← Optimization & Theory
Machine Learning › Optimization & Theory ›

Learning Theory

5312 directly classified papers

Papers per year

Papers

Active Learning for Accurate Estimation of Linear Models ICML 2017
Active Learning for Top-$K$ Rank Aggregation from Noisy Comparisons ICML 2017
Algorithmic Stability and Hypothesis Complexity ICML 2017
Iterative Machine Teaching ICML 2017
Provably Optimal Algorithms for Generalized Linear Contextual Bandits ICML 2017
Adaptive Feature Selection: Computationally Efficient Online Sparse Linear Regression under RIP ICML 2017
Efficient Regret Minimization in Non-Convex Games ICML 2017
Regret Minimization in Behaviorally-Constrained Zero-Sum Games ICML 2017
Maximum Selection and Ranking under Noisy Comparisons ICML 2017
Adaptive Multiple-Arm Identification ICML 2017
Active Heteroscedastic Regression ICML 2017
Spectral Learning from a Single Trajectory under Finite-State Policies ICML 2017
The Price of Differential Privacy for Online Learning ICML 2017
Efficient Kernel Selection via Spectral Analysis IJCAI 2017
Generalization Error of Invariant Classifiers AISTATS 2017
Spectral Methods for Correlated Topic Models AISTATS 2017
Nonparametric Risk Bounds for Time-Series Forecasting JMLR 2017
Statistical and Computational Guarantees for the Baum-Welch Algorithm JMLR 2017
No Learner Left Behind: On the Complexity of Teaching Multiple Learners Simultaneously IJCAI 2017
The End of Optimism? An Asymptotic Analysis of Finite-Armed Linear Bandits AISTATS 2017
A Unified Framework for Structured Prediction: From Theory to Practice EMNLP 2017
Counterfactual Learning from Bandit Feedback under Deterministic Logging : A Case Study in Statistical Machine Translation EMNLP 2017
Learning ReLUs via Gradient Descent NIPS 2017
Regularization and the small-ball method II: complexity dependent error rates JMLR 2017
Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities JMLR 2017