Papers
Bandits with Movement Costs and Adaptive Pricing
Tomer Koren, Roi Livni, Yishay Mansour
Predicting with Distributions
Michael Kearns, Zhiwei Steven Wu
The Hidden Hubs Problem
Ravindran Kannan, Santosh Vempala
A Unified Analysis of Stochastic Optimization Methods Using Jump System Theory and Quadratic Constraints
Bin Hu, Peter Seiler, Anders Rantzer
Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes
Lunjia Hu, Ruihan Wu, Tianhong Li et al.
Surprising properties of dropout in deep networks
David P. Helmbold, Philip M. Long
Submodular Optimization under Noise
Avinatan Hassidim, Yaron Singer
Nearly-tight VC-dimension bounds for piecewise linear neural networks
Nick Harvey, Christopher Liaw, Abbas Mehrabian
Fast Rates for Empirical Risk Minimization of Strict Saddle Problems
Alon Gonen, Shai Shalev-Shwartz
Reliably Learning the ReLU in Polynomial Time
Surbhi Goel, Varun Kanade, Adam Klivans et al.
Effective Semisupervised Learning on Manifolds
Amir Globerson, Roi Livni, Shai Shalev-Shwartz
Two-Sample Tests for Large Random Graphs Using Network Statistics
Debarghya Ghoshdastidar, Maurilio Gutzeit, Alexandra Carpentier et al.
High Dimensional Regression with Binary Coefficients. Estimating Squared Error and a Phase Transtition
Gamarnik David, Zadik Ilias
Matrix Completion from $O(n)$ Samples in Linear Time
David Gamarnik, Quan Li, Hongyi Zhang
Memoryless Sequences for Differentiable Losses
Rafael Frongillo, Andrew Nobel
ZigZag: A New Approach to Adaptive Online Learning
Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
Stochastic Composite Least-Squares Regression with Convergence Rate $O(1/n)$
Nicolas Flammarion, Francis Bach
Greed Is Good: Near-Optimal Submodular Maximization via Greedy Optimization
Moran Feldman, Christopher Harshaw, Amin Karbasi
Generalization for Adaptively-chosen Estimators via Stable Median
Vitaly Feldman, Thomas Steinke
Learning Multivariate Log-concave Distributions
Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
Ten Steps of EM Suffice for Mixtures of Two Gaussians
Constantinos Daskalakis, Christos Tzamos, Manolis Zampetakis
Square Hellinger Subadditivity for Bayesian Networks and its Applications to Identity Testing
Constantinos Daskalakis, Qinxuan Pan
Depth Separation for Neural Networks
Amit Daniely