ML
M. L. Littman
cs.AIcs.LGstat.MLLearningComputational Modelingcs.GTcs.HCMathematical PsychologyQuantitative PsychologySocial Cognition
On Valency
published · living versionsW_q9adsexk·v1 · currentpublished
Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions
with J. A. Csirik, D. McAllester, R. E. Schapire, P. Stone
1 version
Preprints & journals
94 papers in the corpus · 1978–2026Showing versus Doing: Teaching by Demonstration2610.14004v1 · Mark K. Ho, Michael L. Littman, James MacGlashan et al.2026 · 71 citationsAdvances in Neural Information Processing Systems 29 (NIPS 2016)
Shared Lexical Task Representations Explain Behavioral Variability In LLMs2604.22027v2 · Zhuonan Yang, Jacob Xiaochen Li, Francisco Piedrahita Velez et al.2026 · 0 citationsarXiv
AI+HW 2035: Shaping the Next Decade2603.05225v1 · Deming Chen, Jason Cong, Azalia Mirhoseini et al.2026 · 0 citationsarXiv
Preparing medical students for the future of AI in medicine.41794208 · Gruppuso, Philip A, Littman, Michael L, Oster, Emily2026 · 0 citationsThe American journal of medicine. 2026;139(9):1106-1107
Knowledge Retention for Continual Model-Based Reinforcement Learning2503.04256v4 · Yixiang Sun, Haotian Fu, Michael Littman et al.2025 · 0 citationsarXiv
Mitigating Partial Observability in Sequential Decision Processes via the Lambda Discrepancy2407.07333v3 · Cameron Allen, Aaron Kirtland, Ruo Yu Tao et al.2024 · 2 citationsarXiv
Planetarium: A Rigorous Benchmark for Translating Text to Structured Planning Languages2407.03321v2 · Max Zuo, Francisco Piedrahita Velez, Xiaochen Li et al.2024 · 5 citationsProceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
Computably Continuous Reinforcement-Learning Objectives are PAC-learnable2303.05518v2 · Cambridge Yang, Michael Littman, Michael Carbin2023 · 1 citationarXiv
A domain-agnostic approach for characterization of lifelong learning systems.36709531 · Baker, Megan M, New, Alexander, Aguilar-Simon, Mario et al.2023 · 17 citationsNeural networks : the official journal of the International Neural Network Society. 2023;160:274-296
Tiered Reward: Designing Rewards for Specification and Fast Learning of Desired Behavior2212.03733v3 · Zhiyuan Zhou, Shreyas Sundara Raman, Henry Sowerby et al.2022 · 0 citationsReinforcement Learning Journal, vol. 1, no. 1, 2024, pp. TBD
Evaluation Beyond Task Performance: Analyzing Concepts in AlphaZero in Hex2211.14673v1 · Charles Lovering, Jessica Zosa Forde, George Konidaris et al.2022 · 8 citationsarXiv
Reward-Predictive Clustering2211.03281v1 · Lucas Lehnert, Michael J. Frank, Michael L. Littman2022 · 0 citationsarXiv
Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report2210.15767v1 · Michael L. Littman, Ifeoma Ajunwa, Guy Berger et al.2022 · 142 citationsarXiv
Model-based Lifelong Reinforcement Learning with Bayesian Exploration2210.11579v1 · Haotian Fu, Shangqun Yu, Michael Littman et al.2022 · 1 citationarXiv
Meta-Learning Parameterized Skills2206.03597v4 · Haotian Fu, Shangqun Yu, Saket Tiwari et al.2022 · 2 citationsarXiv
Designing Rewards for Fast Learning2205.15400v1 · Henry Sowerby, Zhiyuan Zhou, Michael L. Littman2022 · 10 citationsarXiv
People construct simplified mental representations to plan.35589843 · Ho, Mark K, Abel, David, Correa, Carlos G et al.2022 · 113 citationsNature. 2022;606(7912):129-136
Does DQN really learn? Exploring adversarial training schemes in Pong2203.10614v1 · Bowen He, Sreehari Rammohan, Jessica Forde et al.2022 · 0 citationsarXiv
Faster Deep Reinforcement Learning with Slower Online Network2112.05848v3 · Kavosh Asadi, Rasool Fakoor, Omer Gottesman et al.2021 · 1 citationarXiv
Learning Generalizable Behavior via Visual Rewrite Rules2112.05218v1 · Yiheng Xie, Mingxuan Li, Shangqun Yu et al.2021 · 1 citationarXiv
On the (In)Tractability of Reinforcement Learning for LTL Objectives2111.12679v3 · Cambridge Yang, Michael Littman, Michael Carbin2021 · 13 citationsarXiv
Learning Finite Linear Temporal Logic Specifications with a Specialized Neural Operator2111.04147v2 · Homer Walke, Daniel Ritter, Carl Trimbach et al.2021 · 0 citationsarXiv
On the Expressivity of Markov Reward2111.00876v2 · David Abel, Will Dabney, Anna Harutyunyan et al.2021 · 13 citationsarXiv
Coarse-Grained Smoothness for RL in Metric Spaces2110.12276v1 · Omer Gottesman, Kavosh Asadi, Cameron Allen et al.2021 · 1 citationarXiv
Bad-Policy Density: A Measure of Reinforcement Learning Hardness2110.03424v1 · David Abel, Cameron Allen, Dilip Arumugam et al.2021 · 0 citationsarXiv
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback2109.07054v1 · Ishaan Shah, David Halpern, Kavosh Asadi et al.2021 · 0 citationsarXiv
Communication in action: Planning and interpreting communicative demonstrations.34498911 · Ho, Mark K, Cushman, Fiery, Littman, Michael L et al.2022 · 37 citationsJournal of experimental psychology. General. 2021;150(11):2246-2272
Brittle AI, Causal Confusion, and Bad Mental Models: Challenges and Successes in the XAI Program2106.05506v1 · Jeff Druce, James Niehaus, Vanessa Moody et al.2021 · 8 citationsarXiv
Model Selection's Disparate Impact in Real-World Deep Learning Applications2104.00606v2 · Jessica Zosa Forde, A. Feder Cooper, Kweku Kwegyir-Aggrey et al.2021 · 9 citationsarXiv
Task Scoping: Generating Task-Specific Abstractions for Planning in Open-Scope Models2010.08869v3 · Michael Fishman, Nishanth Kumar, Cameron Allen et al.2020 · 0 citationsarXiv
Reward-predictive representations generalize across tasks in reinforcement learning.33057329 · Lehnert, Lucas, Littman, Michael L, Frank, Michael J2021 · 42 citationsPLoS computational biology. 2020;16(10):e1008317
Towards Sample Efficient Agents through Algorithmic Alignment2008.03229v5 · Mingxuan Li, Michael L. Littman2020 · 0 citationsarXiv
The Efficiency of Human Cognition Reflects Planned Information Processing2002.05769v1 · Mark K. Ho, David Abel, Jonathan D. Cohen et al.2020 · 10 citationsarXiv
Learning State Abstractions for Transfer in Continuous Control2002.05518v1 · Kavosh Asadi, David Abel, Michael L. Littman2020 · 4 citationsarXiv
Deep Radial-Basis Value Functions for Continuous Control2002.01883v2 · Kavosh Asadi, Neev Parikh, Ronald E. Parr et al.2020 · 16 citationsarXiv
Lipschitz Lifelong Reinforcement Learning2001.05411v3 · Erwan Lecarpentier, David Abel, Kavosh Asadi et al.2020 · 6 citationsarXiv
Individual predictions matter: Assessing the effect of data ordering in training fine-tuned CNNs for medical imaging1912.03606v1 · John R. Zech, Jessica Zosa Forde, Michael L. Littman2019 · 2 citationsarXiv
Stackelberg Punishment and Bully-Proofing Autonomous Vehicles1908.08641v1 · Matt Cooper, Jun Ki Lee, Jacob Beck et al.2019 · 7 citationsarXiv
Interactive Learning of Environment Dynamics for Sequential Tasks1907.08478v1 · Robert Loftin, Bei Peng, Matthew E. Taylor et al.2019 · 0 citationsarXiv
The Value of Abstraction6fm9a_v1 · Mark K Ho, David Abel, Tom Griffiths et al.2019 · 29 citationsPsyArXiv
Teaching with IMPACT1903.06209v1 · Carl Trimbach, Michael Littman2019 · 0 citationsarXiv
People teach with rewards and punishments as communication, not reinforcements.30802127 · Ho, Mark K, Cushman, Fiery, Littman, Michael L et al.2019 · 51 citationsJournal of experimental psychology. General. 2019;148(3):520-549
Deep Reinforcement Learning from Policy-Dependent Human Feedback1902.04257v1 · Dilip Arumugam, Jun Ki Lee, Sophie Saskin et al.2019 · 49 citationsarXiv
Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning1901.11437v3 · Lucas Lehnert, Michael L. Littman2019 · 22 citationsarXiv
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning1901.06085v1 · Michael Shum, Max Kleiman-Weiner, Michael L. Littman et al.2019 · 61 citationsarXiv
ReNeg and Backseat Driver: Learning from Demonstration with Continuous Human Feedback1901.05101v1 · Jacob Beck, Zoe Papakipos, Michael Littman2019 · 1 citationarXiv
Measuring and Characterizing Generalization in Deep Reinforcement Learning1812.02868v2 · Sam Witty, Jun Ki Lee, Emma Tosch et al.2018 · 35 citationsarXiv
Mitigating Planner Overfitting in Model-Based Reinforcement Learning1812.01129v2 · Dilip Arumugam, David Abel, Kavosh Asadi et al.2018 · 2 citationsarXiv
Towards a Simple Approach to Multi-step Model-based Reinforcement Learning1811.00128v1 · Kavosh Asadi, Evan Cater, Dipendra Misra et al.2018 · 6 citationsarXiv
Finding Options that Minimize Planning Time1810.07311v3 · Yuu Jinnai, David Abel, D Ellis Hershkowitz et al.2018 · 15 citationsarXiv
Career total: 475 works. 94 are in this corpus.Showing the 50 most recent.
Profile built from the corpus for this byline.
Author records are still filling in while the Hub is in alpha. If this is your page, you'll be able to claim it soon. Spot a mistake? Tell us.