Jonathan Baxter

发表

Reinforcement learning and chess

Andrew Tridgell, Lex Weaver, Jonathan Baxter, 2001 .

Guest Editors' Introduction

Nicolò Cesa-Bianchi, Jonathan Baxter, Nicolò Cesa-Bianchi, 2004, Machine Learning.

ArtLinks: fostering social awareness and reflection in museums

Dan Cosley, Geri Gay, Joel Lewenstein, 2008, CHI.

A tag in the hand: supporting semantic, social, and spatial navigation in museums

Dan Cosley, Geri Gay, Saeko Nomura, 2009, CHI.

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

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

Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning

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

Boosting Algorithms as Gradient Descent

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

Theoretical Models of Learning to Learn pdf

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

Learning to compress ergodic sources

John Shawe-Taylor, Jonathan Baxter, 1996, Proceedings of Data Compression Conference - DCC '96.

Experiments in Parameter Learning Using Temporal Differences

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

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

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

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

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

Scalable Internal-State Policy-Gradient Methods for POMDPs

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

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

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

STD(λ): learning state differences with TD(λ)

Lex Weaver, Jonathan Baxter, 2001 .

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).

Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning

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

Learning Model Bias pdf

Jonathan Baxter, Jonathan Baxter, 1995, NIPS.

The Canonical Distortion Measure for Vector Quantization and Function Approximation

Jonathan Baxter, Jonathan Baxter, 1997, ICML.

Learning to Play Chess Using Temporal Differences

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

Reinforcement Learning in POMDP's via Direct Gradient Ascent

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

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

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

Using technologies to support reminiscence

Dan Cosley, Soyoung Lee, Jonathan Baxter, 2009, BCS HCI.

Improved Generalization Through Explicit Optimization of Margins

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

A Bayesian/information theoretic model of bias learning pdf

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

Direct Optimization of Margins Improves Generalization in Combined Classifiers

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

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

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

Learning internal representations pdf

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

Boosting Algorithms as Gradient Descent in Function Space

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

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

Jonathan Baxter, Jonathan Baxter, 1997, Machine Learning.

A Model of Inductive Bias Learning

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

A Multi-Agent Policy-Gradient Approach to Network Routing

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

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

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

Learning From State Differences: STD( )

Lex Weaver, Jonathan Baxter, Jonathan Baxter, 1999 .