Dilip Arumugam

According to our database1, Dilip Arumugam authored at least 16 papers between 2015 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2021
An Information-Theoretic Perspective on Credit Assignment in Reinforcement Learning.
CoRR, 2021

Deciding What to Learn: A Rate-Distortion Approach.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Randomized Value Functions via Posterior State-Abstraction Sampling.
CoRR, 2020

Reparameterized Variational Divergence Minimization for Stable Imitation.
CoRR, 2020

Flexible and Efficient Long-Range Planning Through Curious Exploration.
Proceedings of the 37th International Conference on Machine Learning, 2020

Value Preserving State-Action Abstractions.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
Deep Reinforcement Learning from Policy-Dependent Human Feedback.
CoRR, 2019

Grounding natural language instructions to semantic goal representations for abstraction and generalization.
Auton. Robots, 2019

State Abstraction as Compression in Apprenticeship Learning.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Mitigating Planner Overfitting in Model-Based Reinforcement Learning.
CoRR, 2018

Sequence-to-Sequence Language Grounding of Non-Markovian Task Specifications.
Proceedings of the Robotics: Science and Systems XIV, 2018

State Abstractions for Lifelong Reinforcement Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Latent Attention Networks.
CoRR, 2017

Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities.
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.
Proceedings of the First Workshop on Language Grounding for Robotics, 2017

2015
Grounding English Commands to Reward Functions.
Proceedings of the Robotics: Science and Systems XI, Sapienza University of Rome, 2015


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