Dilip Arumugam

According to our database, Dilip Arumugam authored at least 27 papers between 2015 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Social Contract AI: Aligning AI Assistants with Implicit Group Norms.

Jan-Philipp Fränken, Sam Kwok, Peixuan Ye, Kanishk Gandhi, Dilip Arumugam, Jared Moore, Alex Tamkin, Tobias Gerstenberg, Noah D. Goodman

CoRR, 2023

Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning.

Akash Velu, Skanda Vaidyanath, Dilip Arumugam

CoRR, 2023

Shattering the Agent-Environment Interface for Fine-Tuning Inclusive Language Models.

Wanqiao Xu, Shi Dong, Dilip Arumugam, Benjamin Van Roy

CoRR, 2023

Bayesian Reinforcement Learning with Limited Cognitive Load.

Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy

CoRR, 2023

2022

Inclusive Artificial Intelligence.

Dilip Arumugam, Shi Dong, Benjamin Van Roy

CoRR, 2022

On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning.

Dilip Arumugam, Mark K. Ho, Noah D. Goodman, Benjamin Van Roy

CoRR, 2022

Between Rate-Distortion Theory & Value Equivalence in Model-Based Reinforcement Learning.

Dilip Arumugam, Benjamin Van Roy

CoRR, 2022

Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning.

Dilip Arumugam, Benjamin Van Roy

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction.

Dilip Arumugam, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Bad-Policy Density: A Measure of Reinforcement Learning Hardness.

David Abel, Cameron Allen, Dilip Arumugam, D. Ellis Hershkowitz, Michael L. Littman, Lawson L. S. Wong

CoRR, 2021

An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning.

Dilip Arumugam, Peter Henderson, Pierre-Luc Bacon

CoRR, 2021

The Value of Information When Deciding What to Learn.

Dilip Arumugam, Benjamin Van Roy

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Deciding What to Learn: A Rate-Distortion Approach.

Dilip Arumugam, Benjamin Van Roy

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Randomized Value Functions via Posterior State-Abstraction Sampling.

Dilip Arumugam, Benjamin Van Roy

CoRR, 2020

Reparameterized Variational Divergence Minimization for Stable Imitation.

Dilip Arumugam, Debadeepta Dey, Alekh Agarwal, Asli Celikyilmaz, Elnaz Nouri, Bill Dolan

CoRR, 2020

Flexible and Efficient Long-Range Planning Through Curious Exploration.

Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin T. Feigelis, Daniel Yamins

Proceedings of the 37th International Conference on Machine Learning, 2020

Value Preserving State-Action Abstractions.

David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael L. Littman

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Deep Reinforcement Learning from Policy-Dependent Human Feedback.

Dilip Arumugam, Jun Ki Lee, Sophie Saskin, Michael L. Littman

CoRR, 2019

Grounding natural language instructions to semantic goal representations for abstraction and generalization.

Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Edward C. Williams, Mina Rhee, Lawson L. S. Wong, Stefanie Tellex

Auton. Robots, 2019

State Abstraction as Compression in Apprenticeship Learning.

David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Mitigating Planner Overfitting in Model-Based Reinforcement Learning.

Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman

CoRR, 2018

Sequence-to-Sequence Language Grounding of Non-Markovian Task Specifications.

Nakul Gopalan, Dilip Arumugam, Lawson L. S. Wong, Stefanie Tellex

Proceedings of the Robotics: Science and Systems XIV, 2018

State Abstractions for Lifelong Reinforcement Learning.

David Abel, Dilip Arumugam, Lucas Lehnert, Michael L. Littman

Proceedings of the 35th International Conference on Machine Learning, 2018

2017

Latent Attention Networks.

Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman

CoRR, 2017

Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities.

Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex

Proceedings of the Robotics: Science and Systems XIII, 2017

A Tale of Two DRAGGNs: A Hybrid Approach for Interpreting Action-Oriented and Goal-Oriented Instructions.

Siddharth Karamcheti, Edward C. Williams, Dilip Arumugam, Mina Rhee, Nakul Gopalan, Lawson L. S. Wong, Stefanie Tellex

Proceedings of the First Workshop on Language Grounding for Robotics, 2017

2015

Grounding English Commands to Reward Functions.

James MacGlashan, Monica Babes-Vroman, Marie desJardins, Michael L. Littman, Smaranda Muresan, Shawn Squire, Stefanie Tellex, Dilip Arumugam, Lei Yang

Proceedings of the Robotics: Science and Systems XI, Sapienza University of Rome, 2015