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Active Learning in Discrete Input Spaces

Document Type: Paper
Tags: AD-trees, Cached Sufficient Statistics, Efficient Statistical Algorithms, Optimization, Association Rules, Active Learning

Traditional design of experiments (DOE) from the statistics literature focuses on optimizing an output parameter over a space of continuous input parameters. Here we consider DOE, or active learning, for descrete input spaces. A trivial example of this is the k-armed bandit problem, which is the...

Alexander Gray

Document Type: Person
Tags: Auton Fast Classifiers, Statistical Data Mining for Astrophysics, K Nearest Neighbor, Astrostatistics, Cached Sufficient Statistics, Clustering, Memory-based Learning, Efficient Statistical Algorithms, Life Science Data Mining, Locally Weighted Learning, Kernel Density Estimation, Bayesian Networks, Kd-trees and Ball-trees, Mixture Models, Optimization

Alex's fascinations in early grade school were Legos, breaking ciphers, and drawing human anatomy. After studying Applied Math and Computer Science at Berkeley, he resisted a job offer to do Hollywood special effects and ended up working at NASA's Jet Propulsion Laboratory for six years developi...

Algorithms for Approximating Optimal Value Functions in Acyclic Domains

Document Type: Paper
Tags: Markov Decision Processes, Optimization, Reinforcement Learning

Some of the most successful recent applications of reinforcement learning have used neural networks and the TD() algorithm to learn evaluation functions. In this paper, we examine the intuition that TD() operates by approximating asynchronous value iteration. We note that on the important subcla...

Andrew Moore

Document Type: Person
Tags: Link Analysis, Auton Fast Classifiers, Statistical Data Mining for Astrophysics, Cached Sufficient Statistics, Efficient Statistical Algorithms, Spatial Statistics, Life Science Data Mining, Logistic Regression, Locally Weighted Learning, GDA, AD-trees, Bayesian Networks, Kernel Density Estimation, Kd-trees and Ball-trees, Mixture Models, WSARE, Reinforcement Learning, Active Learning, Markov Decision Processes, K Nearest Neighbor, Astrostatistics, Clustering, Memory-based Learning, Biosurveillance, Applications, Optimization, Association Rules

Andrew began his career writing video-games for an obscure British personal computer. He rapidly became a thousandaire and retired to academia, where he received a PhD from the University of Cambridge in 1991. He researched robot learning as a Post-doc working with Chris Atkeson, and then moved ...

A Nonparametric Approach to Noisy and Costly Optimization

Document Type: Paper
Tags: Memory-based Learning, Optimization, Active Learning

This paper describes Pairwise Bisection: a nonparametric approach to optimizing a noisy function with few function evaluations. The algorithm uses nonparametric resoning about simple geometric relationships to find minima efficiently. Two factors often frustrate optimization: noise and cost. Out...

Artur Dubrawski

Document Type: Person
Tags: GDA, Biosurveillance, Memory-based Learning, Mixture Models, Applications, Optimization, Association Rules, Locally Weighted Learning, Active Learning, Food Safety, Link Analysis, Social Networks, Dynamic Social Networks, Health of Equipment, Nuclear Safety

Artur Dubrawski considers himself a scientist and a practitioner. He has been tainted with real world entrepreneurial experiences. He had started up a successful company specializing in integration and deployment of advanced control systems and technological devices. He had also been affiliated ...

Brigham Anderson

Document Type: Person
Tags: AD-trees, Statistical Data Mining for Astrophysics, Astrostatistics, Efficient Statistical Algorithms, Memory-based Learning, Applications, Optimization, Association Rules, Reinforcement Learning, Locally Weighted Learning, Active Learning

Covariant Policy Search

Document Type: Paper
Tags: Markov Decision Processes, Optimization, Reinforcement Learning

Abstract We investigate the problem of non-covariant behavior of policy gradient reinforcement learning algorithms. The policy gradient approach is amenable to analysis by information geometric methods. This leads us to propose a natural metric on controller parameterization that results from co...

Direct Policy Search using Paired Statistical Tests

Document Type: Paper
Tags: Optimization, Reinforcement Learning

Direct policy search is a practical way to solve reinforcement learning problems involving continuous state and action spaces. The goal becomes finding policy parameters that maximize a noisy objective function. The Pegasus method converts this stochastic optimization problem into a deterministi...

Distributed Value Functions

Document Type: Paper
Tags: Markov Decision Processes, Optimization, Reinforcement Learning

Many interesting problems, such as power grids, network switches, and traffic flow, that are candidates for solving with reinforcement learning (RL), also have properties that make distributed solutions desirable. We propose an algorithm for distributed reinforcement learning based on distributi...