Sherry Shanshan Ruan

EDBT 2026 Demo / reviewers in the wild / expert

dblp:160/9935 · DBLP profile ↗

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2ranked-venue papers

2as first author

0since 2021 · last 2015

—ORCID · none

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

Artificial intelligence and machine learning · 2 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 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

2 papers

Reinforcement learning · 100%

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

Topic	Weight	Papers	Last	Evidence papers
Machine learning › Reinforcement learning › function approximation › representation learning for reinforcement learning › state abstraction bisimulation metrics	0.4	2	2015	Representation Discovery for MDPs Using Bisimulation Metrics · AAAI 2015 Representation Discovery for MDPs Using Bisimulation Metrics · AAAI 2015
Machine learning › Reinforcement learning markov decision process	0.4	2	2015	Representation Discovery for MDPs Using Bisimulation Metrics · AAAI 2015 Representation Discovery for MDPs Using Bisimulation Metrics · AAAI 2015
Machine learning › Reinforcement learning › function approximation › representation learning for reinforcement learning state representation	0.4	2	2015	Representation Discovery for MDPs Using Bisimulation Metrics · AAAI 2015 Representation Discovery for MDPs Using Bisimulation Metrics · AAAI 2015
Machine learning › Reinforcement learning › function approximation › representation learning for reinforcement learning state abstraction	0.1	2	2015	Representation Discovery for MDPs Using Bisimulation Metrics · AAAI 2015 Representation Discovery for MDPs Using Bisimulation Metrics · AAAI 2015

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

state space partitioning · 0.4iterative refinement · 0.4

Year	Publication	Venue	Position
2015	Representation Discovery for MDPs Using Bisimulation Metrics abstract We provide a novel, flexible, iterative refinement algorithm to automatically construct an approximate statespace representation for Markov Decision Processes (MDPs). Our approach leverages bisimulation metrics, which have been used in prior work to generate features to represent the state space of MDPs. We address a drawback of this approach, which is the expensive computation of the bisimulation metrics. We propose an algorithm to generate an iteratively improving sequence of state space partitions. Partial metric computations guide the representation search and provide much lower space and computational complexity, while maintaining strong convergence properties. We provide theoretical results guaranteeing convergence as well as experimental illustrations of the accuracy and savings (in time and memory usage) of the new algorithm, compared to traditional bisimulation metric computation. Sherry Shanshan Ruan, Gheorghe Comanici, Prakash Panangaden, Doina Precup	AAAI	1
2015	Representation Discovery for MDPs Using Bisimulation Metrics abstract We provide a novel, flexible, iterative refinement algorithm to automatically construct an approximate statespace representation for Markov Decision Processes (MDPs). Our approach leverages bisimulation metrics, which have been used in prior work to generate features to represent the state space of MDPs.We address a drawback of this approach, which is the expensive computation of the bisimulation metrics. We propose an algorithm to generate an iteratively improving sequence of state space partitions. Partial metric computations guide the representation search and provide much lower space and computational complexity, while maintaining strong convergence properties. We provide theoretical results guaranteeing convergence as well as experimental illustrations of the accuracy and savings (in time and memory usage) of the new algorithm, compared to traditional bisimulation metric computation. Sherry Shanshan Ruan, Gheorghe Comanici, Prakash Panangaden, Doina Precup	AAAI	1