Adaptive Sampling for Stochastic Risk-Averse Learning

NeurIPS 2020

Adaptive Sampling for Stochastic Risk-Averse Learning

Dec 06, 2020
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We consider the problem of training machine learning models in a risk-averse manner. In particular, we propose an adaptive sampling algorithm for stochastically optimizing the Conditional Value-at-Risk (CVaR) of a loss distribution. We use a distributionally robust formulation of the CVaR to phrase the problem as a zero-sum game between two players. Our approach solves the game using an efficient no-regret algorithm for each player. Critically, we can apply these algorithms to large-scale settings because the implementation relies on sampling from Determinantal Point Processes. Finally, we empirically demonstrate its effectiveness on large-scale convex and non-convex learning tasks. Speakers: Sebastian Curi, Kfir Y. Levy, Stefanie Jegelka, Andreas Krause

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