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Learning Theory

5312 directly classified papers

Papers per year

Papers

A no-regret generalization of hierarchical softmax to extreme multi-label classification NIPS 2018
Narrative Schema Stability in News Text COLING 2018
A Property Testing Framework for the Theoretical Expressivity of Graph Kernels IJCAI 2018
Learning To Learn Around A Common Mean NIPS 2018
Online Learning of Quantum States NIPS 2018
Community Exploration: From Offline Optimization to Online Learning NIPS 2018
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate NIPS 2018
Tight Bounds for Collaborative PAC Learning via Multiplicative Weights NIPS 2018
Adaptation to Easy Data in Prediction with Limited Advice NIPS 2018
Optimal Cooperative Inference AISTATS 2018
Actively Avoiding Nonsense in Generative Models COLT 2018
Hardness of Learning Noisy Halfspaces using Polynomial Thresholds COLT 2018
What Do Classifiers Actually Learn? a Case Study on Emotion Recognition Datasets INTERSPEECH 2018
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes NIPS 2018
Contextual bandits with surrogate losses: Margin bounds and efficient algorithms NIPS 2018
Effective Acoustic Cue Learning Is Not Just Statistical, It Is Discriminative INTERSPEECH 2018
Escaping Saddle Points in Constrained Optimization NIPS 2018
Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models COLT 2018
Local Optimality and Generalization Guarantees for the Langevin Algorithm via Empirical Metastability COLT 2018
Is Q-Learning Provably Efficient? NIPS 2018
Breaking the $1/\sqrt{n}$ Barrier: Faster Rates for Permutation-based Models in Polynomial Time COLT 2018
Policy Regret in Repeated Games NIPS 2018
Tensor Regression Meets Gaussian Processes AISTATS 2018
Active Tolerant Testing COLT 2018
On the Efficiency of Data Collection for Crowdsourced Classification IJCAI 2018