VLDB 2026 Research / reviewers in the wild / expert

Casey Chu

dblp:211/7167 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 2020
—ORCID · none

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

Artificial intelligence and machine learning · 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
Generative modeling · 65% Learning theory · 23% Probabilistic and Bayesian machine learning · 6%

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

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling
generative adversarial network		 0.822020
Smoothness and Stability in GANs · ICLR 2020
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning · ICML 2019
Machine learning › Generative modeling › generative adversarial network › GAN training
GAN training stability		 0.412020
Smoothness and Stability in GANs · ICLR 2020
Machine learning › Reinforcement learning
actor-critic methods		 0.112019
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning · ICML 2019
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference › approximate inference
variational inference		 0.112019
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning · ICML 2019

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

probability functional descent · 0.4functional minimization · 0.4
YearPublicationVenuePosition
2020 Smoothness and Stability in GANs
Casey Chu, Kentaro Minami, Kenji Fukumizu
 ICLR1
2019 Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning
abstract
The goal of this paper is to provide a unifying view of a wide range of problems of interest in machine learning by framing them as the minimization of functionals defined on the space of probability measures. In particular, we show that generative adversarial networks, variational inference, and actor-critic methods in reinforcement learning can all be seen through the lens of our framework. We then discuss a generic optimization algorithm for our formulation, called probability functional descent (PFD), and show how this algorithm recovers existing methods developed independently in the settings mentioned earlier.
Casey Chu, Jose H. Blanchet, Peter W. Glynn
 ICML1