Jonathan Baxter

57 papers A* 10A 1B 1C 1Journal 33Unranked 9

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Multi-Agent, Policy-Gradient approach to Network Routing. CoRR Nigel Tao, Jonathan Baxter, Lex Weaver
2025	J	`jnl`	A result relating convex n-widths to covering numbers with some applications to neural networks. CoRR Jonathan Baxter, Peter L. Bartlett
2025	J	`jnl`	Analysis and Experimental Validation of a Low-Complexity Enhanced Orientation-Based Controller for Tethered Energy-Harvesting Systems. IEEE Trans. Control. Syst. Technol. Jacob B. Fine, Peter Newell, Kavin Govindarajan, Carson M. McGuire, Paul Paris, Gabriel Matthias, Mary Maceda, Jonathan Baxter, Kenneth Granlund, Matthew Bryant, Chris Vermillion
2025	J	`jnl`	Reinforcement Learning From State and Temporal Differences. CoRR Lex Weaver, Jonathan Baxter
2025	J	`jnl`	Reinforcement Learning in POMDP's via Direct Gradient Ascent. CoRR Jonathan Baxter, Peter L. Bartlett
2025	J	`jnl`	Scaling Internal-State Policy-Gradient Methods for POMDPs. CoRR Douglas Aberdeen, Jonathan Baxter
2025	J	`jnl`	The Evolution of Learning Algorithms for Artificial Neural Networks. CoRR Jonathan Baxter
2020	J	`jnl`	Theoretical Models of Learning to Learn. CoRR Jonathan Baxter
2019	J	`jnl`	92c/MFlops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster. CoRR Douglas Aberdeen, Jonathan Baxter, Robert Edwards
2019	J	`jnl`	A Bayesian/Information Theoretic Model of Bias Learning. CoRR Jonathan Baxter
2019	J	`jnl`	General Matrix-Matrix Multiplication Using SIMD features of the PIII. CoRR Douglas Aberdeen, Jonathan Baxter
2019	J	`jnl`	Hebbian Synaptic Modifications in Spiking Neurons that Learn. CoRR Peter L. Bartlett, Jonathan Baxter
2019	J	`jnl`	Learning Internal Representations (PhD Thesis). CoRR Jonathan Baxter
2019	J	`jnl`	Learning Model Bias. CoRR Jonathan Baxter
2019	J	`jnl`	Learning internal representations. CoRR Jonathan Baxter
2019	J	`jnl`	Some observations concerning Off Training Set (OTS) error. CoRR Jonathan Baxter
2019	J	`jnl`	The Canonical Distortion Measure for Vector Quantization and Function Approximation. CoRR Jonathan Baxter
2011	J	`jnl`	A Model of Inductive Bias Learning CoRR Jonathan Baxter
2011	J	`jnl`	Experiments with Infinite-Horizon, Policy-Gradient Estimation CoRR Peter L. Bartlett, Jonathan Baxter, Lex Weaver
2011	J	`jnl`	Infinite-Horizon Policy-Gradient Estimation CoRR Peter L. Bartlett, Jonathan Baxter
2009	A*	`conf`	A tag in the hand: supporting semantic, social, and spatial navigation in museums. CHI Dan Cosley, Jonathan Baxter, Soyoung Lee, Brian Alson, Saeko Nomura, Phil Adams, Chethan Sarabu, Geri Gay
2009	—	`conf`	Using technologies to support reminiscence. BCS HCI Dan Cosley, Kathy Akey, Brian Alson, Jonathan Baxter, Mark Broomfield, Soyoung Lee, Chethan Sarabu
2008	A*	`conf`	ArtLinks: fostering social awareness and reflection in museums. CHI Dan Cosley, Joel Lewenstein, Andrew Herman, Jenna Holloway, Jonathan Baxter, Saeko Nomura, Kirsten Boehner, Geri Gay
2004	J	`jnl`	Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. J. Mach. Learn. Res. Evan Greensmith, Peter L. Bartlett, Jonathan Baxter
2002	J	`jnl`	Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. J. Comput. Syst. Sci. Peter L. Bartlett, Jonathan Baxter
2002	A*	`conf`	Scalable Internal-State Policy-Gradient Methods for POMDPs. ICML Douglas Aberdeen, Jonathan Baxter
2001	A*	`conf`	A Multi-Agent Policy-Gradient Approach to Network Routing. ICML Nigel Tao, Jonathan Baxter, Lex Weaver
2001	J	`jnl`	Emmerald: a fast matrix-matrix multiply using Intel's SSE instructions. Concurr. Comput. Pract. Exp. Douglas Aberdeen, Jonathan Baxter
2001	J	`jnl`	Experiments with Infinite-Horizon, Policy-Gradient Estimation. J. Artif. Intell. Res. Jonathan Baxter, Peter L. Bartlett, Lex Weaver
2001	J	`jnl`	Infinite-Horizon Policy-Gradient Estimation. J. Artif. Intell. Res. Jonathan Baxter, Peter L. Bartlett
2001	—	`conf`	Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. NIPS Evan Greensmith, Peter L. Bartlett, Jonathan Baxter
2000	A	`conf`	98¢/Mflops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster. SC Douglas Aberdeen, Jonathan Baxter, Robert Edwards
2000	J	`jnl`	A Model of Inductive Bias Learning. J. Artif. Intell. Res. Jonathan Baxter
2000	C	`conf`	Direct gradient-based reinforcement learning. ISCAS Jonathan Baxter, Peter L. Bartlett
2000	A*	`conf`	Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. COLT Peter L. Bartlett, Jonathan Baxter
2000	—	`conf`	General Matrix-Matrix Multiplication Using SIMD Features of the PIII (Research Note). Euro-Par Douglas Aberdeen, Jonathan Baxter
2000	J	`jnl`	Improved Generalization Through Explicit Optimization of Margins. Mach. Learn. Llew Mason, Peter L. Bartlett, Jonathan Baxter
2000	J	`jnl`	Learning to Play Chess Using Temporal Differences. Mach. Learn. Jonathan Baxter, Andrew Tridgell, Lex Weaver
2000	A*	`conf`	Reinforcement Learning in POMDP's via Direct Gradient Ascent. ICML Jonathan Baxter, Peter L. Bartlett
2000	—	`conf`	Stochastic optimization of controlled partially observable Markov decision processes. CDC Peter L. Bartlett, Jonathan Baxter
1999	—	`conf`	Boosting Algorithms as Gradient Descent. NIPS Llew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean
1999	J	`jnl`	Guest Editors' Introduction. Mach. Learn. Jonathan Baxter, Nicolò Cesa-Bianchi
1999	J	`jnl`	KnightCap: A chess program that learns by combining TD(lambda) with game-tree search CoRR Jonathan Baxter, Andrew Tridgell, Lex Weaver
1999	J	`jnl`	TDLeaf(lambda): Combining Temporal Difference Learning with Game-Tree Search CoRR Jonathan Baxter, Andrew Tridgell, Lex Weaver
1998	—	`conf`	Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS Llew Mason, Peter L. Bartlett, Jonathan Baxter
1998	J	`jnl`	Experiments in Parameter Learning Using Temporal Differences. ICCA J. Jonathan Baxter, Andrew Tridgell, Lex Weaver
1998	A*	`conf`	KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree Search. ICML Jonathan Baxter, Andrew Tridgell, Lex Weaver
1998	—	`ch.`	The Canonical Distortion Measure for Vector Quantization and Function Approximation. Learning to Learn Jonathan Baxter
1998	—	`ch.`	Theoretical Models of Learning to Learn. Learning to Learn Jonathan Baxter
1997	J	`jnl`	A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling. Mach. Learn. Jonathan Baxter
1997	—	`conf`	A Result Relating Convex EuroCOLT Jonathan Baxter, Peter L. Bartlett
1997	A*	`conf`	The Canonical Distortion Measure for Vector Quantization and Function Approximation. ICML Jonathan Baxter
1997	—	`conf`	The Canonical Distortion Measure in Feature Space and 1-NN Classification. NIPS Jonathan Baxter, Peter L. Bartlett
1996	A*	`conf`	A Bayesian/Information Theoretic Model of Bias Learning. COLT Jonathan Baxter
1996	B	`conf`	Learning to Compress Ergodic Sources. Data Compression Conference Jonathan Baxter, John Shawe-Taylor
1995	A*	`conf`	Learning Internal Representations. COLT Jonathan Baxter
1995	—	`conf`	Learning Model Bias. NIPS Jonathan Baxter