Michael L. Littman

ORCID

Publication Activity (10 Years)

Years Active: 1989-2024
Publications (10 Years): 121

Top Venues

Publications

Cameron Allen, Aaron Kirtland, Ruo Yu Tao, Sam Lobel, Daniel Scott, Nicholas Petrocelli, Omer Gottesman, Ronald Parr, Michael L. Littman, George Konidaris
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy. CoRR (2024)
Max Zuo, Francisco Piedrahita Velez, Xiaochen Li, Michael L. Littman, Stephen H. Bach
Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages. CoRR (2024)
Haotian Fu, Shangqun Yu, Saket Tiwari, Michael L. Littman, George Konidaris
Meta-learning Parameterized Skills. ICML (2023)
Omer Gottesman, Kavosh Asadi, Cameron S. Allen, Samuel Lobel, George Konidaris, Michael L. Littman
Coarse-Grained Smoothness for Reinforcement Learning in Metric Spaces. AISTATS (2023)
Charles Isbell, Michael L. Littman, Peter Norvig
Software Engineering of Machine Learning Systems. Commun. ACM 66 (2) (2023)
Cambridge Yang, Michael L. Littman, Michael Carbin
Computably Continuous Reinforcement-Learning Objectives Are PAC-Learnable. AAAI (2023)
Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Dimitri Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael L. Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems. CoRR (2023)
Cambridge Yang, Michael L. Littman, Michael Carbin
Computably Continuous Reinforcement-Learning Objectives are PAC-learnable. CoRR (2023)
Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael L. Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha
A domain-agnostic approach for characterization of lifelong learning systems. Neural Networks 160 (2023)
David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
On the Expressivity of Markov Reward (Extended Abstract). IJCAI (2022)
Haotian Fu, Shangqun Yu, Michael L. Littman, George Konidaris
Model-based Lifelong Reinforcement Learning with Bayesian Exploration. NeurIPS (2022)
Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Taesup Kim, Michael L. Littman, Alexander J. Smola
Faster Deep Reinforcement Learning with Slower Online Network. NeurIPS (2022)
Haotian Fu, Shangqun Yu, Saket Tiwari, George Dimitri Konidaris, Michael L. Littman
Meta-Learning Transferable Parameterized Skills. CoRR (2022)
Lucas Lehnert, Michael J. Frank, Michael L. Littman
Reward-Predictive Clustering. CoRR (2022)
Lefan Zhang, Cyrus Zhou, Michael L. Littman, Blase Ur, Shan Lu
Helping Users Debug Trigger-Action Programs. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 6 (4) (2022)
Bowen He, Sreehari Rammohan, Jessica Zosa Forde, Michael L. Littman
Does DQN really learn? Exploring adversarial training schemes in Pong. CoRR (2022)
Charles Lovering, Jessica Zosa Forde, George Konidaris, Ellie Pavlick, Michael L. Littman
Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex. CoRR (2022)
Henry Sowerby, Zhiyuan Zhou, Michael L. Littman
Designing Rewards for Fast Learning. CoRR (2022)
Cambridge Yang, Michael L. Littman, Michael Carbin
On the (In)Tractability of Reinforcement Learning for LTL Objectives. IJCAI (2022)
Michael L. Littman, Ifeoma Ajunwa, Guy Berger, Craig Boutilier, Morgan Currie, Finale Doshi-Velez, Gillian K. Hadfield, Michael C. Horowitz, Charles Isbell, Hiroaki Kitano, Karen Levy, Terah Lyons, Melanie Mitchell, Julie Shah, Steven Sloman, Shannon Vallor, Toby Walsh
Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report. CoRR (2022)
Zhiyuan Zhou, Henry Sowerby, Michael L. Littman
Specifying Behavior Preference with Tiered Reward Functions. CoRR (2022)
Jamar L. Sullivan Jr., William Brackenbury, Andrew McNut, Kevin Bryson, Kwam Byll, Yuxin Chen, Michael L. Littman, Chenhao Tan, Blase Ur
Explaining Why: How Instructions and User Interfaces Impact Annotator Rationales When Labeling Text Data. NAACL-HLT (2022)
Haotian Fu, Shangqun Yu, Michael L. Littman, George Dimitri Konidaris
Model-based Lifelong Reinforcement Learning with Bayesian Exploration. CoRR (2022)
Charles Lovering, Jessica Forde, George Konidaris, Ellie Pavlick, Michael L. Littman
Evaluation beyond Task Performance: Analyzing Concepts in AlphaZero in Hex. NeurIPS (2022)
Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Michael L. Littman, Alexander J. Smola
Deep Q-Network with Proximal Iteration. CoRR (2021)
Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman
Lipschitz Lifelong Reinforcement Learning. AAAI (2021)
Jessica Zosa Forde, A. Feder Cooper, Kweku Kwegyir-Aggrey, Chris De Sa, Michael L. Littman
Model Selection's Disparate Impact in Real-World Deep Learning Applications. CoRR (2021)
Mingxuan Li, Michael L. Littman
Towards Sample Efficient Agents through Algorithmic Alignment (Student Abstract). AAAI (2021)
Jeff Druce, James Niehaus, Vanessa Moody, David D. Jensen, Michael L. Littman
Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program. CoRR (2021)
David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
On the Expressivity of Markov Reward. CoRR (2021)
Kavosh Asadi, Neev Parikh, Ronald E. Parr, George Dimitri Konidaris, Michael L. Littman
Deep Radial-Basis Value Functions for Continuous Control. AAAI (2021)
Valerie Zhao, Lefan Zhang, Bo Wang, Michael L. Littman, Shan Lu, Blase Ur
Understanding Trigger-Action Programs Through Novel Visualizations of Program Differences. CHI (2021)
David Abel, Cameron Allen, Dilip Arumugam, D. Ellis Hershkowitz, Michael L. Littman, Lawson L. S. Wong
Bad-Policy Density: A Measure of Reinforcement Learning Hardness. CoRR (2021)
Cambridge Yang, Michael L. Littman, Michael Carbin
Reinforcement Learning for General LTL Objectives Is Intractable. CoRR (2021)
Mark K. Ho, David Abel, Carlos G. Correa, Michael L. Littman, Jonathan D. Cohen, Thomas L. Griffiths
Control of mental representations in human planning. CoRR (2021)
Omer Gottesman, Kavosh Asadi, Cameron Allen, Sam Lobel, George Konidaris, Michael L. Littman
Coarse-Grained Smoothness for RL in Metric Spaces. CoRR (2021)
Yiheng Xie, Mingxuan Li, Shangqun Yu, Michael L. Littman
Learning Generalizable Behavior via Visual Rewrite Rules. CoRR (2021)
David Abel, Will Dabney, Anna Harutyunyan, Mark K. Ho, Michael L. Littman, Doina Precup, Satinder Singh
On the Expressivity of Markov Reward. NeurIPS (2021)
Homer Walke, Daniel Ritter, Carl Trimbach, Michael L. Littman
Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operator. CoRR (2021)
Ishaan Shah, David Halpern, Kavosh Asadi, Michael L. Littman
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback. CoRR (2021)
Michael L. Littman
Collusion rings threaten the integrity of computer science research. Commun. ACM 64 (6) (2021)
Sam Saarinen, Evan Cater, Michael L. Littman
Applying prerequisite structure inference to adaptive testing. LAK (2020)
David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman
Value Preserving State-Action Abstractions. AISTATS (2020)
Kavosh Asadi, Ronald E. Parr, George Dimitri Konidaris, Michael L. Littman
Deep RBF Value Functions for Continuous Control. CoRR (2020)
Guan Wang, Carl Trimbach, Jun Ki Lee, Mark K. Ho, Michael L. Littman
Teaching a Robot Tasks of Arbitrary Complexity via Human Feedback. HRI (2020)
Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths
The Efficiency of Human Cognition Reflects Planned Information Processing. CoRR (2020)
Lucas Lehnert, Michael L. Littman
Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning. J. Mach. Learn. Res. 21 (2020)
Mingxuan Li, Michael L. Littman
Towards Sample Efficient Agents through Algorithmic Alignment. CoRR (2020)
Zachary Horvitz, Nam Do, Michael L. Littman
Context-Driven Satirical News Generation. Fig-Lang@ACL (2020)
Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths
People Do Not Just Plan, They Plan to Plan. AAAI (2020)
Lefan Zhang, Weijia He, Olivia Morkved, Valerie Zhao, Michael L. Littman, Shan Lu, Blase Ur
Trace2TAP: Synthesizing Trigger-Action Programs from Traces of Behavior. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 4 (3) (2020)
Lucas Lehnert, Michael L. Littman, Michael J. Frank
Reward-predictive representations generalize across tasks in reinforcement learning. PLoS Comput. Biol. 16 (10) (2020)
Nishanth Kumar, Michael Fishman, Natasha Danas, Michael L. Littman, Stefanie Tellex, George Konidaris
Task Scoping: Building Goal-Specific Abstractions for Planning in Complex Domains. CoRR (2020)
Nishanth Kumar, Michael Fishman, Natasha Danas, Stefanie Tellex, Michael L. Littman, George Konidaris
Task Scoping for Efficient Planning in Open Worlds (Student Abstract). AAAI (2020)
Erwan Lecarpentier, David Abel, Kavosh Asadi, Yuu Jinnai, Emmanuel Rachelson, Michael L. Littman
Lipschitz Lifelong Reinforcement Learning. CoRR (2020)
Kavosh Asadi, David Abel, Michael L. Littman
Learning State Abstractions for Transfer in Continuous Control. CoRR (2020)
Kavosh Asadi, Dipendra Misra, Seungchan Kim, Michael L. Littman
Combating the Compounding-Error Problem with a Multi-step Model. CoRR (2019)
John R. Zech, Jessica Zosa Forde, Michael L. Littman
Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging. CoRR (2019)
Yuu Jinnai, David Abel, David Ellis Hershkowitz, Michael L. Littman, George Dimitri Konidaris
Finding Options that Minimize Planning Time. ICML (2019)
Michael Shum, Max Kleiman-Weiner, Michael L. Littman, Joshua B. Tenenbaum
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning. CoRR (2019)
David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong
State Abstraction as Compression in Apprenticeship Learning. AAAI (2019)
Robert T. Loftin, Bei Peng, Matthew E. Taylor, Michael L. Littman, David L. Roberts
Interactive Learning of Environment Dynamics for Sequential Tasks. CoRR (2019)
Michael Shum, Max Kleiman-Weiner, Michael L. Littman, Joshua B. Tenenbaum
Theory of Minds: Understanding Behavior in Groups through Inverse Planning. AAAI (2019)
Matt Cooper, Jun Ki Lee, Jacob Beck, Joshua D. Fishman, Michael Gillett, Zoë Papakipos, Aaron Zhang, Jerome Ramos, Aansh Shah, Michael L. Littman
Stackelberg Punishment and Bully-Proofing Autonomous Vehicles. CoRR (2019)
John Barr, Michael L. Littman, Marie desJardins
Decision trees. Inroads 10 (3) (2019)
David Abel, John Winder, Marie desJardins, Michael L. Littman
The Expected-Length Model of Options. IJCAI (2019)
Will Brackenbury, Abhimanyu Deora, Jillian Ritchey, Jason Vallee, Weijia He, Guan Wang, Michael L. Littman, Blase Ur
How Users Interpret Bugs in Trigger-Action Programming. CHI (2019)
Matt Cooper, Jun Ki Lee, Jacob Beck, Joshua D. Fishman, Michael Gillett, Zoë Papakipos, Aaron Zhang, Jerome Ramos, Aansh Shah, Michael L. Littman
Stackelberg Punishment and Bully-Proofing Autonomous Vehicles. ICSR (2019)
Lucas Lehnert, Michael L. Littman
Successor Features Support Model-based and Model-free Reinforcement Learning. CoRR (2019)
Seungchan Kim, Kavosh Asadi, Michael L. Littman, George Dimitri Konidaris
DeepMellow: Removing the Need for a Target Network in Deep Q-Learning. IJCAI (2019)
Seungchan Kim, Kavosh Asadi, Michael L. Littman, George Dimitri Konidaris
Removing the Target Network from Deep Q-Networks with the Mellowmax Operator. AAMAS (2019)
Carl Trimbach, Michael L. Littman
Teaching with IMPACT. CoRR (2019)
Dilip Arumugam, Jun Ki Lee, Sophie Saskin, Michael L. Littman
Deep Reinforcement Learning from Policy-Dependent Human Feedback. CoRR (2019)
Judah Newman, Bowen Wang, Valerie Zhao, Amy Zeng, Michael L. Littman, Blase Ur
Evidence Humans Provide When Explaining Data-Labeling Decisions. INTERACT (3) (2019)
Jacob Beck, Zoe Papakipos, Michael L. Littman
ReNeg and Backseat Driver: Learning from Demonstration with Continuous Human Feedback. CoRR (2019)
David Abel, Edward C. Williams, Stephen Brawner, Emily Reif, Michael L. Littman
Bandit-Based Solar Panel Control. AAAI (2018)
Kavosh Asadi, Evan Cater, Dipendra Misra, Michael L. Littman
Towards a Simple Approach to Multi-step Model-based Reinforcement Learning. CoRR (2018)
Yuu Jinnai, David Abel, Michael L. Littman, George Dimitri Konidaris
Finding Options that Minimize Planning Time. CoRR (2018)
Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman
Mitigating Planner Overfitting in Model-Based Reinforcement Learning. CoRR (2018)
Lucas Lehnert, Michael L. Littman
Transfer with Model Features in Reinforcement Learning. CoRR (2018)
Marie desJardins, Michael L. Littman
Evolutionary huffman encoding. Inroads 9 (2) (2018)
Sam Witty, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael L. Littman, David D. Jensen
Measuring and Characterizing Generalization in Deep Reinforcement Learning. CoRR (2018)
Bei Peng, James MacGlashan, Robert Tyler Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor
Curriculum Design for Machine Learners in Sequential Decision Tasks. IEEE Trans. Emerg. Top. Comput. Intell. 2 (4) (2018)
Mark K. Ho, Michael L. Littman, Fiery Cushman, Joseph L. Austerweil
Effectively Learning from Pedagogical Demonstrations. CogSci (2018)
Kavosh Asadi, Dipendra Misra, Michael L. Littman
Lipschitz Continuity in Model-based Reinforcement Learning. ICML (2018)
Kavosh Asadi, Dipendra Misra, Michael L. Littman
Lipschitz Continuity in Model-based Reinforcement Learning. CoRR (2018)
Kavosh Asadi, Evan Cater, Dipendra Misra, Michael L. Littman
Equivalence Between Wasserstein and Value-Aware Model-based Reinforcement Learning. CoRR (2018)
David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman
State Abstractions for Lifelong Reinforcement Learning. ICML (2018)
David Abel, Yuu Jinnai, Sophie Yue Guo, George Dimitri Konidaris, Michael L. Littman
Policy and Value Transfer in Lifelong Reinforcement Learning. ICML (2018)
Sam Saarinen, Evan Cater, Michael L. Littman
Personalized Education at Scale. CoRR (2018)
James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, Guan Wang, David L. Roberts, Matthew E. Taylor, Michael L. Littman
Interactive Learning from Policy-Dependent Human Feedback. ICML (2017)
Kavosh Asadi, Michael L. Littman
An Alternative Softmax Operator for Reinforcement Learning. ICML (2017)
Bei Peng, James MacGlashan, Robert T. Loftin, Michael L. Littman, David L. Roberts, Matthew E. Taylor
Curriculum Design for Machine Learners in Sequential Decision Tasks. AAMAS (2017)
Yuhang Song, Christopher Grimm, Xianming Wang, Michael L. Littman
Learning Approximate Stochastic Transition Models. CoRR (2017)
Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman
Latent Attention Networks. CoRR (2017)
David Abel, D. Ellis Hershkowitz, Michael L. Littman
Near Optimal Behavior via Approximate State Abstraction. CoRR (2017)
Nakul Gopalan, Marie desJardins, Michael L. Littman, James MacGlashan, Shawn Squire, Stefanie Tellex, John Winder, Lawson L. S. Wong
Planning with Abstract Markov Decision Processes. ICAPS (2017)
Lucas Lehnert, Stefanie Tellex, Michael L. Littman
Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning. CoRR (2017)
James MacGlashan, Mark K. Ho, Robert Tyler Loftin, Bei Peng, David L. Roberts, Matthew E. Taylor, Michael L. Littman
Interactive Learning from Policy-Dependent Human Feedback. CoRR (2017)
Christopher Grimm, Yuhang Song, Michael L. Littman
Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting. CoRR (2017)

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Publication Activity (10 Years)

Top Topics

Top Venues

Michael L. Littman