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Gerald Tesauro
Gerry Tesauro
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- affiliation: IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA
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2020 – today
- 2023
- [i28]Tyler Malloy, Miao Liu, Matthew D. Riemer, Tim Klinger, Gerald Tesauro, Chris R. Sims:
Learning in Factored Domains with Information-Constrained Visual Representations. CoRR abs/2303.17508 (2023) - 2022
- [c68]Marwa Abdulhai, Dong-Ki Kim, Matthew Riemer, Miao Liu, Gerald Tesauro, Jonathan P. How:
Context-Specific Representation Abstraction for Deep Option Learning. AAAI 2022: 5959-5967 - [c67]Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How:
Influencing Long-Term Behavior in Multiagent Reinforcement Learning. NeurIPS 2022 - [i27]Junkyu Lee, Michael Katz, Don Joven Agravante, Miao Liu, Tim Klinger, Murray Campbell, Shirin Sohrabi, Gerald Tesauro:
AI Planning Annotation for Sample Efficient Reinforcement Learning. CoRR abs/2203.00669 (2022) - [i26]Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How:
Influencing Long-Term Behavior in Multiagent Reinforcement Learning. CoRR abs/2203.03535 (2022) - [i25]Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Gerald Tesauro, Jonathan P. How:
Game-Theoretical Perspectives on Active Equilibria: A Preferred Solution Concept over Nash Equilibria. CoRR abs/2210.16175 (2022) - 2021
- [c66]Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Pushkar Shukla, Sadhana Kumaravel, Gerald Tesauro, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell:
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines. AAAI 2021: 9018-9027 - [c65]Tyler Malloy, Tim Klinger, Miao Liu, Gerald Tesauro, Matthew Riemer, Chris R. Sims:
RL Generalization in a Theory of Mind Game Through a Sleep Metaphor (Student Abstract). AAAI 2021: 15841-15842 - [c64]Tyler Malloy, Chris R. Sims
, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro:
Capacity-Limited Decentralized Actor-Critic for Multi-Agent Games. CoG 2021: 1-8 - [c63]Tyler Malloy, Tim Klinger, Miao Liu, Gerald Tesauro, Matthew Riemer, Chris R. Sims:
Modeling Capacity-Limited Decision Making Using a Variational Autoencoder. CogSci 2021 - [c62]Dong-Ki Kim, Miao Liu, Matthew Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan P. How:
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning. ICML 2021: 5541-5550 - [c61]Cameron Allen, Michael Katz, Tim Klinger, George Konidaris, Matthew Riemer, Gerald Tesauro:
Efficient Black-Box Planning Using Macro-Actions with Focused Effects. IJCAI 2021: 4024-4031 - [i24]Marwa Abdulhai, Dong-Ki Kim, Matthew Riemer, Miao Liu, Gerald Tesauro, Jonathan P. How:
Context-Specific Representation Abstraction for Deep Option Learning. CoRR abs/2109.09876 (2021) - 2020
- [c60]Matthew Riemer, Ignacio Cases, Clemens Rosenbaum, Miao Liu, Gerald Tesauro:
On the Role of Weight Sharing During Deep Option Learning. AAAI 2020: 5519-5526 - [c59]Dong-Ki Kim, Miao Liu, Shayegan Omidshafiei, Sebastian Lopez-Cot, Matthew Riemer, Golnaz Habibi, Gerald Tesauro, Sami Mourad, Murray Campbell, Jonathan P. How:
Learning Hierarchical Teaching Policies for Cooperative Agents. AAMAS 2020: 620-628 - [c58]Gang Wang, Songtao Lu, Georgios B. Giannakis, Gerald Tesauro, Jian Sun:
Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis. NeurIPS 2020 - [i23]Cameron Allen, Tim Klinger, George Konidaris, Matthew Riemer, Gerald Tesauro:
Finding Macro-Actions with Disentangled Effects for Efficient Planning with the Goal-Count Heuristic. CoRR abs/2004.13242 (2020) - [i22]Keerthiram Murugesan, Mattia Atzeni, Pavan Kapanipathi, Pushkar Shukla, Sadhana Kumaravel, Gerald Tesauro, Kartik Talamadupula, Mrinmaya Sachan, Murray Campbell:
Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines. CoRR abs/2010.03790 (2020) - [i21]Tyler Malloy, Chris R. Sims, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro:
Deep RL With Information Constrained Policies: Generalization in Continuous Control. CoRR abs/2010.04646 (2020) - [i20]Dong-Ki Kim, Miao Liu, Matthew Riemer, Chuangchuang Sun, Marwa Abdulhai, Golnaz Habibi, Sebastian Lopez-Cot, Gerald Tesauro, Jonathan P. How:
A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning. CoRR abs/2011.00382 (2020) - [i19]Tyler Malloy, Tim Klinger, Miao Liu, Matthew Riemer, Gerald Tesauro, Chris R. Sims:
Consolidation via Policy Information Regularization in Deep RL for Multi-Agent Games. CoRR abs/2011.11517 (2020)
2010 – 2019
- 2019
- [c57]Xiaoxiao Guo, Shiyu Chang, Mo Yu, Gerald Tesauro, Murray Campbell:
Hybrid Reinforcement Learning with Expert State Sequences. AAAI 2019: 3739-3746 - [c56]Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How:
Learning to Teach in Cooperative Multiagent Reinforcement Learning. AAAI 2019: 6128-6136 - [c55]Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro:
Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference. ICLR (Poster) 2019 - [i18]Dong-Ki Kim, Miao Liu, Shayegan Omidshafiei, Sebastian Lopez-Cot, Matthew Riemer, Golnaz Habibi, Gerald Tesauro, Sami Mourad, Murray Campbell, Jonathan P. How:
Learning Hierarchical Teaching in Cooperative Multiagent Reinforcement Learning. CoRR abs/1903.03216 (2019) - [i17]Xiaoxiao Guo, Shiyu Chang, Mo Yu, Gerald Tesauro, Murray Campbell:
Hybrid Reinforcement Learning with Expert State Sequences. CoRR abs/1903.04110 (2019) - [i16]Matthew Riemer, Ignacio Cases, Clemens Rosenbaum, Miao Liu, Gerald Tesauro:
On the Role of Weight Sharing During Deep Option Learning. CoRR abs/1912.13408 (2019) - 2018
- [j23]Ron Sun, David Silver, Gerald Tesauro, Guang-Bin Huang
Introduction to the special issue on deep reinforcement learning: An editorial. Neural Networks 107: 1-2 (2018) - [c54]Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerry Tesauro, Bowen Zhou, Jing Jiang:
R3: Reinforced Ranker-Reader for Open-Domain Question Answering. AAAI 2018: 5981-5988 - [c53]Marlos C. Machado
Eigenoption Discovery through the Deep Successor Representation. ICLR (Poster) 2018 - [c52]Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell:
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering. ICLR (Poster) 2018 - [c51]Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar
Diverse Few-Shot Text Classification with Multiple Metrics. NAACL-HLT 2018: 1206-1215 - [c50]Xiaoxiao Guo, Hui Wu, Yu Cheng, Steven Rennie, Gerald Tesauro, Rogério Schmidt Feris:
Dialog-based Interactive Image Retrieval. NeurIPS 2018: 676-686 - [c49]Matthew Riemer, Miao Liu, Gerald Tesauro:
Learning Abstract Options. NeurIPS 2018: 10445-10455 - [i15]Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, Bowen Zhou:
Diverse Few-Shot Text Classification with Multiple Metrics. CoRR abs/1805.07513 (2018) - [i14]Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu, Gerald Tesauro, Matthew Riemer, Christopher Amato, Murray Campbell, Jonathan P. How:
Learning to Teach in Cooperative Multiagent Reinforcement Learning. CoRR abs/1805.07830 (2018) - [i13]Matthew Riemer, Miao Liu, Gerald Tesauro:
Learning Abstract Options. CoRR abs/1810.11583 (2018) - [i12]Matthew Riemer, Ignacio Cases, Robert Ajemian, Miao Liu, Irina Rish, Yuhai Tu, Gerald Tesauro:
Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference. CoRR abs/1810.11910 (2018) - 2017
- [j22]Mohan Sridharan, Gerald Tesauro, James A. Hendler
Cognitive Computing. IEEE Intell. Syst. 32(4): 3-4 (2017) - [c48]Iulian Vlad Serban, Tim Klinger, Gerald Tesauro, Kartik Talamadupula, Bowen Zhou, Yoshua Bengio, Aaron C. Courville:
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation. AAAI 2017: 3288-3294 - [c47]Ruben Rodriguez Torrado, Jesus Rios, Gerald Tesauro:
Optimal Sequential Drilling for Hydrocarbon Field Development Planning. AAAI 2017: 4734-4739 - [c46]Xiaoxiao Guo, Tim Klinger, Clemens Rosenbaum, Joseph P. Bigus, Murray Campbell, Ban Kawas, Kartik Talamadupula, Gerry Tesauro, Satinder Singh:
Learning to Query, Reason, and Answer Questions On Ambiguous Texts. ICLR (Poster) 2017 - [i11]Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Gerald Tesauro, Haoyu Wang, Bowen Zhou:
Robust Task Clustering for Deep Many-Task Learning. CoRR abs/1708.07918 (2017) - [i10]Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerald Tesauro, Bowen Zhou, Jing Jiang:
R3: Reinforced Reader-Ranker for Open-Domain Question Answering. CoRR abs/1709.00023 (2017) - [i9]Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, Murray Campbell:
Eigenoption Discovery through the Deep Successor Representation. CoRR abs/1710.11089 (2017) - [i8]Shuohang Wang, Mo Yu, Jing Jiang, Wei Zhang, Xiaoxiao Guo, Shiyu Chang, Zhiguo Wang, Tim Klinger, Gerald Tesauro, Murray Campbell:
Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering. CoRR abs/1711.05116 (2017) - [i7]Miao Liu, Marlos C. Machado, Gerald Tesauro, Murray Campbell:
The Eigenoption-Critic Framework. CoRR abs/1712.04065 (2017) - 2016
- [c45]Ashish Sabharwal, Horst Samulowitz, Gerald Tesauro:
Selecting Near-Optimal Learners via Incremental Data Allocation. AAAI 2016: 2007-2015 - [i6]Ashish Sabharwal, Horst Samulowitz, Gerald Tesauro:
Selecting Near-Optimal Learners via Incremental Data Allocation. CoRR abs/1601.00024 (2016) - [i5]Sarath Chandar, Sungjin Ahn, Hugo Larochelle, Pascal Vincent, Gerald Tesauro, Yoshua Bengio:
Hierarchical Memory Networks. CoRR abs/1605.07427 (2016) - [i4]Iulian Vlad Serban, Tim Klinger, Gerald Tesauro, Kartik Talamadupula, Bowen Zhou, Yoshua Bengio, Aaron C. Courville:
Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation. CoRR abs/1606.00776 (2016) - 2015
- [j21]Stefano V. Albrecht, André da Motta Salles Barreto, Darius Braziunas, David L. Buckeridge, Heriberto Cuayáhuitl, Nina Dethlefs, Markus Endres
Reports of the AAAI 2014 Conference Workshops. AI Mag. 36(1): 87-98 (2015) - [c44]Kareem Amin, Satyen Kale, Gerald Tesauro, Deepak S. Turaga:
Budgeted Prediction with Expert Advice. AAAI 2015: 2490-2496 - [c43]Alain Biem, Maria Butrico, Mark Feblowitz, Tim Klinger, Yuri Malitsky, Kenney Ng, Adam Perer, Chandra Reddy, Anton Riabov, Horst Samulowitz, Daby M. Sow, Gerald Tesauro, Deepak S. Turaga:
Towards Cognitive Automation of Data Science. AAAI 2015: 4268-4269 - 2014
- [i3]Gerald Tesauro, David Gondek, Jonathan Lenchner, James Fan, John M. Prager:
Analysis of Watson's Strategies for Playing Jeopardy! CoRR abs/1402.0571 (2014) - 2013
- [j20]Gerry Tesauro, David Gondek, Jonathan Lenchner, James Fan, John M. Prager:
Analysis of Watson's Strategies for Playing Jeopardy! J. Artif. Intell. Res. 47: 205-251 (2013) - 2012
- [j19]Gerry Tesauro, David Gondek, Jon Lenchner, James Fan, John M. Prager:
Simulation, learning, and optimization techniques in Watson's game strategies. IBM J. Res. Dev. 56(3): 16 (2012) - [c42]Janusz Marecki, Gerald Tesauro, Richard B. Segal:
Playing repeated Stackelberg games with unknown opponents. AAMAS 2012: 821-828 - [c41]Joseph P. Bigus, Ching-Hua Chen-Ritzo, Keith Hermiz, Gerald Tesauro, Robert Sorrentino:
Applying a framework for healthcare incentives simulation. WSC 2012: 80:1-80:12 - [i2]Gerald Tesauro, V. T. Rajan, Richard B. Segal:
Bayesian Inference in Monte-Carlo Tree Search. CoRR abs/1203.3519 (2012) - [i1]Craig Boutilier, Rajarshi Das, Jeffrey O. Kephart, Gerald Tesauro, William E. Walsh:
Cooperative Negotiation in Autonomic Systems using Incremental Utility Elicitation. CoRR abs/1212.2443 (2012) - 2010
- [c40]Gerald Tesauro, V. T. Rajan, Richard B. Segal:
Bayesian Inference in Monte-Carlo Tree Search. UAI 2010: 580-588
2000 – 2009
- 2009
- [c39]David Silver, Gerald Tesauro:
Monte-Carlo simulation balancing. ICML 2009: 945-952 - 2008
- [c38]Rajarshi Das, Jeffrey O. Kephart, Charles Lefurgy, Gerald Tesauro, David W. Levine, Hoi Y. Chan:
Autonomic multi-agent management of power and performance in data centers. AAMAS (Industry Track) 2008: 107-114 - [c37]Irina Rish, Gerald Tesauro:
Active Collaborative Prediction with Maximum Margin Matrix Factorization. ISAIM 2008 - 2007
- [j18]Gerald Tesauro, Nicholas K. Jong, Rajarshi Das, Mohamed N. Bennani:
On the use of hybrid reinforcement learning for autonomic resource allocation. Clust. Comput. 10(3): 287-299 (2007) - [j17]Gerald Tesauro:
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies. IEEE Internet Comput. 11(1): 22-30 (2007) - [c36]Jeffrey O. Kephart, Hoi Y. Chan, Rajarshi Das, David W. Levine, Gerald Tesauro, Freeman L. Rawson III, Charles Lefurgy:
Coordinating Multiple Autonomic Managers to Achieve Specified Power-Performance Tradeoffs. ICAC 2007: 24 - [c35]Irina Rish, Gerald Tesauro:
Estimating End-to-End Performance by Collaborative Prediction with Active Sampling. Integrated Network Management 2007: 294-303 - [c34]Gerald Tesauro, Rajarshi Das, Hoi Y. Chan, Jeffrey O. Kephart, David W. Levine, Freeman L. Rawson III, Charles Lefurgy:
Managing Power Consumption and Performance of Computing Systems Using Reinforcement Learning. NIPS 2007: 1497-1504 - [c33]Kilian Q. Weinberger, Gerald Tesauro:
Metric Learning for Kernel Regression. AISTATS 2007: 612-619 - 2006
- [c32]Gerald Tesauro, Nicholas K. Jong, Rajarshi Das, Mohamed N. Bennani:
Improvement of Systems Management Policies Using Hybrid Reinforcement Learning. ECML 2006: 783-791 - [c31]Gerald Tesauro, Nicholas K. Jong, Rajarshi Das, Mohamed N. Bennani:
A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation. ICAC 2006: 65-73 - 2005
- [c30]Relu Patrascu, Craig Boutilier, Rajarshi Das, Jeffrey O. Kephart, Gerald Tesauro, William E. Walsh:
New Approaches to Optimization and Utility Elicitation in Autonomic Computing. AAAI 2005: 140-145 - [c29]Gerald Tesauro:
Online Resource Allocation Using Decompositional Reinforcement Learning. AAAI 2005: 886-891 - [c28]Gerald Tesauro, Rajarshi Das, William E. Walsh, Jeffrey O. Kephart:
Utility-Function-Driven Resource Allocation in Autonomic Systems. ICAC 2005: 342-343 - 2004
- [c27]Gerald Tesauro, David M. Chess, William E. Walsh, Rajarshi Das, Alla Segal, Ian Whalley, Jeffrey O. Kephart, Steve R. White:
A Multi-Agent Systems Approach to Autonomic Computing. AAMAS 2004: 464-471 - [c26]William E. Walsh, Gerald Tesauro, Jeffrey O. Kephart, Rajarshi Das:
Utility Functions in Autonomic Systems. ICAC 2004: 70-77 - 2003
- [c25]Gerald Tesauro:
Extending Q-Learning to General Adaptive Multi-Agent Systems. NIPS 2003: 871-878 - [c24]Cuihong Li, Gerald Tesauro:
A strategic decision model for multi-attribute bilateral negotiation with alternating. EC 2003: 208-209 - [c23]James E. Hanson, Gerald Tesauro, Jeffrey O. Kephart, E. C. Snibl:
Multi-agent implementation of asymmetric protocol for bilateral negotiations. EC 2003: 224-225 - [c22]Craig Boutilier, Rajarshi Das, Jeffrey O. Kephart, Gerald Tesauro, William E. Walsh:
Cooperative Negotiation in Autonomic Systems using Incremental Utility Elicitation. UAI 2003: 89-97 - 2002
- [j16]Gerald Tesauro, Jeffrey O. Kephart:
Pricing in Agent Economies Using Multi-Agent Q-Learning. Auton. Agents Multi Agent Syst. 5(3): 289-304 (2002) - [j15]Gerald Tesauro:
Programming backgammon using self-teaching neural nets. Artif. Intell. 134(1-2): 181-199 (2002) - [c21]Gerald Tesauro, Jonathan Bredin:
Strategic sequential bidding in auctions using dynamic programming. AAMAS 2002: 591-598 - 2001
- [c20]Rajarshi Das, James E. Hanson, Jeffrey O. Kephart, Gerald Tesauro:
Agent-Human Interactions in the Continuous Double Auction. IJCAI 2001: 1169-1187 - [c19]Gerald Tesauro:
Pricing in Agent Economies Using Neural Networks and Multi-agent Q-Learning. Sequence Learning 2001: 288-307 - [c18]Gerald Tesauro, Rajarshi Das:
High-performance bidding agents for the continuous double auction. EC 2001: 206-209 - 2000
- [j14]Gerald Tesauro, Jeffrey O. Kephart:
Foresight-based pricing algorithms in agent economies. Decis. Support Syst. 28(1-2): 49-60 (2000) - [c17]Manu Sridharan
Multi-Agent Q-Learning and Regression Trees for Automated Pricing Decisions. ICMAS 2000: 447-448 - [c16]Jeffrey O. Kephart, Gerald Tesauro:
Pseudo-convergent Q-Learning by Competitive Pricebots. ICML 2000: 463-470 - [c15]Manu Sridharan, Gerald Tesauro:
Multi-agent Q-learning and Regression Trees for Automated Pricing Decisions. ICML 2000: 927-934
1990 – 1999
- 1999
- [c14]Amy Greenwald
Strategic pricebot dynamics. EC 1999: 58-67 - 1998
- [j13]Gerald Tesauro:
Comments on "Co-Evolution in the Successful Learning of Backgammon Strategy". Mach. Learn. 32(3): 241-243 (1998) - [c13]Gerald Tesauro, Jeffrey O. Kephart:
Foresight-based pricing algorithms in an economy of software agents. ICE 1998: 37-44 - 1996
- [c12]Gerald Tesauro, Gregory R. Galperin:
On-line Policy Improvement using Monte-Carlo Search. NIPS 1996: 1068-1074 - 1995
- [j12]Gerald Tesauro:
Temporal Difference Learning and TD-Gammon. Commun. ACM 38(3): 58-68 (1995) - [j11]Gerald Tesauro:
Temporal Difference Learning and TD-Gammon. J. Int. Comput. Games Assoc. 18(2): 88 (1995) - [c11]Jeffrey O. Kephart, Gregory B. Sorkin, William C. Arnold, David M. Chess, Gerald Tesauro, Steve R. White:
Biologically Inspired Defenses Against Computer Viruses. IJCAI (1) 1995: 985-996 - [e2]Gerald Tesauro, David S. Touretzky, Todd K. Leen:
Advances in Neural Information Processing Systems 7, [NIPS Conference, Denver, Colorado, USA, 1994]. MIT Press 1995 [contents] - 1994
- [j10]Gerald Tesauro:
TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play. Neural Comput. 6(2): 215-219 (1994) - [e1]Jack D. Cowan, Gerald Tesauro, Joshua Alspector:
Advances in Neural Information Processing Systems 6, [7th NIPS Conference, Denver, Colorado, USA, 1993]. Morgan Kaufmann 1994, ISBN 1-55860-322-0 [contents] - 1992
- [j9]Gerald Tesauro:
Practical Issues in Temporal Difference Learning. Mach. Learn. 8: 257-277 (1992) - [j8]David A. Cohn, Gerald Tesauro:
How Tight Are the Vapnik-Chervonenkis Bounds? Neural Comput. 4(2): 249-269 (1992) - [c10]Gerald Tesauro:
Temporal Difference Learning of Backgammon Strategy. ML 1992: 451-457 - 1991
- [j7]Jakub Wejchert, Gerald Tesauro:
Visualizing processes in neural networks. IBM J. Res. Dev. 35(1): 244-253 (1991) - [c9]Gerald Tesauro:
Practical Issues in Temporal Difference Learning. NIPS 1991: 259-266 - 1990
- [c8]Gerald Tesauro:
Neurogammon: a neural-network backgammon program. IJCNN 1990: 33-39 - [c7]David A. Cohn, Gerald Tesauro:
Can Neural Networks Do Better Than the Vapnik-Chervonenkis Bounds? NIPS 1990: 911-917
1980 – 1989
- 1989
- [j6]Gerald Tesauro, Terrence J. Sejnowski:
A Parallel Network that Learns to Play Backgammon. Artif. Intell. 39(3): 357-390 (1989) - [j5]Gerald Tesauro:
Neurogammon Wins Computer Olympiad. Neural Comput. 1(3): 321-323 (1989) - [j4]Gerald Tesauro, Yu He, Subutai Ahmad
Asymptotic Convergence of Backpropagation. Neural Comput. 1(3): 382-391 (1989) - [c6]Jakub Wejchert, Gerald Tesauro:
Neural Network Visualization. NIPS 1989: 465-472 - [c5]Subutai Ahmad, Gerald Tesauro, Yu He:
Asymptotic Convergence of Backpropagation: Numerical Experiments. NIPS 1989: 606-613 - 1988
- [j3]Gerald Tesauro, Bob Janssens:
Scaling Relationships in Back-propagation Learning. Complex Syst. 2(1) (1988) - [j2]Subutai Ahmad, Gerald Tesauro:
A study of scaling and generalization in neural networks. Neural Networks 1(Supplement-1): 3-6 (1988) - [c4]Gerald Tesauro:
Connectionist Learning of Expert Backgammon Evaluations. ML 1988: 200-206 - [c3]Gerald Tesauro:
Connectionist Learning of Expert Preferences by Comparison Training. NIPS 1988: 99-106 - [c2]Subutai Ahmad, Gerald Tesauro:
Scaling and Generalization in Neural Networks: A Case Study. NIPS 1988: 160-168 - 1987
- [j1]Gerald Tesauro:
Scaling Relationships in Back-Propagation Learning: Dependence on Training Set Size. Complex Syst. 1(2) (1987) - [c1]Gerald Tesauro, Terrence J. Sejnowski:
A 'Neural' Network that Learns to Play Backgammon. NIPS 1987: 794-803
Coauthor Index
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