Arumugam, Dilip

1. Deciding What to Learn: A Rate-Distortion Approach
   2021/01/15 by Dilip Arumugam, Arumugam, Dilip, Benjamin Van Roy +1 · 3 citations
   Decision Sciences · Computer Science · #Advanced Bandit Algorithms Research #Reinforcement Learning in Robotics #Machine Learning and Algorithms
2. Deep Reinforcement Learning from Policy-Dependent Human Feedback
   2019/02/12 by Arumugam, Dilip, Lee, Jun Ki, Saskin, Sophie +1 · 1 citation
   #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
3. An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning
   2021/03/10 by Dilip Arumugam, Peter Henderson, Arumugam, Dilip +3 · 1 citation
   Computer Science · Decision Sciences · Engineering · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Smart Grid Energy Management
4. Bayesian Reinforcement Learning with Limited Cognitive Load
   2023/05/05 by Arumugam, Dilip, Ho, Mark K., Goodman, Noah D. +1 · 2 citations
   #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG)
5. The Value of Information When Deciding What to Learn
   2021/10/26 by Dilip Arumugam, Benjamin Van Roy, Arumugam, Dilip +1 · 1 citation
   Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning and Algorithms #Reinforcement Learning in Robotics
6. Toward Efficient Exploration by Large Language Model Agents
   2025/04/29 by Dilip Arumugam, Arumugam, Dilip, Thomas L. Griffiths +1 · 4 citations
   Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multimodal Machine Learning Applications #Natural Language Processing Techniques #Speech and dialogue systems
7. Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning
   2022/06/04 by Arumugam, Dilip, Van Roy, Benjamin · 1 citation
   #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning (stat.ML)
8. Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems
   2025/05/23 by Geng, Jiayi, Chen, Howard, Arumugam, Dilip +1 · 3 citations
   #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG)
9. Using Reinforcement Learning to Train Large Language Models to Explain Human Decisions
   2025/05/16 by Jian-Qiao Zhu, Hanbo Xie, Zhu, Jian-Qiao +7 · 1 voice · 3 citations
   Computer Science · Medicine · #Explainable Artificial Intelligence (XAI) #Multimodal Machine Learning Applications #Artificial Intelligence in Healthcare and Education
10. On Rate-Distortion Theory in Capacity-Limited Cognition & Reinforcement Learning
    2022/10/30 by Dilip Arumugam, Arumugam, Dilip, Mark K. Ho +5 · 1 citation
    Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics