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Learning Theory

5312 directly classified papers

Papers per year

Papers

Data-driven Rank Breaking for Efficient Rank Aggregation ICML 2016
Stochastically Transitive Models for Pairwise Comparisons: Statistical and Computational Issues ICML 2016
Dimension-Free Iteration Complexity of Finite Sum Optimization Problems NIPS 2016
Active Learning with Oracle Epiphany NIPS 2016
Combinatorial semi-bandit with known covariance NIPS 2016
Regret Bounds for Non-decomposable Metrics with Missing Labels NIPS 2016
A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++ NIPS 2016
Learning Using Anti-Training with Sacrificial Data JMLR 2016
Multi-step learning and underlying structure in statistical models NIPS 2016
L1-regularized Neural Networks are Improperly Learnable in Polynomial Time ICML 2016
Tracking the Best Expert in Non-stationary Stochastic Environments NIPS 2016
Regret of Queueing Bandits NIPS 2016
Train faster, generalize better: Stability of stochastic gradient descent ICML 2016
Online Learning with Feedback Graphs Without the Graphs ICML 2016
Refined Lower Bounds for Adversarial Bandits NIPS 2016
Sign rank versus VC dimension COLT 2016
The Extended Littlestone’s Dimension for Learning with Mistakes and Abstentions COLT 2016
No Regret Bound for Extreme Bandits AISTATS 2016
Nearly Optimal Classification for Semimetrics AISTATS 2016
Deep Learning without Poor Local Minima NIPS 2016
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity NIPS 2016
Statistical-Computational Tradeoffs in Planted Problems and Submatrix Localization with a Growing Number of Clusters and Submatrices JMLR 2016
The Benefit of Multitask Representation Learning JMLR 2016
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding NIPS 2016
Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels NIPS 2016