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Learning Theory

5,312 papers

Papers per year

Papers

Monte Carlo Gradient Estimation in Machine Learning JMLR 2020
Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality JMLR 2020
A determinantal point process for column subset selection JMLR 2020
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms JMLR 2020
Spectral bandits JMLR 2020
Learning Sums of Independent Random Variables with Sparse Collective Support JMLR 2020
Theory of Curriculum Learning, with Convex Loss Functions JMLR 2020
Lower Bounds for Learning Distributions under Communication Constraints via Fisher Information JMLR 2020
Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection JMLR 2020
Risk Bounds for Reservoir Computing JMLR 2020
Best Practices for Scientific Research on Neural Architecture Search JMLR 2020
On Efficient Adjustment in Causal Graphs JMLR 2020
Learning the Globally Optimal Distributed LQ Regulator L4DC 2020
Constrained Upper Confidence Reinforcement Learning L4DC 2020
Learning nonlinear dynamical systems from a single trajectory L4DC 2020
A Duality Approach for Regret Minimization in Average-Award Ergodic Markov Decision Processes L4DC 2020
On the limits of cross-domain generalization in automated X-ray prediction MIDL 2020
A Score-and-Search Approach to Learning Bayesian Networks with Noisy-OR Relations PGM 2020
Finite-sample Analysis of Greedy-GQ with Linear Function Approximation under Markovian Noise UAI 2020
Bounding the expected run-time of nonconvex optimization with early stopping UAI 2020
Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems UAI 2020
Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison UAI 2020
Risk Bounds for Low Cost Bipartite Ranking UAI 2020
Estimation Rates for Sparse Linear Cyclic Causal Models UAI 2020
Order Optimal One-Shot Distributed Learning NIPS 2019