Lanka. In 2010 IEEE RIVF …, 2010.
  5. Waidyanatha, N.; Prashant, S.; Ganesan, M et al. Real-time biosurveillance pilot in India and Sri Lanka. In The 12th IEEE International Conference on e-Health Networking, Applications and Services, 2010.
  6. Waidyanatha, N; Prashant, S; Ganesan, M et al. Real-Time Biosurveillance pilot in India and Sri Lanka. e-Health Networking Applications and Services (Healthcom). In 12th IEEE International …, 2010.
  7. Dubrawski, AW; Ostlund, JK; Chen, L et al. Computationally efficient scoring of activity using demographics and connectivity of entities. In Information Technology …, 2010.
  8. MacLachlan, RA and Dubrawski, A Applied Indoor Localization: Map-based, Infrastructure-free, with Divergence Mitigation and Smoothing. In Information Fusion (FUSION….(2012), 2010.
  9. Pál, D; Póczos, B and Szepesvári, C Estimation of Rényi entropy and mutual information based on generalized nearest-neighbor graphs. In Advances in Neural Information …, 2010.
  10. Póczos, B; Kirshner, S and Szepesvári, C REGO: Rank-based estimation of Rényi information using Euclidean graph optimization. In Proceedings of the Thirteenth …, 2010.
  11. Szabó, Z; Póczos, B and Lőrincz, A Auto-regressive independent process analysis without combinatorial efforts. In Pattern Analysis and Applications, 2010.
  12. Ravanbakhsh, SM; Póczos, B and Greiner, R A Cross-Entropy method that optimizes partially decomposable problems: a new way to interpret NMR spectra. In Twenty-Fourth AAAI Conference …, 2010.
  13. Ravanbakhsh, S; Poczos, B and Greiner, R A Cross-Entropy Method that Optimizes Partially Decomposable Problems. In cs.ubc.ca, 2010.

2009

  1. Zhang, Y; Dubrawski, A and Schneider, JG Learning the semantic correlation: An alternative way to gain from unlabeled text. In Advances in Neural …, 2009.
  2. Dubrawski, A; Sarkar, P and Chen, L Trade-offs between agility and reliability of predictions in dynamic social networks used to model risk of microbial contamination of food. In 2009 International Conference on Advances in Social Network Analysis and Mining, 2009.
  3. Dubrawski, A; Sabhnani, M; Knight, M et al. T-Cube web interface in support of real-time bio-surveillance program. In … on Information and …, 2009.
  4. Dubrawski, A; Chen, L; Sabhnani, M et al. Discovering Possible Linkages between Food-borne Illness and the Food Supply Using an Interactive Analysis Tool. In 8th Annual Conference of …, 2009.
  5. Waidyanatha, N; M, G.; Weerakoon, P et al. Real Time Biosurveillance Pilot in India and Sri Lanka: http://lirneasia. net/wp-content/uploads/2009/11. In Prashant and Waidyanatha, 2009.
  6. Dubrawski, A; Ganesan, M; Gow, G et al. Real-Time Biosurveillance Pilot in India and Sri Lanka, eAsia 2009, December 02–04, 2009. In Colombo, Sri Lanka, 2009.
  7. Dubrawski, AS M, and Waidyanatha, N.(2009). In T-Cube Web Interface for Real-Time Biosurveillance …, 2009.
  8. Dubrawski, A and Thorne, H Evolution of a Useful Autonomous System. In Robot Motion and Control 2009, 2009.
  9. Póczos, B and Lorincz, A Identification of recurrent neural networks by Bayesian interrogation techniques. In Journal of Machine Learning Research, 2009.
  10. Póczos, B; Abbasi-Yadkori, Y; Szepesvári, C et al. Learning when to stop thinking and do something!. In Proceedings of the 26th …, 2009.
  11. Póczos, B Introduction to independent component analysis. In Lecture notes, university of Alberta, 2009.

2008

  1. Ray, S; Michalska, A; Sabhnani, M et al. T-Cube Web Interface: a tool for immediate visualization, interactive manipulation and analysis of large sets of multivariate time series. In AMIA Annual Symposium Proceedings, 2008.
  2. Sarkar, P; Chen, L and Dubrawski, A Dynamic network model for predicting occurrences of salmonella at food facilities. In International Workshop on …, 2008.
  3. Ray, S; Michalska, A; Sabhnani, M et al. J, and Ostlund (2008). T-Cube Web Interface: A Tool for Immediate Visualization, Interactive Manipulation and Analysis of Large Sets of Multivariate Time …. In AMIA Annual Symposium, 2008.
  4. Dubrawski, A; Sabhnani, M; Ray, S et al. Interactive manipulation, visualization analysis of large sets of multidimensional time series in health informatics. In INFORMS, 2008.
  5. Chen, L; Dubrawski, AW; Ray, S et al. Detecting linkages between human illness and Salmonella isolates in food using a new tool for spatio-temporal analysis of multi-stream data. In AMIA Annual …, 2008.
  6. Roure, J; Dubrawski, A and Schneider, J Learning detectors of events in multivariate time series. In AMIA Annual Symposium …, 2008.
  7. Dubrawski, A; Chen, L and Ostlund, J Using AFDL algorithm to estimate risk of positive outcomes of microbial tests at food establishments. In Advances in Disease …, 2008.
  8. Houghten, R. A.; Pinilla, C.; Giulianotti, M. A. et al. Strategies for the Use of Mixture-Based Synthetic Combinatorial Libraries: Scaffold Ranking, Direct Testing In Vivo, and Enhanced Deconvolution by Computational Methods. In Journal of Combinatorial Chemistry, 10 (1): 3-19, 2008. doi 
  9. Kirshner, S and Póczos, B ICA and ISA using Schweizer-Wolff measure of dependence. In Proceedings of the 25th international conference …, 2008.
  10. Poczos, B and Lorincz, A D-optimal Bayesian interrogation for parameter and noise identification of recurrent neural networks. In arXiv preprint arXiv:0801.1883, 2008.

2007

  1. Roure, J; Dubrawski, A and Schneider, J A study into detection of bio-events in multiple streams of surveillance data. In Intelligence and security informatics …, 2007.
  2. Dubrawski, A; Sabhnani, M; Ray, S et al. T-Cube as an enabling technology in surveillance applications. In Advances in Disease …, 2007.
  3. Sabhnani, M; Moore, A and Dubrawski, A Rapid processing of ad-hoc queries against large sets of time series. In Advances in Disease Surveillance, 2007.
  4. Siddiqi, SM; Boots, B; Gordon, GJ et al. Learning stable multivariate baseline models for outbreak detection. In Advances in Disease …, 2007.
  5. Dubrawski, A; Baysek, M; Mikus, MS et al. Applying outbreak detection algorithms to prognostics. In AAAI Fall Symposium on …, 2007.
  6. Sabhnani, M; Dubrawski, A and .., Multivariate time series analyses using primitive univariate algorithms. In Advances in Disease …, 2007.
  7. Mikus, S; Dubrawski, A; Sondheimer, N et al. Collective Machine Learning for Early Identification of Logistics Crises. In … Conference, Cincinnati, OH, 2007.
  8. Sabhnani, M; Dubrawski, A and .., T-Cube Web Interface for Real-time Biosurveillance in Sri Lanka. In Advances in Disease …, 2007.
  9. Roure, J; Dubrawski, A and .., Learning specific detectors of adverse events in multivariate time series. In Advances in Disease …, 2007.
  10. Bryan, B.; Schneider, J.; Miller, C. J. et al. Mapping the Cosmological Confidence Ball Surface. In The Astrophysical Journal, 665 (1): 25-41, 2007. doi 
  11. Szabó, Z; Póczos, B and Lőrincz, A Undercomplete blind subspace deconvolution. In Journal of Machine Learning Research, 2007.
  12. Póczos, B; Szabó, Z; Kiszlinger, M et al. Independent process analysis without a priori dimensional information. In International Conference on …, 2007.
  13. Szabó, Z; Póczos, B and Lőrincz, A Undercomplete blind subspace deconvolution via linear prediction. In European Conference on Machine …, 2007.
  14. Szabó, Z; Póczos, B; Szirtes, G et al. Post nonlinear independent subspace analysis. In International Conference on …, 2007.

2006

  1. Dubrawski, A; Elenberg, K; Moore, A et al. Monitoring food safety by detecting patterns in consumer complaints. In Proceedings of the …, 2006.
  2. Dubrawski, A Projektowanie instalacji EIB-uwagi praktyczne dla projektantów. Cz. 1. In Elektro Info, 2006.
  3. Szabó, Z; Póczos, B and Lőrincz, A Cross-entropy optimization for independent process analysis. In International Conference on Independent …, 2006.
  4. Póczos, B and Lőrincz, A Non-combinatorial estimation of independent autoregressive sources. In Neurocomputing, 2006.
  5. Szabó, Z; Póczos, B and Lorincz, A Separation theorem for K-independent subspace analysis with sufficient conditions. In arXiv preprint math/0608100, 2006.

2005

  1. Sabhnani, M; Neill, D; Moore, A et al. Efficient analytics for effective monitoring of biomedical security. In Proceedings of the …, 2005.
  2. Tengli, A; Dubrawski, A and Chen, L Learning Predictive Models from Small Sets of Dirty Data. In International Conference on …, 2005.
  3. Dubrawski, A Inteligentna instalacja elektryczna w obiektach użyteczności publicznej. In Elektro Info, 2005.
  4. Dubrawski, A Instalacja elektryczna w domu. In Elektro Info, 2005.
  5. Póczos, B and Lõrincz, A Independent subspace analysis using geodesic spanning trees. In Proceedings of the 22nd international conference …, 2005.
  6. Póczos, B and Lőrincz, A Independent Subspace Analysis Using k-Nearest Neighborhood Distances. In International Conference on Artificial Neural …, 2005.
  7. Póczos, B; Takács, B and Lőrincz, A Independent subspace analysis on innovations. In European Conference on Machine …, 2005.
  8. Szirtes, G; Póczos, B and Lőrincz, A Neural kalman filter. In Neurocomputing, 2005.

2004

  1. Dubrawski, A Gniazda w pomieszczeniach specjalnych. In Elektro Info, 2004.
  2. Dubrawski, A Inteligentne łączniki czasowe. In Elektro Info, 2004.
  3. Szatmáry, B; Póczos, B and Lőrincz, A Competitive spiking and indirect entropy minimization of rate code: Efficient search for hidden components. In Journal of Physiology-Paris, 2004.
  4. Szirtes, G; Póczos, B and Lorincz, A Neural Adaptation of the Kalman-Gain. In cs.cmu.edu, 2004.

2003

  1. Siemiatkowska, B and Dubrawski, A Navigation of a Mobile Robot. In Rough Sets and Current Trends …, 2003.
  2. Dubrawski, A Kompleksowe sterowanie żaluzjami. In Elektro Info, 2003.
  3. Poczos, B and Lorincz, A Kalman-filtering using local interactions. In arXiv preprint cs/0302039, 2003.
  4. Lörincz, A and Póczos, B Cost component analysis. In International journal of neural systems, 2003.

2002

  1. Dubrawski, A Zasady doboru gniazd wtykowych. In Elektro Info, 2002.
  2. Dubrawski, A Porównanie gniazd wtyczkowych. In Elektro Info, 2002.
  3. Szatmáry, B; Póczos, B; Eggert, J et al. Non-negative matrix factorization extended by sparse code shrinkage and weight sparsification. In ECAI, 2002.
  4. Lörincz, A; Póczos, B; Szirtes, G et al. Ockham's Razor at Work: Modeling of the``Homunculus''. In Brain and Mind, 2002.

2000

  1. Racz, J; Dubrawski, A and .., Computer-aided diagnosis of breast cancer based on analysis of microcalcifications. In Proceedings …, 2000.

1998

  1. Dubrawski, A and Siemiatkowska, I A method for tracking the pose of a mobile robot equipped with a scanning laser range finder. In Proceedings of the 1998 IEEE International Conference on Robotics & Automation, 1998.

1997

  1. Dubrawski, A Stochastic validation for automated tuning of neural network's hyper-parameters. In Robotics and autonomous systems, 1997.
  2. Dubrawski, A and Schneider, J Memory based stochastic optimization for validation and tuning of function approximators. In Conference on AI and Statistics, 1997.
  3. Dubrawski, A Tuning neural networks with stochastic optimization. In Proceedings of the 1997 IEEE/RSJ International …, 1997.
  4. Dubrawski, A and Siemiatkowska, B A Neural Method for Self-Localization of a Mobile Robot Equipped with a 2-D Scanning Laser Range Finder. In 5th International Symposium on Intelligent Robotic …, 1997.
  5. Dubrawski, A and Siemiątkowska, B A neural method for self-localization of a mobile robot equipped with 2D scanning range finder. In Proc. of International Workshop on Intelligent …, 1997.
  6. Dubrawski, AW and Moore, AW Scoring discriminative capabilities of data attributes using Bayesian classi er and Monte Carlo integration. In Proceedings of the ECML97 Workshop on Case-Based …, 1997.

1996

  1. Dubrawski, A Memory-based stochastic optimization for automated tuning of neural network's high level parameters. In 4th International Symposium on Intelligent …, 1996.
  2. Dubrawski, A and Sawwa, R Three-dimensional laser range finders for mobile robots' navigation. In PRACE NAUKOWE-INSTYTUTU CYBERNETYKI …, 1996.
  3. Borkowski, A; Dubrawski, A and Racz, J Neural networks of type art in navigation systems of mobile robots. In PRACE NAUKOWE-INSTYTUTU CYBERNETYKI …, 1996.

1995

  1. Kasperkiewicz, J; Racz, J and Dubrawski, A HPC strength prediction using artificial neural network. In Journal of Computing in Civil Engineering, 9, 1995.
  2. Racz, J and Dubrawski, A Artificial neural network for mobile robot topological localization. In Robotics and autonomous systems, 1995.
  3. Kasperkiewics, J; Racz, J and Dubrawski, A HPC strength prediction using ANN. ASCE J. Comp. In Civil Eng, 1995.
  4. Racz, J and Dubrawski, A Qualitative Pose Estimation Using An Artificial Neural Network. In ICAR95, 1995.
  5. Kazperkiewiecz, J; Raez, J and Dubrawski, A HPC Strength prediction using artificial neural networks. In ASCE Journal of Computing in Civil …, 1995.

1994

  1. Dubrawski, A and Crowley, JL Learning locomotion reflexes: A self-supervised neural system for a mobile robot. In Robotics and Autonomous Systems, 1994.
  2. Dubrawski, A and Reignier, P Learning to categorize perceptual space of a mobile robot using fuzzy-art neural network. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94), 1994.
  3. Dubrawski, A and Crowley, JL Self-supervised neural system for reactive navigation. In Proceedings of the 1994 IEEE International Conference on Robotics and Automation, 1994.
  4. Racz, J and Dubrawski, A Mobile Robot Localization with an Artificial Neural Network. In … Workshop on Intelligent Robotic Systems IRS, 1994.
  5. Kasperkiewicz, J; Racz, J and Dubrawski, A HPC strength prediction using artificial neural networks for development of diagnostic monitoring system in nuclear plants. In ASCE Journal of Computing in Civil …, 1994.

Books

2019

  1. Andrews, M; Usai, E; Bryant, P et al. arXiv: End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data. cds.cern.ch, 2019. www 

2018

  1. Barnes, M and Dubrawski, A Deep Spectral Clustering for Object Instance Segmentation. openreview.net, 2018. www 
  2. Dubrawski, A Machine Learning Algorithms for Statistical Patterns in Large Data Sets. apps.dtic.mil, 2018. www 
  3. Dubrawski, A Machine Learning for Adaptable Heterogeneous Indexing and Search. apps.dtic.mil, 2018. www 
  4. Singh, S and Póczos, B Estimating Probability Distributions and their Properties. contrib.andrew.cmu.edu, 2018. www 
  5. Pham, H; Liang, PP; Manzini, T et al. Learning Robust Joint Representations for Multimodal Sentiment Analysis. openreview.net, 2018. www 
  6. Andrews, MB; Gleyzer, S; Poczos, B et al. Exploring End-to-end Deep Learning Applications for Event Classification at CMS. cds.cern.ch, 2018. www 

2016

  1. Nagpal, C; Boecking, B; Miller, K et al. personaLink: A tool to Link Users across Online Forums. cs.cmu.edu, 2016. www 
  2. Singh, S; Póczos, B and Verstynen, T ADA Project: Measuring Functional Connectivity in fMRI data: Statistical Methods and Comparison to Structure. contrib.andrew.cmu.edu, 2016. www 

2015

  1. Krishnamurthy, A; Kandasamy, K; Poczós, B et al. On Estimating L2. cs.cmu.edu, 2015. www 
  2. Krishnamurthy, A; Kandasamy, K; Poczós, B et al. On Estimating L2. cs.cmu.edu, 2015. www 
  3. Szabó, Z; Sriperumbudur, B; Póczos, B et al. Distribution Regression-Make It Simple and Consistent. discovery.ucl.ac.uk, 2015. www 

2014

  1. Reddi, SJ and Póczos, B k-NN regression on functional data with incomplete observations. kilthub.figshare.com, 2014. www 
  2. Szabó, Z; Gretton, A; Póczos, B et al. Simple consistent distribution regression on compact metric domains. discovery.ucl.ac.uk, 2014. www 

2013

  1. Póczos, B; Rinaldo, A; Singh, A et al. Distribution-free distribution regression. jmlr.org, 2013. www 

2012

  1. Fiterau, M; Dubrawski, A; Schneider, J et al. Trade-offs in Explanatory Model Learning. cs.cmu.edu, 2012. www 
  2. Lux, MA; Ghallab, MM; Lukowicz, MP et al. Learning temporal association rules on Symbolic time sequences. pdfs.semanticscholar.org, 2012. www 
  3. Dubrawski, A The design of small suport-recreational yacht with a wooden compliment (lux edition). repo.pw.edu.pl, 2012. www 
  4. Fiterau, M and Dubrawski, A Explanation-Oriented Classification via Subspace Partitioning. cs.cmu.edu, 2012. www 
  5. Dubrawski, A Zasady komponowania ciągów uczących dla modeli nieszczelności kotłów fluidalnych. repo.pw.edu.pl, 2012. www 
  6. Póczos, B; Xiong, L; Sutherland, DJ et al. Support distribution machines. cs.cmu.edu, 2012. www 
  7. Poczos, B and Schneider, J Conditional distance variance and correlation. kilthub.figshare.com, 2012. www 
  8. Póczos, B; Schneider, J; Xiong, L et al. Machine Learning to Recognize Phenomena in Large Scale Simulations. cs.cmu.edu, 2012. www 
  9. Póczos, B; Schneider, J; Xiong, L et al. Machine Learning to Recognize Phenomena in Large Scale Simulations. cs.cmu.edu, 2012. www 

2011

  1. Poczos, B; Krishner, S; Pal, D et al. Robust Nonparametric Copula Based Dependence Estimators. kilthub.figshare.com, 2011. www 
  2. Poczos, B; Xiong, L and Schneider, J Nonparametric divergence estimation and its applications to machine learning. kilthub.figshare.com, 2011. www 
  3. Szabó, Z; Póczos, B and Lőrincz, A Online dictionary learning with group structure inducing norms. discovery.ucl.ac.uk, 2011. www 

2010

  1. Sabhnani, R; Dubrawski, A; Schneider, J et al. Disjunctive Anomaly Detection: Identifying Complex Anomalous Patterns. cs.cmu.edu, 2010. www 
  2. Xiong, L; Poczos, B; Connolly, A et al. Anomaly detection for astronomical data. kilthub.figshare.com, 2010. www 
  3. Li, L; Póczos, B; Szepesvári, C et al. Budgeted distribution learning of belief net parameters. kilthub.figshare.com, 2010. www 
  4. Póczos, B; Kirshner, S and Szepesvári, C using Euclidean Graph Optimization. videolectures.net, 2010. www 
  5. Póczos, B; Kirshner, S and Szepesvári, C using Euclidean Graph Optimization. videolectures.net, 2010. www 

2009

  1. Waidyanatha, N; Gow, G; Prashat, S et al. Evaluation Guideline RealTime Biosurveillance Program. lirneasia.net, 2009. www 
  2. Dubrawski, Y. Learning Compressible Models. ra.adm.cs.cmu.edu, 2009. www 

2007

  1. Sabhnani, M; Moore, AW and Dubrawski, AW T-Cube: A data structure for fast extraction of time series from large datasets. apps.dtic.mil, 2007. www 
  2. Sabhnani, M; Moore, A and Dubrawski, A T-Cube: Fast extraction of time series from large datasets. … Mellon University, CMU-ML-06-104, 2007.
  3. Sabhnani, M; Moore, AW and Dubrawski, AW A Data Structure for Fast Extraction of Time Series from Large Datasets. reports-archive.adm.cs.cmu.edu, 2007. www 
  4. Póczos, B Independent subspace analysis. cs.cmu.edu, 2007. www 

2006

  1. Moore, A.; Schneider, J.; Kubica, J. et al. Scalable Detection and Optimization of N-ARY Linkages. apps.dtic.mil, 2006. www 

2005

  1. Szabó, Z; Póczos, B and Lőrincz, A Separation theorem for independent subspace analysis. discovery.ucl.ac.uk, 2005. www 

2004

  1. Dubrawski, A A framework for evaluating predictive capability of classifiers using receiver operating characteristic (ROC) approach: a brief introduction. Technical Report, Auton Laboratory …, 2004.

2002

  1. Andras, L; Barnabas, P; Gabor, S et al. Ockham's razor at work: Modeling of the homunculus''. philpapers.org, 2002. www 

2000

  1. Dubrawski, RA Myopic and far-sighted strategies for control of demand driven networks. University of Illinois at Urbana …, 2000.

1999

  1. Siemiątkowska, B and Dubrawski, A Global Map Building and Path Planning for Mobile Robots. LNCS 1424. Springer, 1999.
  2. Dubrawski, AW Neural networks for self-localization of mobile robots. rcin.org.pl, 1999. www 

Collections

2018

  1. Kandasamy, K.; Neiswanger, W.; Schneider, J. et al. Neural Architecture Search with Bayesian Optimisation and Optimal Transport. In Advances in Neural Information Processing Systems 31, pages 2016-2025, Curran Associates, Inc., 2018.

2016

  1. Kandasamy, K.; Dasarathy, G.; Oliva, J. B et al. Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations. In Advances in Neural Information Processing Systems 29, pages 992-1000, Curran Associates, Inc., 2016.
  2. Kandasamy, K.; Dasarathy, G.; Poczos, B. et al. The Multi-fidelity Multi-armed Bandit. In Advances in Neural Information Processing Systems 29, pages 1777-1785, Curran Associates, Inc., 2016.

2014

  1. Wang, X. and Schneider, J. Flexible Transfer Learning under Support and Model Shift. In Advances in Neural Information Processing Systems 27, pages 1898-1906, Curran Associates, Inc., 2014.

2013

  1. Huang, T-K. and Schneider, J. Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition. In Advances in Neural Information Processing Systems 26, pages 333-341, Curran Associates, Inc., 2013.
  2. Ma, Y.; Garnett, R. and Schneider, J. Sigma -Optimality for Active Learning on Gaussian Random Fields. In Advances in Neural Information Processing Systems 26, pages 2751-2759, Curran Associates, Inc., 2013.

2011

  1. Huang, T-k. and Schneider, J. G. Learning Auto-regressive Models from Sequence and Non-sequence Data. In Advances in Neural Information Processing Systems 24, pages 1548-1556, Curran Associates, Inc., 2011.
  2. Xiong, L.; Póczos, B. and Schneider, J. G. Group Anomaly Detection using Flexible Genre Models. In Advances in Neural Information Processing Systems 24, pages 1071-1079, Curran Associates, Inc., 2011.

2007

  1. Roure, J.; Dubrawski, A. and Schneider, J. A study into detection of bio-events in multiple streams of surveillance data. In Intelligence and security informatics: Biosurveillance, pages 124-133, Springer, 2007.

In Proceedings

2018

  1. A Tallavajhula, S M. and Kelly, A Off-Road Lidar Simulation using Data Driven Terrain Primitives. In IEEE International Conference on Robotics and Automation, 2018.

2017

  1. Fu, X.; Huang, K.; Stretcu, O. et al. BRAINZOOM: High resolution reconstruction from multi-modal brain signals. In Proceedings of the 2017 SIAM International Conference on Data Mining, pages 216-227, 2017.
  2. Ravanbakhsh, S.; Lanusse, F.; Mandelbaum, R. et al. Enabling Dark Energy Science with Deep Generative Models of Galaxy Images. In AAAI, pages 1488-1494, 2017.
  3. Kandasamy, K.; Dasarathy, G.; Schneider, J. et al. Multi-fidelity bayesian optimisation with continuous approximations. In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pages 1799-1808, 2017.
  4. Ravanbakhsh, S.; Schneider, J. and Póczos, B. Equivariance Through Parameter-sharing. In Proceedings of the 34th International Conference on Machine Learning - Volume 70, pages 2892-2901, JMLR.org, ICML'17 , 2017.
  5. Oliva, J. B.; Póczos, B. and Schneider, J. The Statistical Recurrent Unit. In Proceedings of the 34th International Conference on Machine Learning - Volume 70, pages 2671-2680, JMLR.org, ICML'17 , 2017.
  6. Ravanbakhsh, S.; Lanusse, F.; Mandelbaum, R. et al. Enabling dark energy science with deep generative models of galaxy images. In Thirty-First AAAI Conference on Artificial Intelligence, 2017.
  7. Ma, Y.; Garnett, R. and Schneider, J. Active search for sparse signals with region sensing. In Thirty-First AAAI Conference on Artificial Intelligence, 2017.

2016

  1. Kandasamy, K.; Dasarathy, G.; Oliva, J. B et al. Gaussian process bandit optimisation with multi-fidelity evaluations. In Advances in Neural Information Processing Systems, pages 992-1000, 2016.
  2. Singh, S.; Du, S. S and P'oczos, B. Efficient nonparametric smoothness estimation. In Advances in Neural Information Processing Systems, pages 1010-1018, 2016.
  3. Reddi, S. J; Sra, S.; P'oczos, B. et al. Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization. In Advances in Neural Information Processing Systems, pages 1145-1153, 2016.
  4. Dubey, K. A.; Reddi, S. J; Williamson, S. A et al. Variance reduction in stochastic gradient Langevin dynamics. In Advances in neural information processing systems, pages 1154-1162, 2016.
  5. Singh, S. and P'oczos, B. Finite-sample analysis of fixed-k nearest neighbor density functional estimators. In Advances in Neural Information Processing Systems, pages 1217-1225, 2016.
  6. Kandasamy, K.; Dasarathy, G.; Poczos, B. et al. The multi-fidelity multi-armed bandit. In Advances in Neural Information Processing Systems, pages 1777-1785, 2016.
  7. Reddi, S. J; Sra, S.; P'oczos, B. et al. Fast incremental method for smooth nonconvex optimization. In Decision and Control (CDC), 2016 IEEE 55th Conference on, pages 1971-1977, 2016.
  8. Tallavajhula, A.; P'oczos, B. and Kelly, A. Nonparametric distribution regression applied to sensor modeling. In Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on, pages 619-625, 2016.
  9. Li, C-L. and P'oczos, B. Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods. In UAI, 2016.
  10. Ravanbakhsh, S.; Oliva, J. B; Fromenteau, S. et al. Estimating Cosmological Parameters from the Dark Matter Distribution. In ICML, pages 2407-2416, 2016.
  11. Reddi, S. J; Hefny, A.; Sra, S. et al. Stochastic variance reduction for nonconvex optimization. In International conference on machine learning, pages 314-323, 2016.
  12. Ravanbakhsh, S.; P'oczos, B. and Greiner, R. Boolean Matrix Factorization and Noisy Completion via Message Passing. In ICML, pages 945-954, 2016.
  13. Wang, X.; Oliva, J. B; Schneider, J. G et al. Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems. In IJCAI, pages 2146-2152, 2016.
  14. Li, C-L.; Kandasamy, K.; P'oczos, B. et al. High dimensional Bayesian optimization via restricted projection pursuit models. In Artificial Intelligence and Statistics, pages 884-892, 2016.
  15. Ravanbakhsh, S.; P'oczos, B.; Schneider, J. et al. Stochastic neural networks with monotonic activation functions. In Artificial Intelligence and Statistics, pages 809-818, 2016.
  16. Oliva, J. B; Dubey, A.; Wilson, A. G et al. Bayesian nonparametric kernel-learning. In Artificial Intelligence and Statistics, pages 1078-1086, 2016.
  17. Sutherland, D. J; Oliva, J. B; P'oczos, B. et al. Linear-Time Learning on Distributions with Approximate Kernel Embeddings. In AAAI, pages 2073-2079, 2016.
  18. Ravanbakhsh, S.; Oliva, J. B; Fromenteau, S. et al. Estimating Cosmological Parameters from the Dark Matter Distribution. In ICML, pages 2407-2416, 2016.
  19. Oliva, J. B; Dubey, A.; Wilson, A. G et al. Bayesian nonparametric kernel-learning. In Artificial Intelligence and Statistics, pages 1078-1086, 2016.
  20. Ravanbakhsh, S.; P'oczos, B.; Schneider, J. et al. Stochastic neural networks with monotonic activation functions. In Artificial Intelligence and Statistics, pages 809-818, 2016.
  21. Li, C-L.; Kandasamy, K.; P'oczos, B. et al. High dimensional bayesian optimization via restricted projection pursuit models. In Artificial Intelligence and Statistics, pages 884-892, 2016.
  22. Wang, X.; Oliva, J. B; Schneider, J. G et al. Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems. In IJCAI, pages 2146-2152, 2016.
  23. Sutherland, D. J; Oliva, J. B; P'oczos, B. et al. Linear-time learning on distributions with approximate kernel embeddings. In Thirtieth AAAI Conference on Artificial Intelligence, 2016.
  24. A Kelly, B P. and Tallavajhula, A Nonparametric Distribution Regression Applied to Sensor Modeling. In IEEE International Conference on Intelligent Robots and Systems (IROS), 2016.
  25. A Tallavajhula, S C. S S. and Kelly, A List Prediction Applied To Motion Planning. In IEEE International Conference on Robotics and Automation, 2016.

2015

  1. Kandasamy, K.; Schneider, J. and P'oczos, B. High dimensional Bayesian optimisation and bandits via additive models. In International Conference on Machine Learning, pages 295-304, 2015.
  2. Kandasamy, K.; Schneider, J. and P'oczos, B. Bayesian active learning for posterior estimation. In Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.
  3. Oliva, J.; Neiswanger, W.; P'oczos, B. et al. Fast function to function regression. In Artificial Intelligence and Statistics, pages 717-725, 2015.
  4. Szab'o, Z.; Gretton, A.; P'oczos, B. et al. Two-stage sampled learning theory on distributions. In Artificial Intelligence and Statistics, pages 948-957, 2015.
  5. Garnett, R.; Ho, S. and Schneider, J. Finding galaxies in the shadows of quasars with Gaussian processes. In International Conference on Machine Learning, pages 1025-1033, 2015.
  6. Kandasamy, K.; Schneider, J. and P'oczos, B. High dimensional Bayesian optimisation and bandits via additive models. In International Conference on Machine Learning, pages 295-304, 2015.
  7. Kandasamy, K.; Schneider, J. and P'oczos, B. Bayesian active learning for posterior estimation. In Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.
  8. Wang, X. and Schneider, J. G Generalization Bounds for Transfer Learning under Model Shift. In UAI, pages 922-931, 2015.
  9. Ma, Y.; Huang, T-K. and Schneider, J. G Active Search and Bandits on Graphs using Sigma-Optimality. In UAI, pages 542-551, 2015.
  10. Ma, Y.; Sutherland, D.; Garnett, R. et al. Active pointillistic pattern search. In Artificial Intelligence and Statistics, pages 672-680, 2015.
  11. Kelly, A and Tallavajhula, A Construction and Validation of a High Fidelity Simulator for a Planar Range Sensor. In IEEE International Conference on Robotics and Automation, 2015.

2014

  1. Ma, Y.; Garnett, R. and Schneider, J. Active area search via Bayesian quadrature. In Artificial intelligence and statistics, pages 595-603, 2014.
  2. Wang, X.; Huang, T-K. and Schneider, J. Active transfer learning under model shift. In International Conference on Machine Learning, pages 1305-1313, 2014.
  3. Ma, Y.; Garnett, R. and Schneider, J. Active area search via Bayesian quadrature. In Artificial intelligence and statistics, pages 595-603, 2014.
  4. Oliva, J.; P'oczos, B.; Verstynen, T. et al. Fusso: Functional shrinkage and selection operator. In Artificial Intelligence and Statistics, pages 715-723, 2014.
  5. Oliva, J.; Neiswanger, W.; P'oczos, B. et al. Fast distribution to real regression. In Artificial Intelligence and Statistics, pages 706-714, 2014.

2013

  1. Sutherland, D. J; P'oczos, B. and Schneider, J. Active learning and search on low-rank matrices. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 212-220, 2013.
  2. Huang, T-K. and Schneider, J. Spectral learning of hidden Markov models from dynamic and static data. In International Conference on Machine Learning, pages 630-638, 2013.
  3. Oliva, J.; P'oczos, B. and Schneider, J. Distribution to distribution regression. In International Conference on Machine Learning, pages 1049-1057, 2013.
  4. Tesch, M.; Schneider, J. and Choset, H. Expensive function optimization with stochastic binary outcomes. In International Conference on Machine Learning, pages 1283-1291, 2013.
  5. Wang, X.; Garnett, R. and Schneider, J. Active Search on Graphs. In Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 731-738, ACM, New York, NY, USA, KDD '13 , 2013.
  6. Sutherland, D. J; P'oczos, B. and Schneider, J. Active learning and search on low-rank matrices. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 212-220, 2013.
  7. Xiong, L.; P'oczos, B. and Schneider, J. Efficient learning on point sets. In 2013 IEEE 13th International Conference on Data Mining, pages 847-856, 2013.
  8. M Dogar, M K. A T. and Srinivasa, S Object Search by Manipulation. In IEEE International Conference on Robotics and Automation, 2013.

2012

  1. Zhang, Yi and Schneider, J. A composite likelihood view for multi-label classification. In Artificial Intelligence and Statistics, pages 1407-1415, 2012.
  2. P'oczos, B. and Schneider, J. G Nonparametric Estimation of Conditional Information and Divergences. In AISTATS, pages 914-923, 2012.
  3. Zhang, Yi and Schneider, J. A composite likelihood view for multi-label classification. In Artificial Intelligence and Statistics, pages 1407-1415, 2012.
  4. P'oczos, B.; Xiong, L.; Sutherland, D. J et al. Nonparametric kernel estimators for image classification. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 2989-2996, 2012.
  5. Huang, T-K. and Schneider, J. Learning Bi-clustered Vector Autoregressive Models. In Machine Learning and Knowledge Discovery in Databases, pages 741-756, Springer Berlin Heidelberg, Berlin, Heidelberg, 2012.

2011

  1. Garnett, R.; Krishnamurthy, Y.; Wang, D. et al. Bayesian optimal active search on graphs. In Ninth Workshop on Mining and Learning with Graphs, 2011.
  2. P'oczos, B. and Schneider, J. On the estimation of alpha-divergences. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pages 609-617, 2011.
  3. Xiong, L.; P'oczos, B.; Schneider, J. et al. Hierarchical probabilistic models for group anomaly detection. In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pages 789-797, 2011.
  4. Zhang, Yi and Schneider, J. Multi-label output codes using canonical correlation analysis. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pages 873-882, 2011.
  5. Tesch, M.; Schneider, J. and Choset, H. Adapting control policies for expensive systems to changing environments. In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 357-364, 2011.
  6. Tesch, M.; Schneider, J. and Choset, H. Using response surfaces and expected improvement to optimize snake robot gait parameters. In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1069-1074, 2011.
  7. P'oczos, B.; Szab'o, Z. and Schneider, J. Nonparametric divergence estimators for independent subspace analysis. In 2011 19th European Signal Processing Conference, pages 1718-1722, 2011.
  8. Xiong, L.; Chen, Xi and Schneider, J. Direct robust matrix factorizatoin for anomaly detection. In 2011 IEEE 11th International Conference on Data Mining, pages 844-853, 2011.

2010

  1. Zhang, Yi and Schneider, J. G Learning multiple tasks with a sparse matrix-normal penalty. In Advances in neural information processing systems, pages 2550-2558, 2010.
  2. Zhang, Yi and Schneider, J. G Projection penalties: dimension reduction without loss. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), pages 1223-1230, 2010.
  3. Huang, T--.; Song, Le and Schneider, J. Learning nonlinear dynamic models from non-sequenced data. In Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pages 350-357, 2010.
  4. Xiong, L.; Chen, Xi; Huang, T-K. et al. Temporal collaborative filtering with bayesian probabilistic tensor factorization. In Proceedings of the 2010 SIAM international conference on data mining, pages 211-222, 2010.
  5. Zhang, Yi; Schneider, J. and Dubrawski, A. Learning compressible models. In Proceedings of the 2010 SIAM International Conference on Data Mining, pages 872-881, 2010.
  6. Donmez, P.; Carbonell, J. and Schneider, J. A probabilistic framework to learn from multiple annotators with time-varying accuracy. In Proceedings of the 2010 SIAM International Conference on Data Mining, pages 826-837, 2010.

2009

  1. Huang, T-K. and Schneider, J. Learning linear dynamical systems without sequence information. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 425-432, 2009.
  2. Donmez, P.; Carbonell, J. G and Schneider, J. Efficiently learning the accuracy of labeling sources for selective sampling. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 259-268, 2009.
  3. Zhang, Yi; Dubrawski, A. and Schneider, J. G Learning the semantic correlation: An alternative way to gain from unlabeled text. In Advances in Neural Information Processing Systems, pages 1945-1952, 2009.

2008

  1. Das, K.; Schneider, J. and Neill, D. B. Anomaly Pattern Detection in Categorical Datasets. In Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 169-176, ACM, New York, NY, USA, KDD '08 , 2008.
  2. Bryan, B. and Schneider, J. Actively Learning Level-sets of Composite Functions. In Proceedings of the 25th International Conference on Machine Learning, pages 80-87, ACM, New York, NY, USA, ICML '08 , 2008.

2007

  1. Sarkar, P.; Siddiqi, S. M and Gordon, G. J A latent space approach to dynamic embedding of co-occurrence data. In Artificial Intelligence and Statistics, pages 420-427, 2007.
  2. Bryan, B.; McMahan, H. B.; Schafer, C. M. et al. Efficiently Computing Minimax Expected-size Confidence Regions. In Proceedings of the 24th International Conference on Machine Learning, pages 97-104, ACM, New York, NY, USA, ICML '07 , 2007.
  3. Roure, J.; Dubrawski, A. and G. Schneider, J. A Study into Detection of Bio-Events in Multiple Streams of Surveillance Data. doi  www 

2006

  1. Neill, D. B; Moore, A. W and Cooper, G. F A Bayesian spatial scan statistic. In Advances in neural information processing systems, pages 1003-1010, 2006.
  2. Bryan, B.; Nichol, R. C; Genovese, C. R et al. Active learning for identifying function threshold boundaries. In Advances in neural information processing systems, pages 163-170, 2006.

2005

  1. Pelleg, D. and Moore, A. W Active learning for anomaly and rare-category detection. In Advances in neural information processing systems, pages 1073-1080, 2005.
  2. Bryan, B.; Schneider, J.; Nichol, R. C. et al. Active Learning for Identifying Function Threshold Boundaries. In Proceedings of the 18th International Conference on Neural Information Processing Systems, pages 163-170, MIT Press, Cambridge, MA, USA, NIPS'05 , 2005.

2004

  1. Neill, D. B and Moore, A. W A fast multi-resolution method for detection of significant spatial disease clusters. In Advances in Neural Information Processing Systems, pages 651-658, 2004.

2003

  1. Goldenberg, A.; Kubica, J.; Komarek, P. et al. A comparison of statistical and machine learning algorithms on the task of link completion. In KDD Workshop on Link Analysis for Detecting Complex Behavior, pages 8, 2003.

2000

  1. Komarek, P. and Moore, A. W A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets. In ICML, pages 495-502, 2000.
  2. Anderson, B. S; Moore, A. W and Cohn, D. A nonparametric approach to noisy and costly optimization. In ICML, pages 17-24, 2000.

1998

  1. Siemiatkowska, B and Dubrawski, A Cellular neural networks for navigation of a mobile robot. In International Conference on Rough Sets and Current Trends in Computing, pages 147-154, Springer, 1998.

Misc

2018

  1. Poczos, B and Tibshirani, R Generalized gradient descent and acceleration. , Query date: 2019-04-09.

2006

  1. Barnabás, P Függetlenaltér-analízis. , Query date: 2019-04-09.

Technical Reports

2018

  1. Tallavajhula, A Lidar Simulation for Robotic Application Development: Modeling and Evaluation. Technical Report CMU-RI-TR-18-18, Robotics Institute, Carnegie Mellon University, 2018.

2015

  1. Tallavajhula, A and Choudhury, S List prediction for motion planning: Case studies. Technical Report CMU-RI-TR-15-25, Robotics Institute, Carnegie Mellon University, 2015.

2008

  1. Siddiqi, S. M; Boots, B. and Gordon, G. J A constraint generation approach to learning stable linear dynamical systems. Technical Report, CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE, 2008.