Jonathan Baxter

2009
31	EE	Dan Cosley, Jonathan Baxter, Soyoung Lee, Brian Alson, Saeko Nomura, Phil Adams, Chethan Sarabu, Geri Gay: A tag in the hand: supporting semantic, social, and spatial navigation in museums. CHI 2009: 1953-1962
2008
30	EE	Dan Cosley, Joel Lewenstein, Andrew Herman, Jenna Holloway, Jonathan Baxter, Saeko Nomura, Kirsten Boehner, Geri Gay: ArtLinks: fostering social awareness and reflection in museums. CHI 2008: 403-412
2004
29	EE	Evan Greensmith, Peter L. Bartlett, Jonathan Baxter: Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. Journal of Machine Learning Research 5: 1471-1530 (2004)
2002
28		Douglas Aberdeen, Jonathan Baxter: Scalable Internal-State Policy-Gradient Methods for POMDPs. ICML 2002: 3-10
27	EE	Peter L. Bartlett, Jonathan Baxter: Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. J. Comput. Syst. Sci. 64(1): 133-150 (2002)
2001
26		Nigel Tao, Jonathan Baxter, Lex Weaver: A Multi-Agent Policy-Gradient Approach to Network Routing. ICML 2001: 553-560
25	EE	Evan Greensmith, Peter L. Bartlett, Jonathan Baxter: Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. NIPS 2001: 1507-1514
24		Douglas Aberdeen, Jonathan Baxter: Emmerald: a fast matrix-matrix multiply using Intel's SSE instructions. Concurrency and Computation: Practice and Experience 13(2): 103-119 (2001)
23	EE	Jonathan Baxter, Peter L. Bartlett: Infinite-Horizon Policy-Gradient Estimation. J. Artif. Intell. Res. (JAIR) 15: 319-350 (2001)
22	EE	Jonathan Baxter, Peter L. Bartlett, Lex Weaver: Experiments with Infinite-Horizon, Policy-Gradient Estimation. J. Artif. Intell. Res. (JAIR) 15: 351-381 (2001)
2000
21		Peter L. Bartlett, Jonathan Baxter: Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. COLT 2000: 133-141
20	EE	Douglas Aberdeen, Jonathan Baxter: General Matrix-Matrix Multiplication Using SIMD Features of the PIII (Research Note). Euro-Par 2000: 980-983
19		Jonathan Baxter, Peter L. Bartlett: Reinforcement Learning in POMDP's via Direct Gradient Ascent. ICML 2000: 41-48
18	EE	Douglas Aberdeen, Jonathan Baxter, Robert Edwards: 98¢/Mflops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster. SC 2000
17	EE	Jonathan Baxter: A Model of Inductive Bias Learning. J. Artif. Intell. Res. (JAIR) 12: 149-198 (2000)
16		Llew Mason, Peter L. Bartlett, Jonathan Baxter: Improved Generalization Through Explicit Optimization of Margins. Machine Learning 38(3): 243-255 (2000)
15		Jonathan Baxter, Andrew Tridgell, Lex Weaver: Learning to Play Chess Using Temporal Differences. Machine Learning 40(3): 243-263 (2000)
1999
14	EE	Llew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean: Boosting Algorithms as Gradient Descent. NIPS 1999: 512-518
13	EE	Jonathan Baxter, Andrew Tridgell, Lex Weaver: TDLeaf(lambda): Combining Temporal Difference Learning with Game-Tree Search CoRR cs.LG/9901001: (1999)
12	EE	Jonathan Baxter, Andrew Tridgell, Lex Weaver: KnightCap: A chess program that learns by combining TD(lambda) with game-tree search CoRR cs.LG/9901002: (1999)
11		Jonathan Baxter, Nicolò Cesa-Bianchi: Guest Editors' Introduction. Machine Learning 37(3): 239-240 (1999)
1998
10		Jonathan Baxter, Andrew Tridgell, Lex Weaver: KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree Search. ICML 1998: 28-36
9	EE	Llew Mason, Peter L. Bartlett, Jonathan Baxter: Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS 1998: 288-294
1997
8		Jonathan Baxter, Peter L. Bartlett: A Result Relating Convex n-Widths to Covering Numbers with some Applications to Neural Networks. EuroCOLT 1997: 251-259
7		Jonathan Baxter: The Canonical Distortion Measure for Vector Quantization and Function Approximation. ICML 1997: 39-47
6		Jonathan Baxter, Peter L. Bartlett: The Canonical Distortion Measure in Feature Space and 1-NN Classification. NIPS 1997
5		Jonathan Baxter: A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling. Machine Learning 28(1): 7-39 (1997)
1996
4	EE	Jonathan Baxter: A Bayesian/Information Theoretic Model of Bias Learning. COLT 1996: 77-88
3		Jonathan Baxter, John Shawe-Taylor: Learning to Compress Ergodic Sources. Data Compression Conference 1996: 423
1995
2	EE	Jonathan Baxter: Learning Internal Representations. COLT 1995: 311-320
1	EE	Jonathan Baxter: Learning Model Bias. NIPS 1995: 169-175

Coauthor Index

1	Douglas Aberdeen	[18] [20] [24] [28]
2	Phil Adams	[31]
3	Brian Alson	[31]
4	Peter L. Bartlett	[6] [8] [9] [14] [16] [19] [21] [22] [23] [25] [27] [29]
5	Kirsten Boehner	[30]
6	Nicolò Cesa-Bianchi	[11]
7	Dan Cosley	[30] [31]
8	Robert Edwards	[18]
9	Marcus R. Frean	[14]
10	Geri Gay	[30] [31]
11	Evan Greensmith	[25] [29]
12	Andrew Herman	[30]
13	Jenna Holloway	[30]
14	Soyoung Lee	[31]
15	Joel Lewenstein	[30]
16	Llew Mason	[9] [14] [16]
17	Saeko Nomura	[30] [31]
18	Chethan Sarabu	[31]
19	John Shawe-Taylor	[3]
20	Nigel Tao	[26]
21	Andrew Tridgell	[10] [12] [13] [15]
22	Lex Weaver	[10] [12] [13] [15] [22] [26]

Colors in the list of coauthors