Learning Internal Representations. - Jonathan Baxter (COLT, 1995)

Learning Model Bias. - Jonathan Baxter (NIPS, 1995)

A Bayesian/Information Theoretic Model of Bias Learning. - Jonathan Baxter (COLT, 1996)

Learning to Compress Ergodic Sources. - Jonathan Baxter; John Shawe-Taylor (Data Compression Conference, 1996)

A Result Relating Convex n-Widths to Covering Numbers with some Applications to Neural Networks. - Jonathan Baxter; Peter L. Bartlett (EuroCOLT, 1997)

The Canonical Distortion Measure for Vector Quantization and Function Approximation. - Jonathan Baxter (ICML, 1997)

The Canonical Distortion Measure in Feature Space and 1-NN Classification. - Jonathan Baxter; Peter L. Bartlett (NIPS, 1997)

A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling. - Jonathan Baxter (Mach. Learn., 1997)

Theoretical Models of Learning to Learn. - Jonathan Baxter (Learning to Learn, 1998)

The Canonical Distortion Measure for Vector Quantization and Function Approximation. - Jonathan Baxter (Learning to Learn, 1998)

KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree Search. - Jonathan Baxter; Andrew Tridgell; Lex Weaver (ICML, 1998)

Direct Optimization of Margins Improves Generalization in Combined Classifiers. - Llew Mason; Peter L. Bartlett; Jonathan Baxter (NIPS, 1998)

Experiments in Parameter Learning Using Temporal Differences. - Jonathan Baxter; Andrew Tridgell; Lex Weaver (J. Int. Comput. Games Assoc., 1998)

Boosting Algorithms as Gradient Descent. - Llew Mason; Jonathan Baxter; Peter L. Bartlett; Marcus R. Frean (NIPS, 1999)

TDLeaf(lambda): Combining Temporal Difference Learning with Game-Tree Search - Jonathan Baxter; Andrew Tridgell; Lex Weaver (CoRR, 1999)

KnightCap: A chess program that learns by combining TD(lambda) with game-tree search - Jonathan Baxter; Andrew Tridgell; Lex Weaver (CoRR, 1999)

Guest Editors' Introduction. - Jonathan Baxter; Nicolò Cesa-Bianchi (Mach. Learn., 1999)

Stochastic optimization of controlled partially observable Markov decision processes. - Peter L. Bartlett; Jonathan Baxter (CDC, 2000)

General Matrix-Matrix Multiplication Using SIMD Features of the PIII (Research Note). - Douglas Aberdeen; Jonathan Baxter (Euro-Par, 2000)

Reinforcement Learning in POMDP's via Direct Gradient Ascent. - Jonathan Baxter; Peter L. Bartlett (ICML, 2000)

Direct gradient-based reinforcement learning. - Jonathan Baxter; Peter L. Bartlett (ISCAS, 2000)

98¢/Mflops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster. - Douglas Aberdeen; Jonathan Baxter; Robert Edwards (SC, 2000)

A Model of Inductive Bias Learning. - Jonathan Baxter (J. Artif. Intell. Res., 2000)

Learning to Play Chess Using Temporal Differences. - Jonathan Baxter; Andrew Tridgell; Lex Weaver (Mach. Learn., 2000)

Improved Generalization Through Explicit Optimization of Margins. - Llew Mason; Peter L. Bartlett; Jonathan Baxter (Mach. Learn., 2000)

A Multi-Agent Policy-Gradient Approach to Network Routing. - Nigel Tao; Jonathan Baxter; Lex Weaver (ICML, 2001)

Emmerald: a fast matrix-matrix multiply using Intel's SSE instructions. - Douglas Aberdeen; Jonathan Baxter (Concurr. Comput. Pract. Exp., 2001)

Infinite-Horizon Policy-Gradient Estimation. - Jonathan Baxter; Peter L. Bartlett (J. Artif. Intell. Res., 2001)

Experiments with Infinite-Horizon, Policy-Gradient Estimation. - Jonathan Baxter; Peter L. Bartlett; Lex Weaver (J. Artif. Intell. Res., 2001)

Scalable Internal-State Policy-Gradient Methods for POMDPs. - Douglas Aberdeen; Jonathan Baxter (ICML, 2002)

Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. - Peter L. Bartlett; Jonathan Baxter (J. Comput. Syst. Sci., 2002)

Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. - Evan Greensmith; Peter L. Bartlett; Jonathan Baxter (J. Mach. Learn. Res., 2004)

ArtLinks: fostering social awareness and reflection in museums. - Dan Cosley; Joel Lewenstein; Andrew Herman; Jenna Holloway; Jonathan Baxter; Saeko Nomura; Kirsten Boehner; Geri Gay (CHI, 2008)

Using technologies to support reminiscence. - Dan Cosley; Kathy Akey; Brian Alson; Jonathan Baxter; Mark Broomfield; Soyoung Lee; Chethan Sarabu (BCS HCI, 2009)

A tag in the hand: supporting semantic, social, and spatial navigation in museums. - Dan Cosley; Jonathan Baxter; Soyoung Lee; Brian Alson; Saeko Nomura; Phil Adams; Chethan Sarabu; Geri Gay (CHI, 2009)

Learning Internal Representations (PhD Thesis). - Jonathan Baxter (CoRR, 2019)

92c/MFlops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster. - Douglas Aberdeen; Jonathan Baxter; Robert Edwards (CoRR, 2019)

Hebbian Synaptic Modifications in Spiking Neurons that Learn. - Peter L. Bartlett; Jonathan Baxter (CoRR, 2019)

General Matrix-Matrix Multiplication Using SIMD features of the PIII. - Douglas Aberdeen; Jonathan Baxter (CoRR, 2019)

Some observations concerning Off Training Set (OTS) error. - Jonathan Baxter (CoRR, 2019)

Theoretical Models of Learning to Learn. - Jonathan Baxter (CoRR, 2020)

Analysis and Experimental Validation of a Low-Complexity Enhanced Orientation-Based Controller for Tethered Energy-Harvesting Systems. - Jacob B. Fine; Peter Newell; Kavin Govindarajan; Carson M. McGuire; Paul Paris; Gabriel Matthias; Mary Maceda; Jonathan Baxter; Kenneth Granlund; Matthew Bryant; Chris Vermillion (IEEE Trans. Control. Syst. Technol., 2025)