Michael I. Jordan

TL;DR: This work addresses the problem of policy evaluation in discounted Markov decision processes, and provides instance-dependent guarantees on the $\ell_\infty$-error under a generative model, and establishes both asymptotic and non-asymptotic versions of local minimax lower bounds for policy evaluation, thereby providing an instance- dependent baseline by which to compare algorithms.

TL;DR: This paper proposes a general framework for parallelizing stochastic algorithms on multi-node distributed systems called Splash, which consists of a programming interface and an execution engine and provides theoretical justifications on the optimal rate of convergence.

TL;DR: A hierarchical, nonparametric statistical model for wavelet representations of natural images that automatically adapts to the complexity of different images and wavelet bases through a Monte Carlo learning algorithm.

TL;DR: A control-theoretic perspective on optimal tensor algorithms for minimizing a convex function in a finite-dimensional Euclidean space is provided.

TL;DR: The authors proposed a method for feature selection that employs kernel-based measures of independence to find a subset of covariates that is maximally predictive of the response, and showed how to perform feature selection via a constrained optimization problem involving the trace of the conditional covariance operator.