NIPS 2017, presentations from the Algorithms session

• A Linear-Time Kernel Goodness-of-Fit Test • Generalization Properties of Learning with Random Features • Communication-Efficient Distributed Learning of Discrete Distributions • Optimistic posterior sampling for reinforcement learning: worst-case regret bounds • Regret Analysis for Continuous Dueling Bandit • Minimal Exploration in Structured Stochastic Bandits • Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe • Diving into the shallows: a computational perspective on large-scale shallow learning • Monte-Carlo Tree Search by Best Arm Identification • A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control • Parameter-Free Online Learning via Model Selection • Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction • Gaussian Quadrature for Kernel Features Learning Linear Dynamical Systems via Spectral Filtering