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Jonathan Baxter

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2009
31EEDan 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
30EEDan 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
29EEEvan 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
27EEPeter 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
25EEEvan 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)
23EEJonathan Baxter, Peter L. Bartlett: Infinite-Horizon Policy-Gradient Estimation. J. Artif. Intell. Res. (JAIR) 15: 319-350 (2001)
22EEJonathan 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
20EEDouglas 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
18EEDouglas Aberdeen, Jonathan Baxter, Robert Edwards: 98¢/Mflops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster. SC 2000
17EEJonathan 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
14EELlew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean: Boosting Algorithms as Gradient Descent. NIPS 1999: 512-518
13EEJonathan Baxter, Andrew Tridgell, Lex Weaver: TDLeaf(lambda): Combining Temporal Difference Learning with Game-Tree Search CoRR cs.LG/9901001: (1999)
12EEJonathan 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
9EELlew 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
4EEJonathan 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
2EEJonathan Baxter: Learning Internal Representations. COLT 1995: 311-320
1EEJonathan Baxter: Learning Model Bias. NIPS 1995: 169-175

Coauthor Index

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

Colors in the list of coauthors

Copyright © Sun May 17 03:24:02 2009 by Michael Ley (ley@uni-trier.de)