Michael I. Jordan

TL;DR: This work considers first-order stochastic optimization from a general statistical point of view, motivating a specific form of recursive averaging of past Stochastic gradients, and concludes that the resulting algorithm, which is referred to as ROOT-SGD, matches the state-of-the-art convergence rate among online variance-reduced stochastically approximation methods.

TL;DR: In this paper, an approximate message passing (AMP) algorithm is proposed for decoding a discrete signal of categorical variables from the observation of several histograms of pooled subsets of it.

TL;DR: This work develops a new two-step method for solving constrained counterfactual policy optimization problem, which first casts the reward estimation problem as a domain adaptation problem with supplementary structure, and then subsequently uses the estimators for optimizing the policy with constraints.

TL;DR: The Neural Rendering Model (NRM), a new probabilistic generative model whose inference calculations correspond to those in a given CNN architecture, and a new loss termed as the Max-Min cross entropy which outperforms the traditional cross-entropy loss for object classification.