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Learning Theory

5312 directly classified papers

Papers per year

Papers

Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network JMLR 2017
Unifying Task Specification in Reinforcement Learning ICML 2017
On the Learnability of Fully-Connected Neural Networks AISTATS 2017
On the Complexity of Learning from Label Proportions IJCAI 2017
Limits of End-to-End Learning ACML 2017
Sharp Minima Can Generalize For Deep Nets ICML 2017
Large-Scale Evolution of Image Classifiers ICML 2017
Failures of Gradient-Based Deep Learning ICML 2017
Contextual Decision Processes with low Bellman rank are PAC-Learnable ICML 2017
Minimax Regret Bounds for Reinforcement Learning ICML 2017
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions NIPS 2017
Optimal Rates for Multi-pass Stochastic Gradient Methods JMLR 2017
A survey of Algorithms and Analysis for Adaptive Online Learning JMLR 2017
Minimax Estimation of Kernel Mean Embeddings JMLR 2017
Asymptotic behavior of Support Vector Machine for spiked population model JMLR 2017
Nearly optimal classification for semimetrics JMLR 2017
Online Prediction with Selfish Experts NIPS 2017
Generalization for Adaptively-chosen Estimators via Stable Median COLT 2017
Square Hellinger Subadditivity for Bayesian Networks and its Applications to Identity Testing COLT 2017
Discovering Potential Correlations via Hypercontractivity NIPS 2017
A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening NIPS 2017
Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks ICML 2017
Exploring Generalization in Deep Learning NIPS 2017
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces NIPS 2017
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions NIPS 2017