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

Learning Theory

5,312 papers

Papers per year

Papers

Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks NIPS 2021
SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs NIPS 2021
Near-Optimal No-Regret Learning in General Games NIPS 2021
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources NIPS 2021
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game NIPS 2021
Variational Bayesian Reinforcement Learning with Regret Bounds NIPS 2021
On the Sample Complexity of Privately Learning Axis-Aligned Rectangles NIPS 2021
Minimax Regret for Stochastic Shortest Path NIPS 2021
Turing Completeness of Bounded-Precision Recurrent Neural Networks NIPS 2021
How Powerful are Performance Predictors in Neural Architecture Search? NIPS 2021
Online Convex Optimization with Continuous Switching Constraint NIPS 2021
A Universal Law of Robustness via Isoperimetry NIPS 2021
Deep Networks Provably Classify Data on Curves NIPS 2021
On the Expected Complexity of Maxout Networks NIPS 2021
On the Role of Optimization in Double Descent: A Least Squares Study NIPS 2021
On the Cryptographic Hardness of Learning Single Periodic Neurons NIPS 2021
Robust Generalization despite Distribution Shift via Minimum Discriminating Information NIPS 2021
A Geometric Analysis of Neural Collapse with Unconstrained Features NIPS 2021
Optimal Algorithms for Stochastic Contextual Preference Bandits NIPS 2021
Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions CORL 2021
Automated Symbolic Law Discovery: A Computer Vision Approach AAAI 2021
Disjunctive Temporal Problems under Structural Restrictions AAAI 2021
Finding Diverse Trees, Paths, and More AAAI 2021
Parameterizing Branch-and-Bound Search Trees to Learn Branching Policies AAAI 2021
Taxonomy Completion via Triplet Matching Network AAAI 2021