Jonathan Baxter - researchr alias

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Viewing Publication 1 - 35 from 35

2025

Analysis and Experimental Validation of a Low-Complexity Enhanced Orientation-Based Controller for Tethered Energy-Harvesting SystemsJacob B. Fine, Peter Newell, Kavin Govindarajan, Carson M. McGuire, Paul Paris, Gabriel Matthias, Mary Maceda, Jonathan Baxter, Kenneth Granlund, Matthew Bryant, Chris Vermillion. tcst, 33(5):1743-1756, September 2025. [doi]

2009

2008

ArtLinks: fostering social awareness and reflection in museumsDan Cosley, Joel Lewenstein, Andrew Herman, Jenna Holloway, Jonathan Baxter, Saeko Nomura, Kirsten Boehner, Geri Gay. CHI 2008: 403-412 [doi]

2004

Variance Reduction Techniques for Gradient Estimates in Reinforcement LearningEvan Greensmith, Peter L. Bartlett, Jonathan Baxter. jmlr, 5:1471-1530, 2004. [doi]

2002

Scalable Internal-State Policy-Gradient Methods for POMDPsDouglas Aberdeen, Jonathan Baxter. icml 2002: 3-10

Estimation and Approximation Bounds for Gradient-Based Reinforcement LearningPeter L. Bartlett, Jonathan Baxter. jcss, 64(1):133-150, 2002. [doi]

2001

2000

98¢/Mflops/s, Ultra-Large-Scale Neural-Network Training on a PIII ClusterDouglas Aberdeen, Jonathan Baxter, Robert Edwards. sc 2000: 44 [doi]

General Matrix-Matrix Multiplication Using SIMD Features of the PIII (Research Note)Douglas Aberdeen, Jonathan Baxter. europar 2000: 980-983 [doi]

Stochastic optimization of controlled partially observable Markov decision processesPeter L. Bartlett, Jonathan Baxter. cdc 2000: 124-129 [doi]

Estimation and Approximation Bounds for Gradient-Based Reinforcement LearningPeter L. Bartlett, Jonathan Baxter. colt 2000: 133-141

Reinforcement Learning in POMDP s via Direct Gradient AscentJonathan Baxter, Peter L. Bartlett. icml 2000: 41-48

Learning to Play Chess Using Temporal DifferencesJonathan Baxter, Andrew Tridgell, Lex Weaver. ml, 40(3):243-263, 2000.

A Model of Inductive Bias LearningJonathan Baxter. jair, 12:149-198, 2000. [doi]

Improved Generalization Through Explicit Optimization of MarginsLlew Mason, Peter L. Bartlett, Jonathan Baxter. ml, 38(3):243-255, 2000.

1999

Guest Editors IntroductionJonathan Baxter, Nicolò Cesa-Bianchi. ml, 37(3):239-240, 1999.

Boosting Algorithms as Gradient DescentLlew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean. nips 2000: 512-518 [doi]

1998

KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree SearchJonathan Baxter, Andrew Tridgell, Lex Weaver. icml 1998: 28-36

Experiments in Parameter Learning Using Temporal DifferencesJonathan Baxter, Andrew Tridgell, Lex Weaver. icga, 21(2):84-99, 1998. [doi]

The Canonical Distortion Measure for Vector Quantization and Function ApproximationJonathan Baxter. In Sebastian Thrun, Lorien Y. Pratt, editors, Learning to Learn. pages 159-179, Springer, 1998. [doi]

Theoretical Models of Learning to LearnJonathan Baxter. In Sebastian Thrun, Lorien Y. Pratt, editors, Learning to Learn. pages 71-94, Springer, 1998. [doi]

Direct Optimization of Margins Improves Generalization in Combined ClassifiersLlew Mason, Peter L. Bartlett, Jonathan Baxter. nips 1999: 288-294 [doi]

1997

The Canonical Distortion Measure in Feature Space and 1-NN ClassificationJonathan Baxter, Peter L. Bartlett. nips 1998: 245-251

A Result Relating Convex ::::n::::-Widths to Covering Numbers with some Applications to Neural NetworksJonathan Baxter, Peter L. Bartlett. eurocolt 1997: 251-259

The Canonical Distortion Measure for Vector Quantization and Function ApproximationJonathan Baxter. icml 1997: 39-47

A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task SamplingJonathan Baxter. ml, 28(1):7-39, 1997.

1996

A Bayesian/Information Theoretic Model of Bias LearningJonathan Baxter. colt 1996: 77-88 [doi]

Learning to Compress Ergodic SourcesJonathan Baxter, John Shawe-Taylor. dcc 1996: 423

1995

Learning Model BiasJonathan Baxter. nips 1996: 169-175 [doi]

Learning Internal RepresentationsJonathan Baxter. colt 1995: 311-320 [doi]

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