conftrace_
← Optimization & Theory
Machine Learning › Optimization & Theory ›

Learning Theory

5,312 papers

Papers per year

Papers

Best-item Learning in Random Utility Models with Subset Choices AISTATS 2020
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks AISTATS 2020
On the Sample Complexity of Learning Sum-Product Networks AISTATS 2020
On Learnability wih Computable Learners ALT 2020
Distribution Free Learning with Local Queries ALT 2020
First-Order Bayesian Regret Analysis of Thompson Sampling ALT 2020
Cautious Limit Learning ALT 2020
Algebraic and Analytic Approaches for Parameter Learning in Mixture Models ALT 2020
On the Complexity of Proper Distribution-Free Learning of Linear Classifiers ALT 2020
A Non-Trivial Algorithm Enumerating Relevant Features over Finite Fields ALT 2020
Privately Answering Classification Queries in the Agnostic PAC Model ALT 2020
Top-$k$ Combinatorial Bandits with Full-Bandit Feedback ALT 2020
On the Practical Ability of Recurrent Neural Networks to Recognize Hierarchical Languages COLING 2020
Picking BERT’s Brain: Probing for Linguistic Dependencies in Contextualized Embeddings Using Representational Similarity Analysis COLING 2020
Classifier Probes May Just Learn from Linear Context Features COLING 2020
An empirical analysis of existing systems and datasets toward general simple question answering COLING 2020
How coherent are neural models of coherence? COLING 2020
Optimality and Approximation with Policy Gradient Methods in Markov Decision Processes COLT 2020
Closure Properties for Private Classification and Online Prediction COLT 2020
Hardness of Identity Testing for Restricted Boltzmann Machines and Potts models COLT 2020
Proper Learning, Helly Number, and an Optimal SVM Bound COLT 2020
Sharper Bounds for Uniformly Stable Algorithms COLT 2020
ID3 Learns Juntas for Smoothed Product Distributions COLT 2020
Bounds in query learning COLT 2020
Learning Polynomials in Few Relevant Dimensions COLT 2020