David Abel
22 papers · 2016–2025 · 7 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (30) π Conference Polyglot Β· Silver π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Bronze π£ Hot Topic Early Bird
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Keyword Champion
(6)
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Grand Slam
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Prolific Year Β· Productive
π₯
Unstoppable Β· Bronze
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The Questioner
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Century Club Β· Bronze
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Conference Pioneer
ποΈ
Keyword Collector
(98)
Top co-authors
Research topics
Machine Learning
(18)
Reinforcement Learning
(14)
Artificial Intelligence
(13)
Methods
(13)
Core AI
(12)
Optimization & Theory
(10)
Learning Types Β· Machine Learning
(9)
Applications
(4)
Core Methods
(3)
Learning Paradigms
(3)
Deep Learning
(2)
Learning Types Β· Deep Learning
(2)
Interdisciplinary
(1)
Application Areas
(1)
Cognitive Science
(1)
Keywords
reinforcement learning
(10)
state abstraction
(6)
value function
(4)
lifelong reinforcement learning
(3)
markov decision process
(3)
transfer learning
(2)
optimal policy
(2)
option discovery
(2)
hierarchical abstraction
(2)
task specification
(2)
lifelong learning
(2)
pac learning
(2)
information theory
(2)
markov reward
(2)
information bottleneck
(1)
policy optimization
(1)
risk management
(1)
learning theory
(1)
deep reinforcement learning
(1)
knowledge transfer
(1)
Papers
A Black Swan Hypothesis: The Role of Human Irrationality in AI Safety
ICLR 2025
General agents need world models
ICML 2025
Optimizing Return Distributions with Distributional Dynamic Programming
JMLR 2025
Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning
ICLR 2025
Pragmatic Feature Preferences: Learning Reward-Relevant Preferences from Human Input
ICML 2024
A Definition of Continual Reinforcement Learning
NeurIPS 2023
Settling the Reward Hypothesis
ICML 2023
On the Expressivity of Markov Reward (Extended Abstract)
IJCAI 2022
Lipschitz Lifelong Reinforcement Learning
AAAI 2021
On the Expressivity of Markov Reward
NeurIPS 2021
Revisiting Pengβs Q($Ξ»$) for Modern Reinforcement Learning
ICML 2021
People Do Not Just Plan,They Plan to Plan
AAAI 2020
Value Preserving State-Action Abstractions
AISTATS 2020
What can I do here? A Theory of Affordances in Reinforcement Learning
ICML 2020
A Theory of State Abstraction for Reinforcement Learning
AAAI 2019
Discovering Options for Exploration by Minimizing Cover Time
ICML 2019
Finding Options that Minimize Planning Time
ICML 2019
State Abstraction as Compression in Apprenticeship Learning
AAAI 2019
The Expected-Length Model of Options
IJCAI 2019
Policy and Value Transfer in Lifelong Reinforcement Learning
ICML 2018
State Abstractions for Lifelong Reinforcement Learning
ICML 2018
Near Optimal Behavior via Approximate State Abstraction
ICML 2016