Arumugam, Dilip

13 publications

UAI 2025 Hindsight Merging: Diverse Data Generation with Language Models Veniamin Veselovsky, Benedikt Stroebl, Gianluca Bencomo, Dilip Arumugam, Lisa Schut, Arvind Narayanan, Thomas L. Griffiths

NeurIPSW 2023 Social Contract AI: Aligning AI Assistants with Implicit Group Norms Jan-Philipp Fränken, Samuel Kwok, Peixuan Ye, Kanishk Gandhi, Dilip Arumugam, Jared Moore, Alex Tamkin, Tobias Gerstenberg, Noah Goodman

NeurIPS 2022 Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning Dilip Arumugam, Benjamin Van Roy

ICMLW 2022 Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning Dilip Arumugam, Benjamin Van Roy

NeurIPSW 2022 In the ZONE: Measuring Difficulty and Progression in Curriculum Generation Rose E Wang, Jesse Mu, Dilip Arumugam, Natasha Jaques, Noah Goodman

NeurIPSW 2022 On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning Dilip Arumugam, Mark K Ho, Noah Goodman, Benjamin Van Roy

NeurIPS 2022 Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction Dilip Arumugam, Satinder P. Singh

ICML 2021 Deciding What to Learn: A Rate-Distortion Approach Dilip Arumugam, Benjamin Van Roy

NeurIPS 2021 The Value of Information When Deciding What to Learn Dilip Arumugam, Benjamin Van Roy

ICML 2020 Flexible and Efficient Long-Range Planning Through Curious Exploration Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin Feigelis, Daniel Yamins

AISTATS 2020 Value Preserving State-Action Abstractions David Abel, Nate Umbanhowar, Khimya Khetarpal, Dilip Arumugam, Doina Precup, Michael Littman

AAAI 2019 State Abstraction as Compression in Apprenticeship Learning David Abel, Dilip Arumugam, Kavosh Asadi, Yuu Jinnai, Michael L. Littman, Lawson L. S. Wong

ICML 2018 State Abstractions for Lifelong Reinforcement Learning David Abel, Dilip Arumugam, Lucas Lehnert, Michael Littman