Dilip Arumugam

46 papers A* 7A 2B 3Journal 30Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Cognitive Models and AI Algorithms Provide Templates for Designing Language Agents. CoRR Ryan Liu, Dilip Arumugam, Cedegao E. Zhang, Sean Escola, Xaq Pitkow, Thomas L. Griffiths
2025	J	`jnl`	Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems. CoRR Jiayi Geng, Howard Chen, Dilip Arumugam, Thomas L. Griffiths
2025	J	`jnl`	Demystifying the Mechanisms Behind Emergent Exploration in Goal-conditioned RL. CoRR Mahsa Bastankhah, Grace Liu, Dilip Arumugam, Thomas L. Griffiths, Benjamin Eysenbach
2025	B	`conf`	Discovering Hidden Laws in Innovation by Recombination. CogSci Bonan Zhao, Elizabeth Mieczkowski, Dilip Arumugam, Natalia Vélez, Tom Griffiths
2025	A	`conf`	Hindsight Merging: Diverse Data Generation with Language Models. UAI Veniamin Veselovsky, Benedikt Stroebl, Gianluca M. Bencomo, Dilip Arumugam, Lisa Schut, Arvind Narayanan, Thomas L. Griffiths
2025	J	`jnl`	Lossy communication constrains iterated learning. CoRR Ben Prystawski, Dilip Arumugam, Noah D. Goodman
2025	J	`jnl`	Toward Efficient Exploration by Large Language Model Agents. CoRR Dilip Arumugam, Thomas L. Griffiths
2025	B	`conf`	Trade-Offs Between Tasks Induced by Capacity Constraints Bound the Scope of Intelligence. CogSci Cameron Rouse Turner, Dilip Arumugam, Logan Nelson, Tom Griffiths
2025	J	`jnl`	Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions. CoRR Jian-Qiao Zhu, Hanbo Xie, Dilip Arumugam, Robert C. Wilson, Thomas L. Griffiths
2024	J	`jnl`	Exploration Unbound. CoRR Dilip Arumugam, Wanqiao Xu, Benjamin Van Roy
2024	J	`jnl`	Satisficing Exploration for Deep Reinforcement Learning. CoRR Dilip Arumugam, Saurabh Kumar, Ramki Gummadi, Benjamin Van Roy
2023	J	`jnl`	Bayesian Reinforcement Learning with Limited Cognitive Load. CoRR Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy
2023	B	`conf`	Cultural reinforcement learning: a framework for modeling cumulative culture on a limited channel. CogSci Ben Prystawski, Dilip Arumugam, Noah D. Goodman
2023	J	`jnl`	Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning. CoRR Akash Velu, Skanda Vaidyanath, Dilip Arumugam
2023	J	`jnl`	Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models. CoRR Wanqiao Xu, Shi Dong, Dilip Arumugam, Benjamin Van Roy
2023	J	`jnl`	Social Contract AI: Aligning AI Assistants with Implicit Group Norms. CoRR Jan-Philipp Fränken, Sam Kwok, Peixuan Ye, Kanishk Gandhi, Dilip Arumugam, Jared Moore, Alex Tamkin, Tobias Gerstenberg, Noah D. Goodman
2022	J	`jnl`	Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning. CoRR Dilip Arumugam, Benjamin Van Roy
2022	A*	`conf`	Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning. NeurIPS Dilip Arumugam, Benjamin Van Roy
2022	J	`jnl`	Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning. CoRR Dilip Arumugam, Benjamin Van Roy
2022	J	`jnl`	Inclusive Artificial Intelligence. CoRR Dilip Arumugam, Shi Dong, Benjamin Van Roy
2022	J	`jnl`	On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning. CoRR Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy
2022	A*	`conf`	Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction. NeurIPS Dilip Arumugam, Satinder Singh
2022	J	`jnl`	Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction. CoRR Dilip Arumugam, Satinder Singh
2021	J	`jnl`	An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning. CoRR Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon
2021	J	`jnl`	Bad-Policy Density: A Measure of Reinforcement Learning Hardness. CoRR David Abel, Cameron Allen, Dilip Arumugam, D. Ellis Hershkowitz, Michael L. Littman, Lawson L. S. Wong
2021	A*	`conf`	Deciding What to Learn: A Rate-Distortion Approach. ICML Dilip Arumugam, Benjamin Van Roy
2021	J	`jnl`	Deciding What to Learn: A Rate-Distortion Approach. CoRR Dilip Arumugam, Benjamin Van Roy
2021	A*	`conf`	The Value of Information When Deciding What to Learn. NeurIPS Dilip Arumugam, Benjamin Van Roy
2021	J	`jnl`	The Value of Information When Deciding What to Learn. CoRR Dilip Arumugam, Benjamin Van Roy
2020	A*	`conf`	Flexible and Efficient Long-Range Planning Through Curious Exploration. ICML Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin T. Feigelis, Daniel Yamins
2020	J	`jnl`	Flexible and Efficient Long-Range Planning Through Curious Exploration. CoRR Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin T. Feigelis, Daniel Yamins
2020	J	`jnl`	Randomized Value Functions via Posterior State-Abstraction Sampling. CoRR Dilip Arumugam, Benjamin Van Roy
2020	J	`jnl`	Reparameterized Variational Divergence Minimization for Stable Imitation. CoRR Dilip Arumugam, Debadeepta Dey, Alekh Agarwal, Asli Celikyilmaz, Elnaz Nouri, Bill Dolan
2020	A	`conf`	Value Preserving State-Action Abstractions. AISTATS David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman
2019	J	`jnl`	Deep Reinforcement Learning from Policy-Dependent Human Feedback. CoRR Dilip Arumugam, Jun Ki Lee, Sophie Saskin, Michael L. Littman
2019	J	`jnl`	Grounding natural language instructions to semantic goal representations for abstraction and generalization. Auton. Robots Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Edward C. Williams, Mina Rhee, Lawson L. S. Wong, Stefanie Tellex
2019	A*	`conf`	State Abstraction as Compression in Apprenticeship Learning. AAAI David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong
2018	J	`jnl`	Mitigating Planner Overfitting in Model-Based Reinforcement Learning. CoRR Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman
2018	—	`conf`	Sequence-to-Sequence Language Grounding of Non-Markovian Task Specifications. Robotics: Science and Systems Nakul Gopalan, Dilip Arumugam, Lawson L. S. Wong, Stefanie Tellex
2018	A*	`conf`	State Abstractions for Lifelong Reinforcement Learning. ICML David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman
2017	—	`conf`	A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions. RoboNLP@ACL Siddharth Karamcheti, Edward C. Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex
2017	J	`jnl`	A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions. CoRR Siddharth Karamcheti, Edward C. Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex
2017	—	`conf`	Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities. Robotics: Science and Systems Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex
2017	J	`jnl`	Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities. CoRR Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex
2017	J	`jnl`	Latent Attention Networks. CoRR Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman
2015	—	`conf`	Grounding English Commands to Reward Functions. Robotics: Science and Systems James MacGlashan, Monica Babes-Vroman, Marie desJardins, Michael L. Littman, Smaranda Muresan, Shawn Squire, Stefanie Tellex, Dilip Arumugam, Lei Yang