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ORCIDDilip Arumugam
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2020 – today
- 2024
[i23]Dilip Arumugam, Wanqiao Xu, Benjamin Van Roy:
Exploration Unbound. CoRR abs/2407.12178 (2024)- [i22]Dilip Arumugam, Saurabh Kumar, Ramki Gummadi, Benjamin Van Roy:
Satisficing Exploration for Deep Reinforcement Learning. CoRR abs/2407.12185 (2024) - 2023
- [c13]Ben Prystawski, Dilip Arumugam, Noah D. Goodman:
Cultural reinforcement learning: a framework for modeling cumulative culture on a limited channel. CogSci 2023 - [i21]Dilip Arumugam, Mark K. Ho
, Noah D. Goodman, Benjamin Van Roy:
Bayesian Reinforcement Learning with Limited Cognitive Load. CoRR abs/2305.03263 (2023) - [i20]Wanqiao Xu, Shi Dong, Dilip Arumugam, Benjamin Van Roy:
Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models. CoRR abs/2305.11455 (2023) - [i19]Akash Velu, Skanda Vaidyanath, Dilip Arumugam:
Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning. CoRR abs/2307.11897 (2023) - [i18]Jan-Philipp Fränken, Sam Kwok, Peixuan Ye, Kanishk Gandhi, Dilip Arumugam, Jared Moore, Alex Tamkin, Tobias Gerstenberg, Noah D. Goodman:
Social Contract AI: Aligning AI Assistants with Implicit Group Norms. CoRR abs/2310.17769 (2023) - 2022
- [c12]Dilip Arumugam, Satinder Singh:
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction. NeurIPS 2022 - [c11]Dilip Arumugam, Benjamin Van Roy:
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning. NeurIPS 2022 - [i17]Dilip Arumugam, Benjamin Van Roy:
Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning. CoRR abs/2206.02025 (2022) - [i16]Dilip Arumugam, Benjamin Van Roy:
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning. CoRR abs/2206.02072 (2022) - [i15]Dilip Arumugam, Satinder Singh:
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction. CoRR abs/2210.16872 (2022) - [i14]Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy:
On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning. CoRR abs/2210.16877 (2022) - [i13]Dilip Arumugam, Shi Dong, Benjamin Van Roy:
Inclusive Artificial Intelligence. CoRR abs/2212.12633 (2022) - 2021
- [c10]Dilip Arumugam, Benjamin Van Roy:
Deciding What to Learn: A Rate-Distortion Approach. ICML 2021: 373-382 - [c9]Dilip Arumugam, Benjamin Van Roy:
The Value of Information When Deciding What to Learn. NeurIPS 2021: 9816-9827 - [i12]Dilip Arumugam, Benjamin Van Roy:
Deciding What to Learn: A Rate-Distortion Approach. CoRR abs/2101.06197 (2021) - [i11]Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon:
An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning. CoRR abs/2103.06224 (2021) - [i10]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 abs/2110.03424 (2021) - [i9]Dilip Arumugam, Benjamin Van Roy:
The Value of Information When Deciding What to Learn. CoRR abs/2110.13973 (2021) - 2020
- [c8]David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman:
Value Preserving State-Action Abstractions. AISTATS 2020: 1639-1650 - [c7]Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin T. Feigelis, Daniel Yamins:
Flexible and Efficient Long-Range Planning Through Curious Exploration. ICML 2020: 2238-2249 - [i8]Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin T. Feigelis, Daniel Yamins:
Flexible and Efficient Long-Range Planning Through Curious Exploration. CoRR abs/2004.10876 (2020) - [i7]Dilip Arumugam, Debadeepta Dey, Alekh Agarwal, Asli Celikyilmaz, Elnaz Nouri, Bill Dolan:
Reparameterized Variational Divergence Minimization for Stable Imitation. CoRR abs/2006.10810 (2020) - [i6]Dilip Arumugam, Benjamin Van Roy:
Randomized Value Functions via Posterior State-Abstraction Sampling. CoRR abs/2010.02383 (2020)
2010 – 2019
- 2019
- [j1]Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Edward C. Williams, Mina Rhee, Lawson L. S. Wong, Stefanie Tellex:
Grounding natural language instructions to semantic goal representations for abstraction and generalization. Auton. Robots 43(2): 449-468 (2019) - [c6]David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong:
State Abstraction as Compression in Apprenticeship Learning. AAAI 2019: 3134-3142 - [i5]Dilip Arumugam, Jun Ki Lee, Sophie Saskin, Michael L. Littman:
Deep Reinforcement Learning from Policy-Dependent Human Feedback. CoRR abs/1902.04257 (2019) - 2018
- [c5]David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman:
State Abstractions for Lifelong Reinforcement Learning. ICML 2018: 10-19 - [c4]Nakul Gopalan, Dilip Arumugam, Lawson L. S. Wong, Stefanie Tellex:
Sequence-to-Sequence Language Grounding of Non-Markovian Task Specifications. Robotics: Science and Systems 2018 - [i4]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 abs/1812.01129 (2018) - 2017
- [c3]Siddharth Karamcheti, Edward C. Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex:
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions. RoboNLP@ACL 2017: 67-75 - [c2]Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex:
Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities. Robotics: Science and Systems 2017 - [i3]Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex:
Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities. CoRR abs/1704.06616 (2017) - [i2]Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman:
Latent Attention Networks. CoRR abs/1706.00536 (2017) - [i1]Siddharth Karamcheti, Edward C. Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex:
A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions. CoRR abs/1707.08668 (2017) - 2015
- [c1]James MacGlashan, Monica Babes-Vroman, Marie desJardins, Michael L. Littman, Smaranda Muresan, Shawn Squire, Stefanie Tellex, Dilip Arumugam, Lei Yang:
Grounding English Commands to Reward Functions. Robotics: Science and Systems 2015
Coauthor Index
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