Papers
A Hitting Time Analysis of Stochastic Gradient Langevin Dynamics
Yuchen Zhang, Percy Liang, Moses Charikar
Empirical Risk Minimization for Stochastic Convex Optimization: $O(1/n)$- and $O(1/n^2)$-type of Risk Bounds
Lijun Zhang, Tianbao Yang, Rong Jin
Learning Non-Discriminatory Predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian et al.
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox
Jialei Wang, Weiran Wang, Nathan Srebro
Ignoring Is a Bliss: Learning with Large Noise Through Reweighting-Minimization
Daniel Vainsencher, Shie Mannor, Huan Xu
On Learning vs. Refutation
Salil Vadhan
The Simulator: Understanding Adaptive Sampling in the Moderate-Confidence Regime
Max Simchowitz, Kevin Jamieson, Benjamin Recht
Fast and robust tensor decomposition with applications to dictionary learning
Tselil Schramm, David Steurer
An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits
Yevgeny Seldin, Gábor Lugosi
Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization
Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities
Alexander Rakhlin, Karthik Sridharan
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis
Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky
Exact tensor completion with sum-of-squares
Aaron Potechin, David Steurer
Sample complexity of population recovery
Yury Polyanskiy, Ananda Theertha Suresh, Yihong Wu
Fast rates for online learning in Linearly Solvable Markov Decision Processes
Gergely Neu, Vicenç Gómez
Mixing Implies Lower Bounds for Space Bounded Learning
Dana Moshkovitz, Michal Moshkovitz
Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality
Song Mei, Theodor Misiakiewicz, Andrea Montanari et al.
A Second-order Look at Stability and Generalization
Andreas Maurer
Inapproximability of VC Dimension and Littlestone’s Dimension
Pasin Manurangsi, Aviad Rubinstein
Noisy Population Recovery from Unknown Noise
Shachar Lovett, Jiapeng Zhang
Adaptivity to Noise Parameters in Nonparametric Active Learning
Carpentier Alexandra Locatelli Andrea, Kpotufe Samory
Robust and Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities
Jerry Li, Ludwig Schmidt
Fundamental limits of symmetric low-rank matrix estimation
Marc Lelarge, Léo Miolane
On the Ability of Neural Nets to Express Distributions
Holden Lee, Rong Ge, Tengyu Ma et al.
Sparse Stochastic Bandits
Joon Kwon, Vianney Perchet, Claire Vernade