Papers
An Information-Theoretic Approach to Minimax Regret in Partial Monitoring
Tor Lattimore, Csaba Szepesvári
Global Convergence of the EM Algorithm for Mixtures of Two Component Linear Regression
Jeongyeol Kwon, Wei Qian, Constantine Caramanis et al.
Distribution-Dependent Analysis of Gibbs-ERM Principle
Ilja Kuzborskij, Nicolò Cesa-Bianchi, Csaba Szepesvári
Contextual bandits with continuous actions: Smoothing, zooming, and adapting
Akshay Krishnamurthy, John Langford, Aleksandrs Slivkins et al.
Bandit Principal Component Analysis
Wojciech Kotłowski, Gergely Neu
Discrepancy, Coresets, and Sketches in Machine Learning
Zohar Karnin, Edo Liberty
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme
Belhal Karimi, Blazej Miasojedow, Eric Moulines et al.
On Communication Complexity of Classification Problems
Daniel Kane, Roi Livni, Shay Moran et al.
Privately Learning High-Dimensional Distributions
Gautam Kamath, Jerry Li, Vikrant Singhal et al.
Sample complexity of partition identification using multi-armed bandits
Sandeep Juneja, Subhashini Krishnasamy
Parameter-Free Online Convex Optimization with Sub-Exponential Noise
Kwang-Sung Jun, Francesco Orabona
An Optimal High-Order Tensor Method for Convex Optimization
Bo Jiang, Haoyue Wang, Shuzhong Zhang
The implicit bias of gradient descent on nonseparable data
Ziwei Ji, Matus Telgarsky
Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation
Vishesh Jain, Frederic Koehler, Jingbo Liu et al.
Making the Last Iterate of SGD Information Theoretically Optimal
Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
Sample-Optimal Low-Rank Approximation of Distance Matrices
Pitor Indyk, Ali Vakilian, Tal Wagner et al.
A Robust Spectral Algorithm for Overcomplete Tensor Decomposition
Samuel B. Hopkins, Tselil Schramm, Jonathan Shi
How Hard is Robust Mean Estimation?
Samuel B. Hopkins, Jerry Li
Reasoning in Bayesian Opinion Exchange Networks Is PSPACE-Hard
Jan Hązła, Ali Jadbabaie, Elchanan Mossel et al.
Tight analyses for non-smooth stochastic gradient descent
Nicholas J. A. Harvey, Christopher Liaw, Yaniv Plan et al.
Better Algorithms for Stochastic Bandits with Adversarial Corruptions
Anupam Gupta, Tomer Koren, Kunal Talwar
Sampling and Optimization on Convex Sets in Riemannian Manifolds of Non-Negative Curvature
Navin Goyal, Abhishek Shetty
When can unlabeled data improve the learning rate?
Christina Göpfert, Shai Ben-David, Olivier Bousquet et al.
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time
Surbhi Goel, Adam R. Klivans
Learning Ising Models with Independent Failures
Surbhi Goel, Daniel M. Kane, Adam R. Klivans