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Kevin Chan

2 accepted papers

2021

Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributions

ICML 2021spotlight

Generative adversarial networks (GANs) are often billed as "universal distribution learners", but precisely what distributions they can represent and learn is still an open question. Heavy-tailed distributions are prevalent in many different domains such as financial risk-assessment, physics, and ep…

2019

MaxHedge: Maximizing a Maximum Online

AISTATS 2019poster

We introduce a new online learning framework where, at each trial, the learner is required to select a subset of actions from a given known action set. Each action is associated with an energy value, a reward and a cost. The sum of the energies of the actions selected cannot exceed a given energy bu…

Cited by 5SourcePDFScholar