- CSB - MetaPress - Google - Bing - Yahoo
 | 2011 | |
|---|---|---|
| i5 |      | |
| i4 | | Peter L. Bartlett, Jonathan Baxter: Infinite-Horizon Policy-Gradient Estimation. CoRR abs/1106.0665 (2011) |
| i3 | | Peter L. Bartlett, Jonathan Baxter, Lex Weaver: Experiments with Infinite-Horizon, Policy-Gradient Estimation. CoRR abs/1106.0666 (2011) |
| 2009 | ||
| c20 | | Dan Cosley, Kathy Akey, Brian Alson, Jonathan Baxter, Mark Broomfield, Soyoung Lee, Chethan Sarabu: Using technologies to support reminiscence. BCS HCI 2009: 480-484 |
| c19 | | 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 | ||
| c18 | | 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 | ||
| j10 | | 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 | ||
| j9 | | Peter L. Bartlett, Jonathan Baxter: Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. J. Comput. Syst. Sci. 64(1): 133-150 (2002) |
| c17 |  | Douglas Aberdeen, Jonathan Baxter: Scalable Internal-State Policy-Gradient Methods for POMDPs. ICML 2002: 3-10 |
| 2001 | ||
| j8 | | 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) |
| j7 | | Jonathan Baxter, Peter L. Bartlett: Infinite-Horizon Policy-Gradient Estimation. J. Artif. Intell. Res. (JAIR) 15: 319-350 (2001) |
| j6 | | Jonathan Baxter, Peter L. Bartlett, Lex Weaver: Experiments with Infinite-Horizon, Policy-Gradient Estimation. J. Artif. Intell. Res. (JAIR) 15: 351-381 (2001) |
| c16 | | Nigel Tao, Jonathan Baxter, Lex Weaver: A Multi-Agent Policy-Gradient Approach to Network Routing. ICML 2001: 553-560 |
| c15 | | Evan Greensmith, Peter L. Bartlett, Jonathan Baxter: Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. NIPS 2001: 1507-1514 |
| 2000 | ||
| j5 | | Jonathan Baxter: A Model of Inductive Bias Learning. J. Artif. Intell. Res. (JAIR) 12: 149-198 (2000) |
| j4 | | Llew Mason, Peter L. Bartlett, Jonathan Baxter: Improved Generalization Through Explicit Optimization of Margins. Machine Learning 38(3): 243-255 (2000) |
| j3 | | Jonathan Baxter, Andrew Tridgell, Lex Weaver: Learning to Play Chess Using Temporal Differences. Machine Learning 40(3): 243-263 (2000) |
| c14 | | Peter L. Bartlett, Jonathan Baxter: Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. COLT 2000: 133-141 |
| c13 | | Douglas Aberdeen, Jonathan Baxter: General Matrix-Matrix Multiplication Using SIMD Features of the PIII (Research Note). Euro-Par 2000: 980-983 |
| c12 | | Jonathan Baxter, Peter L. Bartlett: Reinforcement Learning in POMDP's via Direct Gradient Ascent. ICML 2000: 41-48 |
| c11 | | Douglas Aberdeen, Jonathan Baxter, Robert Edwards: 98¢/Mflops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster. SC 2000: 44 |
| 1999 | ||
| j2 | | Jonathan Baxter, Nicolò Cesa-Bianchi: Guest Editors' Introduction. Machine Learning 37(3): 239-240 (1999) |
| c10 | | Llew Mason, Jonathan Baxter, Peter L. Bartlett, Marcus R. Frean: Boosting Algorithms as Gradient Descent. NIPS 1999: 512-518 |
| i2 | | Jonathan Baxter, Andrew Tridgell, Lex Weaver: TDLeaf(lambda): Combining Temporal Difference Learning with Game-Tree Search. CoRR cs.LG/9901001 (1999) |
| i1 | | 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) |
| 1998 | ||
| c9 | | Jonathan Baxter, Andrew Tridgell, Lex Weaver: KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree Search. ICML 1998: 28-36 |
| c8 | | Llew Mason, Peter L. Bartlett, Jonathan Baxter: Direct Optimization of Margins Improves Generalization in Combined Classifiers. NIPS 1998: 288-294 |
| 1997 | ||
| j1 | | Jonathan Baxter: A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling. Machine Learning 28(1): 7-39 (1997) |
| c7 | | Jonathan Baxter, Peter L. Bartlett: A Result Relating Convex n-Widths to Covering Numbers with some Applications to Neural Networks. EuroCOLT 1997: 251-259 |
| c6 | | Jonathan Baxter: The Canonical Distortion Measure for Vector Quantization and Function Approximation. ICML 1997: 39-47 |
| c5 | | Jonathan Baxter, Peter L. Bartlett: The Canonical Distortion Measure in Feature Space and 1-NN Classification. NIPS 1997 |
| 1996 | ||
| c4 | | |
| c3 | | Jonathan Baxter, John Shawe-Taylor: Learning to Compress Ergodic Sources. Data Compression Conference 1996: 423 |
| 1995 | ||
| c2 | | |
| c1 | | |
Colors in the list of coauthors
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