Poster
Adaptive Antithetic Sampling for Variance Reduction
Hongyu Ren · Shengjia Zhao · Stefano Ermon

Thu Jun 13th 06:30 -- 09:00 PM @ Pacific Ballroom #205

Variance reduction is crucial in stochastic estimation and optimization problems. Antithetic sampling reduces the variance of a Monte Carlo estimator by drawing correlated, rather than independent, samples. However, designing an effective correlation structure is challenging and application specific, thus limiting the practical applicability of these methods. In this paper, we propose a general-purpose adaptive antithetic sampling framework. We provide gradient-based and gradient-free methods to train the samplers such that they reduce variance while ensuring that the underlying Monte Carlo estimator is provably unbiased. We demonstrate the effectiveness of our approach on Bayesian inference and generative model training, where it reduces variance and improves task performance with little computational overhead.

Author Information

Hongyu Ren (Stanford University)
Shengjia Zhao (Stanford University)
Stefano Ermon (Stanford University)

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