Dilip Arumugam
12 papers · 2015–2025 · 6 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+9 more ↓ Show less ↑
πΊοΈ Taxonomy Completionist (33) π Academic Marathon Β· Half π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Gold π£ Hot Topic Early Bird
π
Keyword Champion
(4)
π§
Keyword Pioneer
π§¬
Topic Evolution
π
Trend Setter
π₯
Unstoppable Β· Bronze
π
Conference Pioneer
π
Renaissance Researcher
(5)
π
Century Club Β· Bronze
ποΈ
Keyword Collector
(66)
Top co-authors
Research topics
Artificial Intelligence
(11)
Core AI
(11)
Reinforcement Learning
(7)
Methods
(7)
Machine Learning
(6)
Learning Types Β· Machine Learning
(6)
Optimization & Theory
(5)
Deep Learning
(3)
Learning Types Β· Deep Learning
(3)
Natural Language Processing
(2)
Applications Β· Reinforcement Learning
(2)
Applications Β· Natural Language Processing
(2)
Core Methods
(2)
Learning Paradigms
(2)
Mathematics & Optimization
(1)
Keywords
state abstraction
(4)
rate-distortion theory
(4)
reinforcement learning
(3)
natural language understanding
(2)
imitation learning
(2)
language grounding
(2)
markov decision process
(2)
exploration exploitation
(2)
sequential decision making
(2)
transfer learning
(2)
robot manipulation
(2)
natural language grounding
(2)
hierarchical planning
(1)
instruction following
(1)
pac learning
(1)
information bottleneck
(1)
exploration-exploitation tradeoff
(1)
sample complexity
(1)
regret minimization
(1)
robot learning
(1)
Papers
Hindsight Merging: Diverse Data Generation with Language Models
UAI 2025
Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning
NeurIPS 2022
Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction
NeurIPS 2022
Deciding What to Learn: A Rate-Distortion Approach
ICML 2021
The Value of Information When Deciding What to Learn
NeurIPS 2021
Flexible and Efficient Long-Range Planning Through Curious Exploration
ICML 2020
Value Preserving State-Action Abstractions
AISTATS 2020
State Abstraction as Compression in Apprenticeship Learning
AAAI 2019
Sequence-to-Sequence Language Grounding of Non-Markovian Task Specifications
RSS 2018
State Abstractions for Lifelong Reinforcement Learning
ICML 2018
Accurately and Efficiently Interpreting Human-Robot Instructions of Varying Granularities
RSS 2017
Grounding English Commands to Reward Functions
RSS 2015