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Jonathan Baxter
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2020 – today
- 2020
- [i15]Jonathan Baxter:
Theoretical Models of Learning to Learn. CoRR abs/2002.12364 (2020)
2010 – 2019
- 2019
- [i14]Jonathan Baxter:
Learning Internal Representations (PhD Thesis). CoRR abs/1911.03731 (2019) - [i13]Douglas Aberdeen, Jonathan Baxter, Robert Edwards:
92c/MFlops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster. CoRR abs/1911.05181 (2019) - [i12]Jonathan Baxter:
Learning internal representations. CoRR abs/1911.05781 (2019) - [i11]Jonathan Baxter:
A Bayesian/Information Theoretic Model of Bias Learning. CoRR abs/1911.06129 (2019) - [i10]Jonathan Baxter:
Learning Model Bias. CoRR abs/1911.06164 (2019) - [i9]Jonathan Baxter:
The Canonical Distortion Measure for Vector Quantization and Function Approximation. CoRR abs/1911.06319 (2019) - [i8]Peter L. Bartlett, Jonathan Baxter:
Hebbian Synaptic Modifications in Spiking Neurons that Learn. CoRR abs/1911.07247 (2019) - [i7]Douglas Aberdeen, Jonathan Baxter:
General Matrix-Matrix Multiplication Using SIMD features of the PIII. CoRR abs/1912.04379 (2019) - [i6]Jonathan Baxter:
Some observations concerning Off Training Set (OTS) error. CoRR abs/1912.05915 (2019) - 2011
- [i5]Jonathan Baxter:
A Model of Inductive Bias Learning. CoRR abs/1106.0245 (2011) - [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)
2000 – 2009
- 2009
- [c22]Dan Cosley, Kathy Akey, Brian Alson, Jonathan Baxter, Mark Broomfield, Soyoung Lee, Chethan Sarabu:
Using technologies to support reminiscence. BCS HCI 2009: 480-484 - [c21]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
- [c20]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
- [j11]Evan Greensmith, Peter L. Bartlett, Jonathan Baxter:
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. J. Mach. Learn. Res. 5: 1471-1530 (2004) - 2002
- [j10]Peter L. Bartlett
, Jonathan Baxter:
Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. J. Comput. Syst. Sci. 64(1): 133-150 (2002) - [c19]Douglas Aberdeen, Jonathan Baxter:
Scalable Internal-State Policy-Gradient Methods for POMDPs. ICML 2002: 3-10 - 2001
- [j9]Douglas Aberdeen, Jonathan Baxter:
Emmerald: a fast matrix-matrix multiply using Intel's SSE instructions. Concurr. Comput. Pract. Exp. 13(2): 103-119 (2001) - [j8]Jonathan Baxter, Peter L. Bartlett
Infinite-Horizon Policy-Gradient Estimation. J. Artif. Intell. Res. 15: 319-350 (2001) - [j7]Jonathan Baxter, Peter L. Bartlett, Lex Weaver:
Experiments with Infinite-Horizon, Policy-Gradient Estimation. J. Artif. Intell. Res. 15: 351-381 (2001) - [c18]Nigel Tao, Jonathan Baxter, Lex Weaver:
A Multi-Agent Policy-Gradient Approach to Network Routing. ICML 2001: 553-560 - [c17]Evan Greensmith, Peter L. Bartlett, Jonathan Baxter:
Variance Reduction Techniques for Gradient Estimates in Reinforcement Learning. NIPS 2001: 1507-1514 - 2000
- [j6]Jonathan Baxter:
A Model of Inductive Bias Learning. J. Artif. Intell. Res. 12: 149-198 (2000) - [j5]Llew Mason, Peter L. Bartlett
Improved Generalization Through Explicit Optimization of Margins. Mach. Learn. 38(3): 243-255 (2000) - [j4]Jonathan Baxter, Andrew Tridgell, Lex Weaver:
Learning to Play Chess Using Temporal Differences. Mach. Learn. 40(3): 243-263 (2000) - [c16]Peter L. Bartlett, Jonathan Baxter:
Stochastic optimization of controlled partially observable Markov decision processes. CDC 2000: 124-129 - [c15]Peter L. Bartlett, Jonathan Baxter:
Estimation and Approximation Bounds for Gradient-Based Reinforcement Learning. COLT 2000: 133-141 - [c14]Douglas Aberdeen, Jonathan Baxter:
General Matrix-Matrix Multiplication Using SIMD Features of the PIII (Research Note). Euro-Par 2000: 980-983 - [c13]Jonathan Baxter, Peter L. Bartlett:
Reinforcement Learning in POMDP's via Direct Gradient Ascent. ICML 2000: 41-48 - [c12]Jonathan Baxter, Peter L. Bartlett:
Direct gradient-based reinforcement learning. ISCAS 2000: 271-274 - [c11]Douglas Aberdeen, Jonathan Baxter, Robert Edwards:
98¢/Mflops/s, Ultra-Large-Scale Neural-Network Training on a PIII Cluster. SC 2000: 44
1990 – 1999
- 1999
- [j3]Jonathan Baxter, Nicolò Cesa-Bianchi:
Guest Editors' Introduction. Mach. Learn. 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
- [j2]Jonathan Baxter, Andrew Tridgell, Lex Weaver:
Experiments in Parameter Learning Using Temporal Differences. J. Int. Comput. Games Assoc. 21(2): 84-99 (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 - [p2]Jonathan Baxter:
Theoretical Models of Learning to Learn. Learning to Learn 1998: 71-94 - [p1]Jonathan Baxter:
The Canonical Distortion Measure for Vector Quantization and Function Approximation. Learning to Learn 1998: 159-179 - 1997
- [j1]Jonathan Baxter:
A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling. Mach. Learn. 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: 245-251 - 1996
- [c4]Jonathan Baxter:
A Bayesian/Information Theoretic Model of Bias Learning. COLT 1996: 77-88 - [c3]Jonathan Baxter, John Shawe-Taylor
Learning to Compress Ergodic Sources. Data Compression Conference 1996: 423 - 1995
- [c2]Jonathan Baxter:
Learning Internal Representations. COLT 1995: 311-320 - [c1]Jonathan Baxter:
Learning Model Bias. NIPS 1995: 169-175
Coauthor Index
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