Jonathan Baxter

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Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning

Peter L. Bartlett, Jonathan Baxter, Evan Greensmith, 2001, J. Mach. Learn. Res..

Infinite-Horizon Policy-Gradient Estimation

Peter L. Bartlett, Jonathan Baxter, P. Bartlett, 2001, J. Artif. Intell. Res..

Stochastic optimization of controlled partially observable Markov decision processes

P. Bartlett, Jonathan Baxter, 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

Direct Optimization of Margins Improves Generalization in Combined Classifiers

Peter L. Bartlett, Llew Mason, Jonathan Baxter, 1998, NIPS.

Experiments with Infinite-Horizon, Policy-Gradient Estimation

Peter L. Bartlett, Lex Weaver, Jonathan Baxter, 2001, J. Artif. Intell. Res..

Reinforcement Learning From State and Temporal Differences

Jonathan Baxter, Lex Weaver, 1999 .

Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning

Peter L. Bartlett, Jonathan Baxter, P. Bartlett, 2000, J. Comput. Syst. Sci..

Reinforcement Learning in POMDP's via Direct Gradient Ascent

Peter L. Bartlett, Jonathan Baxter, P. Bartlett, 2000, ICML.

A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling

Jonathan Baxter, Jonathan Baxter, 1997, Machine Learning.

The evolution of learning algorithms for artificial neural networks

Jonathan Baxter, 1993 .

Boosting Algorithms as Gradient Descent

Peter L. Bartlett, Marcus R. Frean, Llew Mason, 1999, NIPS.

A Result Relating Convex n-Widths to Covering Numbers with some Applications to Neural Networks

Peter L. Bartlett, Jonathan Baxter, P. Bartlett, 1997, EuroCOLT.

Learning Model Bias pdf

Jonathan Baxter, Jonathan Baxter, 1995, NIPS.

KnightCap: A chess program that learns by combining TD( ) with minimax search

Andrew Tridgell, Lex Weaver, Jonathan Baxter, 1997, ICML 1997.

Learning internal representations pdf

Jonathan Baxter, Jonathan Baxter, 1995, COLT '95.

A Multi-Agent Policy-Gradient Approach to Network Routing

Lex Weaver, Jonathan Baxter, Nigel Tao, 2001, ICML.

Scaling Internal-State Policy-Gradient Methods for POMDPs

Jonathan Baxter, D. Aberdeen, Douglas Aberdeen, 2002 .

Reinforcement learning and chess

Andrew Tridgell, Lex Weaver, Jonathan Baxter, 2001 .

Experiments in Parameter Learning Using Temporal Differences

Andrew Tridgell, Lex Weaver, Jonathan Baxter, 1998, J. Int. Comput. Games Assoc..

A Model of Inductive Bias Learning

Jonathan Baxter, Jonathan Baxter, 2000, J. Artif. Intell. Res..

Learning to Play Chess Using Temporal Differences

Andrew Tridgell, Lex Weaver, Jonathan Baxter, 2000, Machine Learning.

Theoretical Models of Learning to Learn pdf

Jonathan Baxter, Jonathan Baxter, 1998, Learning to Learn.

The Canonical Distortion Measure for Vector Quantization and Function Approximation

Jonathan Baxter, Jonathan Baxter, 1997, ICML.

Scalable Internal-State Policy-Gradient Methods for POMDPs

Douglas Aberdeen, Jonathan Baxter, Jonathan Baxter, 2002, ICML.

Boosting Algorithms as Gradient Descent in Function Space

Peter L. Bartlett, Marcus Frean, Llew Mason, 2007 .

General Matrix-Matrix Multiplication Using SIMD Features of the PIII (Research Note) pdf

Douglas Aberdeen, Jonathan Baxter, Jonathan Baxter, 2000, Euro-Par.

Improved Generalization Through Explicit Optimization of Margins

Peter L. Bartlett, Llew Mason, Jonathan Baxter, 2000, Machine Learning.

TDLeaf(lambda): Combining Temporal Difference Learning with Game-Tree Search

Andrew Tridgell, Lex Weaver, Jonathan Baxter, 1999, ArXiv.

A Bayesian/information theoretic model of bias learning pdf

Jonathan Baxter, Jonathan Baxter, 2019, COLT '96.

The Canonical Distortion Measure in Feature Space and 1-NN Classification

Peter L. Bartlett, Jonathan Baxter, P. Bartlett, 1997, NIPS.

The Canonical Metric For Vector Quantization

Jonathan Baxter, 1995 .

Emmerald: a fast matrix–matrix multiply using Intel's SSE instructions

Douglas Aberdeen, Jonathan Baxter, Jonathan Baxter, 2001, Concurr. Comput. Pract. Exp..

Boosting Algorithms as Gradient Descent in Function

P. Bartlett, Jonathan Baxter, Marcus Frean, 1999 .

Hebbian Synaptic Modifications in Spiking Neurons that Learn pdf

Peter L. Bartlett, Jonathan Baxter, P. Bartlett, 2019, ArXiv.

Learning Internal Representations (PhD Thesis) pdf

Jonathan Baxter, Jonathan Baxter, 2019, ArXiv.

Functional Gradient Techniques for Combining Hypotheses

Peter L. Bartlett, Marcus Frean, Llew Mason, 2000 .

TDLeaf ( ) : Combining Temporal Difference Learning with Game-Tree Search

Jonathan Baxter, Lex Weaver, A. Tridgell, 1998 .

Learning From State Differences: STD( )

Lex Weaver, Jonathan Baxter, Jonathan Baxter, 1999 .

KnightCap : A chess program that learns by combining TD ( ) with game-tree search

Jonathan Baxter, 1998 .

Direct Gradient-Based Reinforcement Learning: II. Gradient Ascent Algorithms and Experiments

P. Bartlett, Jonathan Baxter, Lex Weaver, 1999 .

Direct Gradient-Based Reinforcement Learning: I. Gradient Estimation Algorithms

P. Bartlett, Jonathan Baxter, 1999 .

Direct gradient-based reinforcement learning

J. Baxter, P. L. Bartlett, P. Bartlett, 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

Learning Internal Representations (PhD Thesis)

Jonathan Baxter, 2019, ArXiv.

KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree Search

Andrew Tridgell, Lex Weaver, Jonathan Baxter, 1998, ICML.

92¢ /MFlops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster

Douglas Aberdeen, Robert Edwards, Jonathan Baxter, 2000, ACM/IEEE SC 2000 Conference (SC'00).

Reinforcement Learning From State and Temporal Differences

Jonathan Baxter, Lex Weaver, 1999 .