Papers
1,396 papers found
Fundamental limits of symmetric low-rank matrix estimation
Marc Lelarge, Léo Miolane
Generalization for Adaptively-chosen Estimators via Stable Median
Vitaly Feldman, Thomas Steinke
Greed Is Good: Near-Optimal Submodular Maximization via Greedy Optimization
Moran Feldman, Christopher Harshaw, Amin Karbasi
High Dimensional Regression with Binary Coefficients. Estimating Squared Error and a Phase Transtition
Gamarnik David, Zadik Ilias
Homotopy Analysis for Tensor PCA
Anima Anandkumar, Yuan Deng, Rong Ge et al.
Ignoring Is a Bliss: Learning with Large Noise Through Reweighting-Minimization
Daniel Vainsencher, Shie Mannor, Huan Xu
Inapproximability of VC Dimension and Littlestone’s Dimension
Pasin Manurangsi, Aviad Rubinstein
Learning Disjunctions of Predicates
Nader H. Bshouty, Dana Drachsler-Cohen, Martin Vechev et al.
Learning Multivariate Log-concave Distributions
Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
Learning Non-Discriminatory Predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian et al.
Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems
Maria-Florina Balcan, Vaishnavh Nagarajan, Ellen Vitercik et al.
Learning with Limited Rounds of Adaptivity: Coin Tossing, Multi-Armed Bandits, and Ranking from Pairwise Comparisons
Arpit Agarwal, Shivani Agarwal, Sepehr Assadi et al.
Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization
Jonathan Scarlett, Ilija Bogunovic, Volkan Cevher
Matrix Completion from $O(n)$ Samples in Linear Time
David Gamarnik, Quan Li, Hongyi Zhang
Memory and Communication Efficient Distributed Stochastic Optimization with Minibatch Prox
Jialei Wang, Weiran Wang, Nathan Srebro
Memoryless Sequences for Differentiable Losses
Rafael Frongillo, Andrew Nobel
Mixing Implies Lower Bounds for Space Bounded Learning
Dana Moshkovitz, Michal Moshkovitz
Multi-Observation Elicitation
Sebastian Casalaina-Martin, Rafael Frongillo, Tom Morgan et al.
Nearly Optimal Sampling Algorithms for Combinatorial Pure Exploration
Lijie Chen, Anupam Gupta, Jian Li et al.
Nearly-tight VC-dimension bounds for piecewise linear neural networks
Nick Harvey, Christopher Liaw, Abbas Mehrabian
Noisy Population Recovery from Unknown Noise
Shachar Lovett, Jiapeng Zhang
Non-convex learning via Stochastic Gradient Langevin Dynamics: a nonasymptotic analysis
Maxim Raginsky, Alexander Rakhlin, Matus Telgarsky
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities
Alexander Rakhlin, Karthik Sridharan
On Learning vs. Refutation
Salil Vadhan