Bethany R. Leffler

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4ranked-venue papers

1as first author

0since 2021 · last 2009

0009-0003-0640-8752ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 4 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence

3 papers

Reinforcement learning · 56% Learning theory · 44%

Topics — the 4 heaviest of 5, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Reinforcement learning factored reinforcement learning	0.1	1	2009	The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning · ICML 2009
Machine learning › Learning theory › online learning › online learning theory KWIK learning	0.1	1	2009	The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning · ICML 2009
Machine learning › Learning theory PAC learning	0.1	1	2009	Provably Efficient Learning with Typed Parametric Models · J. Mach. Learn. Res. 2009
Machine learning › Reinforcement learning › exploration efficient exploration	0.1	1	2007	Efficient Reinforcement Learning with Relocatable Action Models · AAAI 2007

Methods — techniques the papers use, named apart from their topics

sample complexity analysis · 0.1parametric model · 0.1adaptive k-meteorologists algorithm · 0.1action model learning · 0.1

Year	Publication	Venue	Position
2009	The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning abstract The purpose of this paper is three-fold. First, we formalize and study a problem of learning probabilistic concepts in the recently proposed KWIK framework. We give details of an algorithm, known as the Adaptive k-Meteorologists Algorithm, analyze its sample-complexity upper bound, and give a matching lower bound. Second, this algorithm is used to create a new reinforcement-learning algorithm for factored-state problems that enjoys significant improvement over the previous state-of-the-art algorithm. Finally, we apply the Adaptive k-Meteorologists Algorithm to remove a limiting assumption in an existing reinforcement-learning algorithm. The effectiveness of our approaches is demonstrated empirically in a couple benchmark domains as well as a robotics navigation problem. Carlos Diuk, Lihong Li 0001, Bethany R. Leffler	ICML	3
2009	Provably Efficient Learning with Typed Parametric Models Emma Brunskill, Bethany R. Leffler, Lihong Li 0001, Michael L. Littman, Nicholas Roy	J. Mach. Learn. Res.	2
2008	CORL: A Continuous-state Offset-dynamics Reinforcement Learner Emma Brunskill, Bethany R. Leffler, Lihong Li 0001, Michael L. Littman, Nicholas Roy	UAI	2
2007	Efficient Reinforcement Learning with Relocatable Action Models Bethany R. Leffler, Michael L. Littman, Timothy Edmunds	AAAI	1