autonlab.org

Papers

NameAuthorsShort descriptionBook titleYearActions
Active Area Search via Bayesian Quadrature

Yifei Ma and Roman Garnett and Jeff Schneider

Gateway to "AAS via BQ". Paper and Code2014show
Fast Distribution To Real Regression

Junier Oliva, Willie Neiswanger, Barnabas Poczos, Jeff Schneider, Eric Xing

AISTATS 20142014show
FuSSO: Functional Shrinkage and Selection Operator

Junier Oliva, Barnabas Poczos, Timothy Verstynen, Aarti Singh, Jeff Schneider, Fang-Cheng Yeh, Wen-Yih Tseng

AISTATS 20142014show
Active Transfer Learning under Model Shift

Xuezhi Wang, Tzu-Kuo Huang, Jeff Schneider

ICML2014show
Learning from Point Sets with Observational Bias

Liang Xiong and Jeff Schneider

Uncertainty in Artificial Intelligence (UAI)2014show
Flexible Transfer Learning under Support and Model Shift

Xuezhi Wang, Jeff Schneider

transfer learning with flexible transformation on both features and labelsNIPS2014show
Distribution to Distribution Regression

Junier Oliva, Barnabas Poczos, Jeff Schneider

ICML 20132013show
Spectral Learning of Hidden Markov Models from Dynamic and Static Data

Tzu-Kuo Huang, Jeff Schneider

Proceedings of the 30th International Conference on Machine Learning2013show
Active Search on Graphs

Xuezhi Wang, Roman Garnett, Jeff Schneider

a soft-label model and the impact criterion for active search on large graph datasetsKDD 20132013show
Active Learning and Search on Low-Rank Matrices

Dougal J. Sutherland, Barnabás Póczos, and Jeff Schneider

ACM SIGKDD2013show
Sigma-Optimality in Active Learning on Gaussian Random Fields

Yifei Ma and Roman Garnett and Jeff Schneider

A new heuristic proposed. Algorithmic bounds discovered.NIPS2013show
Efficient Learining on Point Sets

Liang Xiong, Barnabas Poczos, Jeff Schneider

IEEE International Conference on Data Mining2013show
Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition

Tzu-Kuo Huang, Jeff Schneider

Advances in Neural Information Processing Systems2013show
Separation Theorem for Independent Subspace Analysis and its Consequences

Zoltan Szabo, Barnabas Poczos, Andras Lorincz

Review on different generalizations of independent subspace analysis and the ISA separation principle.Pattern Recognition2012show
Kernels on Sample Sets via Nonparametric Divergence Estimates

Dougal J. Sutherland, Liang Xiong, Barnabás Póczos, Jeff Schneider

Kernel algorithms on distributionsTechnical Report2012show
Collaborative Filtering via Group-Structured Dictionary Learning

Zoltan Szabo, Barnabas Poczos, Andras Lorincz

Application of structured dictionary learning for collaborative filteringLVA-ICA 20122012show
Nonparametric Estimation of Conditional Information and Divergences

Barnabas Poczos, Jeff Schneider

nonparametric conditional mutual information estimatorAISTATS 20122012show
Nonparametric Kernel Estimators for Image Classification

Barnabas Poczos, Liang Xiong, Dougal J. Sutherland, Jeff Schneider

nonparametric kernel estimation for image classificationCVPR 20122012show
Copula-based Kernel Dependency Measures

Barnabas Poczos, Zoubin Ghahramani, Jeff Schneider

Copula-based Kernel Dependency MeasuresICML 20122012show
Bayesian Optimal Active Search and Surveying

Roman Garnett, Yamuna Krishnamurthy, Xuehan Xiong, Jeff Schneider, Richard Mann

Proceredings of the 29th Annual International Conference on Machine Learning (ICML 2012)2012show
Maximum Margin Output Coding

Yi Zhang and Jeff Schneider

Maximum Margin Output CodingICML2012show
Learning Bi-clustered Vector Autoregressive Models

Tzu-Kuo Huang,Jeff Schneider

ECML2012show
An Impact Criterion for Active Graph Search

Xuezhi Wang, Roman Garnett, Jeff Schneider

proposed impact criterion for active graph searchNIPS workshop on Bayesian Optimization and Decision Making2012show
A Composite Likelihood View for Multi-Label Classification

Yi Zhang and Jeff Schneider

A Composite Likelihood View for Multi-Label ClassificationAISTATS 20122012show
On the Estimation of alpha-Divergences

Barnabas Poczos, Jeff Schneider

A nonparametric Renyi and Tsallis divergence estimatorAISTATS 20112011show
Hierarchical Probabilistic Models for Group Anomaly Detection

Liang Xiong, Barnabas Poczos, Jeff Schneider, Andrew Connolly, Jake VanderPlas

Multinomial genre model for group anomaly detectionAISTATS 20112011show
Online Group-Structured Dictionary Learning

Zoltan Szabo, Barnabas Poczos, Andras Lorincz

Online dictionary learning method which enables overlapping group structures with non-convex sparsity-inducing regularization and handles the partially observable caseCVPR 20112011show
Multi-label Output Codes using Canonical Correlation Analysis

Yi Zhang and Jeff Schneider

Multi-label Output Codes using Canonical Correlation AnalysisAISTATS 20112011show
Nonparametric Divergence Estimation for Machine Learning on Distributions

Barnas Poczos, Liang Xiong, Jeff Schneider

Learning Workshop (Snowbird) poster2011show
Nonparametric Independent Process Analysis

Zoltan Szabo, Barnabas Poczos

EUSIPCO 20112011show
Nonparametric Divergence Estimators for Independent Subspace Analysis

Barnabas Poczos, Zoltan Szabo, Jeff Schneider

Nonparametric Divergence Estimators for Independent Subspace AnalysisEUSIPCO 20112011show
Online Dictionary Learning with Group Structure Inducing Norms

Zoltan Szabo, Barnabas Poczos, Andras Lorincz

ICML 2011 workshop paper on online dictionary learning with group structure inducing normsICML-2011 workshop on "Structured Sparsity: Learning and Inference"2011show
Nonparametric divergence estimation with applications to machine learning on distributions

Barnabas Poczos, Liang Xiong, Jeff Schneider

Divergence estimatiors with applications to clustering, classification, low-dimensional embedding, outlier detection.UAI 20112011show
Learning Dynamic Models from Non-sequenced Data

Tzu-Kuo Huang, Jeff Schneider

Learning workshop (Snowbird) poster2011show
Direct Robust Matrix Factorization

Liang Xiong, Xi Chen, Jeff Schneider

IEEE International Conference on Data Mining2011show
Group Anomaly Detection using Flexible Genre Models

Liang Xiong, Barnabas Poczos, Jeff Schneider

flexible genre modelsNIPS2011show
Robust Nonparametric Copula Based Dependence Estimators

Barnabas Poczos, Sergey Krishner, David Pal, Csaba Szepesvari, Jeff Schneider

Review on copula based dependence estimatorsCopulas in machine learning. Nips 2011 Workshop2011show
Learning Auto-regressive Models from Sequence and Non-sequence Data

Tzu-Kuo Huang, Jeff Schneider

Combining sequence and non-sequence data to improve dynamic model learningNIPS 20112011show
Bayesian Optimal Active Search on Graphs

Roman Garnett, Yamuna Krishnamurthy, Donghan Wang, Jeff Schneider, Richard Mann

Proceedings of the Ninth Annual Workshop on Mining and Learning with graphs (MLG 2011), at KDD 2011.2011show
Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization

Liang Xiong, Xi Chen, Tzu-Kuo Huang, Jeff Schneider, Jaime G. Carbonell

Bayesian probabilistic tensor factorizationProceedings of SIAM Data Mining2010show
Learning Compressible Models

Yi ZhangJeff Schneider, Artur Dubrawski

Learning Compressible ModelsIn Proceedings of SIAM Data Mining (SDM) Conference2010show
Learning Nonlinear Dynamic Models from Non-sequenced Data

Tzu-Kuo Huang, Le Song, Jeff Schneider

Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS) 20102010show
Projection Penalties: Dimension Reduction without Loss

Yi Zhang, Jeff Schneider

Projection Penalties for Using Dimension Reduction without LossICML 20102010show
Multi-task Active Learning with Output Constraints

Yi Zhang

A multi-task active learning framework with task outputs coupled by constraintsAAAI 20102010show
Fast Nearest-neighbor Search in Disk-resident Graphs

Purnamrita Sarkar

Andrew W. Moore

2010show
Learning Multiple Tasks with a Sparse Matrix-Normal Penalty

Yi Zhang and Jeff Schneider

NIPS 20102010show
Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs

David Pal, Barnabas Poczos, Csaba Szepesvari

A nonparametric method for entropy and mutual information estimation. We prove consistency and provide convergence rates.NIPS 20102010show
T-Cube Web Interface as a tool for detecting disease outbreaks in real-time: A pilot in India and Sri Lanka

Nuwan Waidyanatha, Chamindu Sampath, Artur Dubrawski, Maheshkumar Sabhnani, Lujie Chen

T-Cube Web Interface for detecting disease outbreaksRIVF 20102010show
Automated Detection of Data Entry Errors in a Real Time Surveillance System

Lujie Chen, Artur Dubrawski, Nuwan Waidyanatha, Chamindu Weerasinghe

Error detection in real time biosurveillanceAdvances in Disease Surveillance2010show
Challenges of Introducing Disease Surveillance Technology in Developing Countries: Experiences from India and Sri Lanka

Chamindu Weerasinghe, Nuwan Waidyanatha, Artur Dubrawski, Michael Baysek

2010show
Fast Dynamic Reranking in Large Graphs

Purnamrita Sarkar, Andrew W. Moore

International World Wide Web Conference2009show
Learning Linear Dynamical Systems without Sequence Information

Tzu-Kuo Huang, Jeff Schneider

ICML 2009: Proceedings of the 26th International Conference on Machine Learning2009show
Trade-offs between Agility and Reliability of Predictions in Dynamic Social Networks Used to Model Risk of Microbial Contamination of Food

Artur Dubrawski, Purnamrita Sarkar, Lujie Chen

Best paper award, ASONAM 20092009 International Conference on Advances in Social Network Analysis and Mining ASONAM 20092009show
Evolution of a Useful Autonomous System

Artur Dubrawski, Henry Thorne

Proceedings of the 7th International Workshop on Robot Motion and Control2009show
T-Cube Web Interface in Support of Real-Time Bio-surveillance Program

Artur Dubrawski, Maheshkumar Sabhnani, Michael Knight, Michael Baysek, Daniel Neill, Saswati Ray, Anna Michalska, Nuwan Waidyanatha

Proceedings of the International Conference on Information and Communication Technologies and Development ICTD 20092009show
Smart PCA

Yi Zhang

Smart PCA algorithm to incorporate domain knowledge into dimension reductionIJCAI 20092009show
T-Cube Web Interface for Real-time Biosurveillance in Sri Lanka

Maheshkumar Sabhnani, Artur Dubrawski, Nuwan Waidyanatha

Advances in Disease Surveillance2009show
Discovering Possible Linkages between Food-borne Illness and the Food Supply Using an Interactive Analysis Tool

Artur Dubrawski, Lujie Chen, Maheshkumar Sabhnani, Paula J. Fedorka-Cray,
Lynda Kelley, Peter Gerner-Smidt, Ian Williams, Mark Huckabee, Adrienne Dunham

Advances in Disease Surveillance2009show
Compact Spatial Joins

Brent Bryan, Frederick Eberhardt, Christos Faloutsos

ICDE2008show
Actively Learning Level-Sets of Composite Functions

Brent Bryan, Jeff Schneider

ICML 2008: Proceedings of the 25th International Conference on Machine Learning2008show
Fast Incremental Proximity Search in Large Graphs

Purnamrita Sarkar, Andrew W. Moore, Amit Prakash

Slightly revised from the ICML camera-ready versionProceedings of the 25th International Conference on Machine Learning2008show
Anomaly Pattern Detection in Categorical Datasets

Kaustav Das, Jeff Schneider and Daniel Neill

Proceedings of 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2008)2008show
Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text

Yi ZhangJeff Schneider, Artur Dubrawski

A semi-supervised learning algorithm on textNIPS2008show
Dynamic Network Model for Predicting Occurrences of Salmonella at Food Facilities

Purnamrita Sarkar, Lujie Chen, Artur Dubrawski

BioSecure 20082008show
Efficiently Computing Minimax Expected-Size Confidence Regions

Brent Bryan, H. Brendan McMahan, Chad M. Schafer, Jeff Schneider

ICML 2007: Proceedings of the 24th International Conference on Machine Learning2007show
Mapping the Cosmological Confidence Ball Surface

Brent Bryan, Jeff Schneider, Christopher J. Miller, Robert C. Nichol, Christopher Genovese, Larry Wasserman

2007show
A Tractable Approach to Finding Closest Truncated-commute-time Neighbors in Large Graphs

Purnamrita Sarkar, Andrew W. Moore

The 23rd Conference on Uncertainty in Artificial Intelligence(UAI)2007show
A Latent Space Approach to Dynamic Embedding of Co-occurrence Data

Purnamrita Sarkar, Sajid Siddiqi, Geoff Gordon

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AI-STATS)2007show
Detecting Anomalous Records in Categorical Datasets

Kaustav Das, Jeff Schneider

Proc. of the thirteenth ACM SIGKDD international conference on Knowledge discovery and data mining2007show
A Study into Detection of Bio-Events in Multiple Streams of Surveillance Data

Josep Roure, Artur Dubrawski, Jeff Schneider

Intelligence and Security Informatics: Biosurveillance2007show
A Constraint Generation Approach to Learning Stable Linear Dynamical Systems

Sajid Siddiqi

Byron Boots

Geoff Gordon

Advances in Neural Information Processing Systems2007show
Fast State Discovery for HMM Model Selection and Learning

Sajid Siddiqi

Geoff Gordon

Andrew Moore

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AI-STATS)2007show
Actively Learning Specific Function Properties with Applications to Statistical Inference

Brent Bryan

2007show
Applying Outbreak Detection Algorithms to Prognostics

Artur Dubrawski, Michael Baysek, Shannon Mikus, Charles McDaniel, Bradley Mowry, Laurel Moyer, John Ostlund, Norman Sondheimer, Timothy Stewart

AAAI Fall Symposium on Artificial Intelligence in Prognostics2007show
T-Cube as an Enabling Technology in Surveillance Applications

Artur Dubrawski, Maheshkumar Sabhnani, Saswati Ray, Josep Roure, Michael Baysek

Advances in Disease Surveillance2007show
Dependency Trees in Sub-linear Time and Bounded Memory

Dan Pelleg, Andrew Moore

Efficient learning of dependency trees for huge datasets.2006show
Monitoring Food Safety by Detecting Patterns in Consumer Complaints

Artur Dubrawski, Kimberly Elenberg, Andrew Moore, Maheshkumar Sabhnani

Proceedings of the National Conference on Artificial Intelligence AAAI/IAAI 20062006show
Autonomous Visualization

Khalid El-Arini, Andrew W. Moore, Ting Liu

European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2006)2006show
Sequential Update of ADtrees

Josep Roure, Andrew W. Moore

ICML2006show
Disease Outbreak Detection using Discriminative Random Field

Kaustav Das, Robin Sabhnani, Eric Xing

2006show
Detecting Significant Multidimensional Spatial Clusters

Daniel Neill, Andrew Moore

Applying the fast multidimensional spatial scan statistic to detect clusters in epidemiological and brain imaging data.Advances in Neural Information Processing Systems 172005show
Efficiently Identifying Close Track/Observation Pairs in Continuous Timed Data

Jeremy Kubica, Andrew Moore, Andrew Connolly, Robert Jedicke

Proc. SPIE Signal and Data Processing of Small Targets2005show
Fast Inference and Learning in Large-State-Space HMMs

Sajid Siddiqi, Andrew Moore

Proceedings of the 22nd International Conference on Machine Learning2005show
Making Logistic Regression A Core Data Mining Tool: A Practical Investigation of Accuracy, Speed, and Simplicity

Paul Komarek, Andrew Moore

Regularized logistic regression can be fast, accurate, and simple. This paper includes the most important findings of my thesis, and a few new details.2005show
Alias Detection in Link Data Sets

Paul Hsiung, Andrew Moore, Daniel Neill, Jeff Schneider

Combining string similarity with contextual similarity when searching for aliases using active learning.Proceedings of the International Conference on Intelligence Analysis2005show
Algorithms for rapid outbreak detection: a research synthesis2005show
A Multiple Tree Algorithm for the Efficient Association of Asteroid Observations

Jeremy Kubica, Andrew Moore, Andrew Connolly, Robert Jedicke

The Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining2005show
Making Logistic Regression A Core Data Mining Tool With TR-IRLS

Paul Komarek, Andrew Moore

This short paper is the easiest, fastest way to learn about Truncated Regularized Iteratively Re-weighted Least Squares (TR-IRLS), my algorithm for fast, parameter-free logistic regression. TR-IRLS can also be used for any generalized linear model. ThisProceedings of the 5th International Conference on Data Mining Machine Learning2005show
Finding Optimal Bayesian Networks by Dynamic Programming

Ajit Singh, Andrew Moore

Learning the optimal Bayes net structure2005show
Person Identification in Webcam Images: An Application of Semi-Supervised Learning

Maria-Florina Balcan, Avrim Blum, Patrick Pakyan Choi, John Lafferty, Brian Pantano, Mugizi R. Rwebangira, Xiaojin Zhu

2005show
Bayesian Detection of Router Configuration Anomalies

Khalid El-Arini, Kevin Killourhy

ACM SIGCOMM Workshop on Mining Network Data (MineNet-05)2005show
Anomalous Spatial Cluster Detection

Daniel B. Neill, Andrew W. Moore

A general and powerful framework for spatial cluster detection.Proceedings of the KDD 2005 Workshop on Data Mining Methods for Anomaly Detection2005show
Detecting Anomalous Patterns in Pharmacy Retail Data

Robin Sabhnani, Daniel Neill, Andrew Moore

A bio-surveillance system to collect disease outbreak feedback from public health officialsProceedings of the KDD 2005 Workshop on Data Mining Methods for Anomaly Detection2005show
Dynamic Social Network Analysis using Latent Space Models

Purnamrita Sarkar and Andrew Moore

2005show
Learning Predictive Models from Small Sets of Dirty Data

Ashwin Tengli, Artur Dubrawski and Lujie Chen

2005show
Efficient Analytics for Effective Monitoring of Biomedical Security

Robin Sabhnani, Daniel B. Neill, Andrew W. Moore, Artur W. Dubrawski, Weng-Keen Wong

2005show
Variable KD-Tree Algorithms for Spatial Pattern Search and Discovery

Jeremy Kubica, Joseph Masiero, Andrew Moore, Robert Jedicke, Andrew Connolly

Advances in Neural Information Processing Systems2005show
Active Learning For Identifying Function Threshold Boundaries

Brent Bryan, Jeff Schneider, Robert C. Nichol, Christopher J. Miller, Christopher R. Genovese, Larry Wasserman

2005show
Bayes Net Graphs to Understand Coauthorship Networks2005show
A Bayesian spatial scan statistic

Daniel Neill, Andrew Moore, Gregory Cooper

A new Bayesian method for spatial cluster detectionAdvances in Neural Information Processing Systems2005show
A Bayesian scan statistic for spatial cluster detection

Daniel Neill, Andrew Moore, Gregory Cooper

A new Bayesian method for cluster detectionProceedings of the National Syndromic Surveillance Conference2005show
Tractable Learning of Large Bayes Net Structures from Sparse Data

Anna Goldenberg, Andrew W. Moore

in this paper we propose an algorithm that allows to learn a Bayes Net structure from sparse data (e.g., power-law distributed) with over 100,000 variables. we also report time and performance accuracy when applied to several very large datasetsICML2004show
Alias Detection in Link Data Sets

Paul Hsiung, Andrew Moore, Daniel Neill, Jeff Schneider

An active learning approach to deciding whether two names correspond to the same entity, combining string similarity information and context similarity.2004show
Rapid Detection of Significant Spatial Clusters

Daniel Neill, Andrew Moore

A new spatial scan algorith that searches over arbitrary rectangles in addition to squares.2004show
The IOC algorithm: Efficient Many-Class Non-parametric Classification for High-Dimensional Data

Ting Liu, Ke Yang, Andrew Moore

Performing k-nearest-neghbor classifications on multi-class problems without actually finding the k-nearest neighbors.Proceedings of the conference on Knowledge Discovery in Databases (KDD)2004show
Semantic based Biomedical Image Indexing and Retrieval

Yanxi Liu, N Lazar, W Rothfus, Frank Dellaert, Andrew Moore, Jeff Schneider, Takeo Kanade

Volumetric pathological neuroimage retrieval under the framework of classification-driven feature selection.Trends and Advances in Content-Based Image and Video Retrieval2004show
Active Learning for Anomaly and Rare-Category Detection

Dan Pelleg, Andrew Moore

How to use active learning in a real-life scenario.Advances in Neural Information Processing Systems 182004show
Fast Nonlinear Regression via Eigenimages Applied to Galactic Morphology

Brigham Anderson, Andrew Moore, Andrew Connolly, Robert Nichol

Determining the shapes of millions of galaxies.2004show
High-Dimensional Probabilistic Classification for Drug Discovery

Alexander Gray, Paul Komarek, Ting Liu, Andrew Moore

Discriminative probabilistic classifiers have been used successfully on large life-sciences datasets, but high dimensionalities have prohibited the use of nonparametric class probability estimation. This paper explores a method (SLAMDUNK) which addressesProceedings of the Computational Statistics2004show
Logistic Regression for Data Mining and High-Dimensional Classification

Paul Komarek

We document several approached to logistic regression parameter estimation, and detail the most promising implementation for high-dimensinal classification.2004show
An Investigation of Practical Approximate Nearest Neighbor Algorithms

Ting Liu, Andrew Moore, Alexander Gray, Ke Yang

How to use variations on classic exact data structures for nearest neighbor, if you want to get faster answers and are prepared to accept approximation?2004show
Belief State Approaches to Signaling Alarms in Surveillance Systems

Kaustav Das, Andrew Moore, Jeff Schneider

KDD 2004 Proceedings2004show
Fast Robust Logistic Regression for Large Sparse Datasets with Binary Outputs

Paul Komarek, Andrew Moore

Logistic regression can provide faster, better results than SVM for life-sciences datasets with hundreds of thousands of attributes.Artificial Intelligence and Statistics2003show
A Comparison of Statistical and Machine Learning Algorithms on the Task of Link Completion

Anna Goldenberg, Jeremy Kubica, Paul Komarek, Andrew Moore, Jeff Schneider

This paper examines the task of link completion, relative algorithm performance, and what this can tell us about the structure of the data.KDD Workshop on Link Analysis for Detecting Complex Behavior2003show
Covariant Policy Search

Drew Bagnell, Jeff Schneider

A simple algorithm leads to very fast, covariant policy search.Proceedings of the International Joint Conference on Artificial Intelligence2003show
Policy Search by Dynamic Programming

Drew Bagnell, Sham Kakade, Andrew Ng, Jeff Schneider

Dynamic programming techniques can make direct policy search computationally and sample efficient.Proceedings of Neural Information Processing Systems2003show
Probabilistic Noise Identification and Data Cleaning

Jeremy Kubica, Andrew Moore

We examine the use of explicit noise and corruption models to aid in the task of noise identification and data cleaning.The Third IEEE International Conference on Data Mining2003show
cGraph: A Fast Graph-Based Method for Link Analysis and Queries

Jeremy Kubica, Andrew Moore, David Cohn, Jeff Schneider

This paper is an extended version of the 2003 ICML conference paper.Proceedings of the 2003 IJCAI Text-Mining & Link-Analysis Workshop2003show
Finding Underlying Connections: A Fast Graph-Based Method for Link Analysis and Collaboration Queries

Jeremy Kubica, Andrew Moore, David Cohn, Jeff Schneider

CGraph is an algorithm to quickly learn a graph-based model of the underlying connections of a set of entities given link data.Proceedings of the International Conference on Machine Learning2003show
Tractable Group Detection on Large Link Data Sets

Jeremy Kubica, Andrew Moore, Jeff Schneider

We present the k-groups algorithm, an improvement of the GDA algorithm that includes significant computational advantages. The k-groups algorithm allows tractable group detection on large data sets.The Third IEEE International Conference on Data Mining2003show
Optimal Reinsertion: A new search operator for accelerated and more accurate Bayesian network structure learning

Andrew Moore, Weng-Keen Wong

Very aggressive but computationally efficient search steps for Bayes net learning.Proceedings of the 20th International Conference on Machine Learning (ICML '03)2003show
What's Strange About Recent Events

Weng-Keen Wong, Andrew Moore, Gregory Cooper, Michael Wagner

A shorter paper on WSARE that was submitted to the Journal of Urban Health2003show
Empirical Bayes Screening for Link Analysis

Anna Goldenberg, Andrew Moore

An algorithm for discovering top N strange co-occurences of size 2,3,4, etc Uses ideas of frequent sets, but stratifies them according to a statistically justified hierarchical bayes model, using empirical bayes to find the parametersWorkshop on Text Analysis and Link Detection, IJCAI2003show
Bayesian Network Anomaly Pattern Detection for Disease Outbreaks

Weng-Keen Wong, Andrew Moore, Gregory Cooper, Michael Wagner

Handles temporal trends in data by replacing the baseline of WSARE 2.0 with a baseline generated by a Bayesian network.Proceedings of the Twentieth International Conference on Machine Learning2003show
A Fast Multi-Resolution Method for Detection of Significant Spatial Overdensities

Daniel Neill, Andrew Moore

Scaling up the classical Kulldorff scan statistic.2003show
Efficient Exact k-NN and Nonparametric Classification in High Dimensions

Ting Liu, Andrew Moore, Alexander Gray

Can we do non-approximate k-NN classification without actually finding the k-NN?Proceedings of Neural Information Processing Systems2003show
Rapid Evaluation of Multiple Density Models

Alexander Gray, Andrew Moore

A way to quickly evaluate and compare multiple nonparametric density estimates.Artificial Iintelligence and Statistics2003show
A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters

Daniel Neill, Andrew Moore

Rapid detection of disease clusters using a fast spatial scan statistic algorithm.Advances in Neural Information Processing Systems 162003show
Variable Resolution Discretization in Optimal Control

Remi Munos, Andrew Moore

A short paper on choosing the right resolution in a tessalation of state space.2002show
Rule-based Anomaly Pattern Detection for Detecting Disease Outbreaks

Weng-Keen Wong, Andrew Moore, Gregory Cooper, Michael Wagner

Answering the question: "What's Strange About Recent Events?"Proceedings of the 18th National Conference on Artificial Intelligence2002show
Summary of Biosurveillance-relevant statistical and data mining technologies

Andrew Moore, Gregory Cooper, Rich Tsui, Michael Wagner

A brief informal survey of some techniques that have been used for Biosurveillance.2002show
Stochastic Link and Group Detection

Jeremy Kubica, Andrew Moore, Jeff Schneider, Yiming Yang

This paper introduces the GDA algorithm. We use noisy link data (n-tuples of entities) to learn underlying groupings of entities.Proceedings of the Eighteenth National Conference on Artificial Intelligence2002show
Efficient Algorithms for Non-Parametric Clustering with Clutter

Weng-Keen Wong, Andrew Moore

Finding and counting the high density regions in spatial data.Proceedings of the 34th Interface Symposium2002show
Interpolating Conditional Density Trees

Scott Davies, Andrew Moore

Very fast non-parametric Bayesian Network nodesConference on Uncertainty in Artificial Intelligence2002show
Using Tarjan's Red Rule for Fast Dependency Tree Construction

Dan Pelleg, Andrew Moore

Very fast growth of dependency trees.2002show
Real-valued All-Dimensions search: Low-overhead rapid searching over subsets of attributes

Andrew Moore, Jeff Schneider

Searching over large numbers of contingency tables quicklyProceedings of the 18th Conference on Uncertainty in Artificial Intelligence2002show
Active Learning in Discrete Input Spaces

Jeff Schneider, Andrew Moore

Using modified Gittins indices to decide which datapoint to actively label next whilst being rewarded for each new label.Proceedings of the 34th Interface Symposium2002show
Direct Policy Search using Paired Statistical Tests

Malcolm Strens, Andrew Moore

If you're going to choose the best policy by roll-outs, how can statistical tests and "racing" help?Proceedings of the 18th International Conference on Machine Learning2001show
Solving Uncertain Markov Decision Problems

Drew Bagnell, Andrew Ng, Jeff Schneider

Finding good policies in uncertain models2001show
Mixtures of Rectangles: Interpretable Soft Clustering

Dan Pelleg, Andrew Moore

A mixture model that is easily readable by humans.Proceedings of the 18th International Conference on Machine Learning2001show
Repairing Faulty Mixture Models using Density Estimation

Peter Sand, Andrew Moore

Intelligent automatic selection of new mixture model componentsInternational Conference on Machine Learning2001show
N-Body Problems in Statistical Learning

Alexander Gray, Andrew Moore

A way to use multiple trees simultaneously to solve a large class of statistical problems efficiently.Advances in Neural Information Processing Systems2001show
Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures

Yanxi Liu, Frank Dellaert, W Rothfus, Andrew Moore, Jeff Schneider, Takeo Kanade

Using machine learning to detect abnormalities in neuro-imaging output.Proceedings of the 2001 Medical Imaging Computing and Computer Assisted Intervention Conference (MICCAI '01)2001show
Autonomous Helicopter Control using Reinforcement Learning Policy Search Methods

Drew Bagnell, Jeff Schneider

We consider the difficult control problem of learing to fly an autonomous helicopter using limited observational data of its dynamics. To that end, we develop policy search techiques that perform well on average with respect to dynamic consistent with ourProceedings of the International Conference on Robotics and Automation2001show
Reinforcement Learning for Cooperating and Communicating Reactive Agents in Electrical Power Grids

Martin Riedmiller, Andrew Moore, Jeff Schneider

One approach to distributed reinforcement learningBalancing Reactivity and Social Deliberation in Multi-Agent Systems2001show
A Nonparametric Approach to Noisy and Costly Optimization

Brigham Anderson, Andrew Moore, David Cohn

Optimizing a noisy process in a possibly discontinuous or non-euclidian space.International Conference on Machine Learning2000show
Condensed Representations for computationally tractable data mining of massive sky surveys

Andrew Moore, Robert Nichol, Larry Wasserman, Andrew Connolly

2000show
A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets

Paul Komarek, Andrew Moore

Fast implementation of on-demand AD-trees for scores of high-arity attributes and millions of rows.Proceedings of the 17th International Conference on Machine Learning2000show
The Anchors Hierarchy: Using the Triangle Inequality to Survive High-Dimensional Data

Andrew Moore

Using Ball-trees allows cached sufficient statistics-based accelerations even in high dimensions. The Anchors approach quickly optimizes the Ball-tree structure for this purpose.Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence2000show
X-means: Extending K-means with Efficient Estimation of the Number of Clusters

Dan Pelleg, Andrew Moore

Extension to popular K-means, where the number of clusters K is also estimated.Proceedings of the Seventeenth International Conference on Machine Learning2000show
Learning Filaments

Geoff Gordon, Andrew Moore

A generative model and efficient algorithm for identifying noisy networks of points in k-dimensional spaceProceedings of the International Conference on Machine Learning2000show
Mixnets: Learning Bayesian Networks with mixtures of discrete and continuous attributes

Scott Davies, Andrew Moore

Bayes Nets with Mixture Models in the nodesProceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence2000show
Bayesian Networks for Lossless Dataset Compression

Scott Davies, Andrew Moore

Practical ways to compress large tabular datasetsProceedings of the Fifth International Conference on Knowledge Discovery in Databases1999show
Efficient Multi-Object Dynamic Query Histograms

Mark Derthick, James Harrison, Andrew Moore, Steven Roth

Using Multiresolution kdtrees to accelerate visualization algorithmsProceedings of the IEEE Symposium on Information Visualization1999show
Influence and Variance of a Markov Chain: Application to adaptive discretization in optimal control

Remi Munos, Andrew Moore

You're using variable resolution splines to approximate a value function. Where is most profitable to increasing the resolution?Conference on Decision and Control (CDC99)1999show
Variable Resolution discretizations for high-accuracy solutions of optimal control problems

Remi Munos, Andrew Moore

Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm1999show
Distributed Value Functions

Jeff Schneider, Weng-Keen Wong, Andrew Moore, Martin Riedmiller

Distributed Reinforcement learning for applications such a power gridsProceedings of the 16th International Conference on Machine Learning1999show
Accelerating Exact k-means Algorithms with Geometric Reasoning

Dan Pelleg, Andrew Moore

Using cached counts and a different kind of search operator during k-means updates, with no approximationProceedings of the Fifth International Conference on Knowledge Discovery in Databases1999show
Very Fast EM-based Mixture Model Clustering Using Multiresolution KD-trees

Andrew Moore

Using kdtrees with centroids in the nodes can allow accurate EM updates in time sublinear in the number of recordsAdvances in Neural Information Processing Systems1999show
Accelerating Exact k-means Algorithms with Geometric Reasoning (Extended version)

Dan Pelleg, Andrew Moore

This is an extended version of the KDD99 paper.1999show
Multi-Value-Functions: Efficient Automatic Action Hierarchies for Multiple Goal MDPs

Andrew Moore, Leemon Baird, Leslie Pack Kaelbling

An efficient procedure to approximately compute all policies for all possible goal states.Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm1999show
Applying online search techniques to Continuous-State reinforcement learning

Scott Davies, Andrew Ng, Andrew Moore

Using a specialized version of A-star to boost the performance of approximate value functionsProceedings of the Fifteenth National Conference on Artificial Intelligence1998show
AD-trees for Fast Counting and for Fast Learning of Association Rules

Brigham Anderson, Andrew Moore

Using AD-trees to learn conjunctive rules via beam search.Knowledge Discovery from Databases Conference1998show
Learning Evaluation Functions for Global Optimization and Boolean Satisfiability

Justin Boyan, Andrew Moore

Proceedings of the Fifteenth National Conference on Artificial Intelligence1998show
Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets

Andrew Moore, Mary Soon Lee

Introduces AD-trees: a new way to implicitly pre-cache the answers to all possible counting queries for a dataset.1998show
Q2: Memory-based active learning for optimizing noisy continuous functions

Andrew Moore, Jeff Schneider, Justin Boyan, Mary Soon Lee

Maximizing a very noisy function in k-dimensional space with few samplesProceedings of the Fifteenth International Conference of Machine Learning1998show
Value Function Based Production Scheduling

Jeff Schneider, Justin Boyan, Andrew Moore

Production scheduling in which we account for a probability distribution on future jobs by means of kernel-based value function approximationProceedings of the 15th International Conference on Machine Learning1998show
Simulation-based optimization of a stochastic product coating problem using hash-table cacheing of costs

Andrew Moore, Jeff Schneider

1997show
Multidimensional Triangulation and Interpolation for Reinforcement Learning

Scott Davies

Neural Information Processing Systems 9,1997show
A tutorial on using the Vizier memory-based learning system

Jeff Schneider, Mary Soon Lee, Andrew Moore

A tutorial on using the Windows Vizier software for fast locally weighted and k-NN style classification and regression.1997show
Locally Weighted Learning for Control

Chris Atkeson, Andrew Moore, Stefan Schaal

How can kernel methods and locally weighted regression help robots learn to control themselves?1997show
Using Prediction to Improve Combinatorial Optimization Search

Justin Boyan, Andrew Moore

Automatically improving combinatorial search by reinforcement-learning-style analysis of earlier runsAmerican Association for Artificial Intelligence C1997show
Exploiting Model Uncertainty Estimates for Safe Dynamic Control Learning

Jeff Schneider

Advances in Neural Information Processing Systems 9,1997show
Locally Weighted Learning

Chris Atkeson, Andrew Moore, Stefan Schaal

Survey of the use of kernel functions in kernel regression, locally weighted regression and related function approximators.1997show
Efficient Locally Weighted Polynomial Regression Predictions

Andrew Moore, Jeff Schneider, Kan Deng

Using Multiresolution KD-trees with cached first and second moments in the nodesProceedings of the Fourteenth International Conference on Machine Learning1997show
Memory based Stochastic Optimization for Validation and Tuning of Function Approximators

Artur Dubrawski, Jeff Schneider

Conference on AI and Statistics1997show
The Racing Algorithm: Model Selection for Lazy Learners

Oded Maron, Andrew Moore

A detailed analysis and study of Racing.Artificial Intelligence Review1997show
Algorithms for Approximating Optimal Value Functions in Acyclic Domains

Justin Boyan, Andrew Moore

Using "Rollouts" to make value-function-based RL more practicalMachine Learning: Proceedings of the Thirteenth International Conference1996show
Reinforcement Learning: A Survey

Leslie Pack Kaelbling, Michael Littman, Andrew Moore

Surveys MDPs, TD, Q-learning and many other Reinforcement Learning staples.1996show
Memory-based Stochastic Optimization

Andrew Moore, Jeff Schneider

Using locally weighted regression to model response surfaces and to choose the next experimentNeural Information Processing Systems 81996show
Proceedings of the Workshop on Value Function Approximation, Machine Learning Conference 1995.

Justin Boyan, Andrew Moore, Richard Sutton

Short talks from a workshop about value function approximation1995show
Variable Resolution Reinforcement Learning

Andrew Moore

Briefly surveys a number of approaches to making Bellman updates fasterProceedings of the Eighth Yale Workshop on Adaptiv1995show
An Empirical Investigation of Brute Force to choose Features, Smoothers and Function Approximators

Andrew Moore, Daniel Hill, Michael Johnson

What happens when you use very intense cross-validation?Computational Learning Theory and Natural Learning1995show
Generalization in Reinforcement Learning: Safely Approximating the Value Function

Justin Boyan, Andrew Moore

An introduction to the ways that naive application of function approximation of value functions can fail.Neural Information Processing Systems 71995show
Learning Automated Product Recommendations Without Observable Features: An Initial Investigation

Mary Soon Lee, Andrew Moore

1995show
The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces

Andrew Moore, Chris Atkeson

Automatic variable resolution discretization of a multidimensional state space during searches for shortest paths1995show
Multiresolution Instance-based Learning

Kan Deng, Andrew Moore

Multiresolution Kd-trees with cached statistics for accelerating kernel regressionProceedings of the Twelfth International Joint Conference on Artificial Intellingence1995show
Task-level Training Signals for Learning Controllers

Jeff Schneider

A new learning algorithm and an example on the inverted pendulum taskProceedings of the IEEE Symposium on Intelligent Control1994show
A short tutorial note on computing information gain from counts

Andrew Moore

A simple 2-page tutorial.1994show
Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation

Oded Maron, Andrew Moore

Perform many cross-validation operations in a round-robin fashion, pruning likely non-winners early.Advances in Neural Information Processing Systems1994show
Efficient Algorithms for Minimizing Cross Validation Error

Andrew Moore, Mary Soon Lee

Proceedings of the 11th International Confonference on Machine Learning1994show
High Dimension Action Spaces in Robot Skill Learning

Jeff Schneider

Proceedings of the Twelfth National Conference on Artificial Intelligence1994show
The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces

Andrew Moore, Chris Atkeson

A short introduction to an efficient learning-shortest-paths algorithm.Advances in Neural Information Processing Systems1994show
Prioritized Sweeping: Reinforcement Learning with Less Data and Less Real Time

Andrew Moore, Chris Atkeson

As estimates of rewards and transition probabilites improve during learning, how can we efficiently allow the value function to keep up?1993show
Memory-based Reinforcement Learning: Efficient Computation with Prioritized Sweeping

Andrew Moore, Chris Atkeson

Using a priority queue to schedule the most useful value function updatesAdvances in Neural Information Processing Systems1992show
Fast, Robust Adaptive Control by Learning only Forward Models

Andrew Moore

A real robot pool player achieves high accuracy by learning in a forward direction.Advances in Neural Information Processing Systems1992show
Knowledge of Knowledge and Intelligent Experimentation for Learning Control

Andrew Moore

Proceedings of the 1991 Seattle International Joint Conference on Neural Networks1991show
Variable Resolution Dynamic Programming: Efficiently Learning Action Maps in Multivariate Real-valued State-spaces

Andrew Moore

Machine Learning: Proceedings of the Eighth International Conference1991show
A tutorial on kd-trees

Andrew Moore

A description of Bentley et al's classic nearest neighbor algorithmUniversity of Cambridge Computer Laboratory Technical Report No. 2091991show
Acquisition of Dynamic Control Knowledge for a Robotic Manipulator

Andrew Moore

Proceedings of the 7th International Conference on Machine Learning1990show
Efficient Memory-based Learning for Robot Control

Andrew Moore

Using KD-trees, nearest neighbor and active learning.1990show
Experiments in Adaptive State Space Robotics

William Clocksin, Andrew Moore

Using a nearest neighbor classifier to design control experimentsProceedings of the 7th AISB Conference, Brighton1989show
Learning Robotic Control: PhD. Thesis Proposal

Andrew Moore

Thesis Proposal1988show
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