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Optimization & Theory

36,874 papers (3 classified directly here)

Subtopics

Optimization (14,983) Bayesian Inference (5,566) Learning Theory (5,334) Theory (4,955) Statistical Learning (4,117) Neural Network Optimization (3,649) Stochastic Processes (2,667) Loss Functions (1,162) Distributed Learning (1,135) Stochastic Methods (1,118) Online Algorithms (806) Statistics (684) Information Theory (318) Generalization (220) Sparse Optimization (158) Sample Complexity (99) Game Theory (30) Robust Optimization (17) Optimal Transport (14) Approximation Algorithms (10) Distributed Optimization (8) Convex Optimization (8)

Papers per year

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Papers (including subtopics)

Stochastic Methods in Variational Inequalities: Ergodicity, Bias and Refinements AISTATS 2024
Self-Compatibility: Evaluating Causal Discovery without Ground Truth AISTATS 2024
Equivariant bootstrapping for uncertainty quantification in imaging inverse problems AISTATS 2024
An Improved Algorithm for Learning Drifting Discrete Distributions AISTATS 2024
Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems AISTATS 2024
Weight-Sharing Regularization AISTATS 2024
First Passage Percolation with Queried Hints AISTATS 2024
User-level Differentially Private Stochastic Convex Optimization: Efficient Algorithms with Optimal Rates AISTATS 2024
DE-HNN: An effective neural model for Circuit Netlist representation AISTATS 2024
Simulation-Based Stacking AISTATS 2024
Benchmarking Observational Studies with Experimental Data under Right-Censoring AISTATS 2024
Sequential Monte Carlo for Inclusive KL Minimization in Amortized Variational Inference AISTATS 2024
Anytime-Constrained Reinforcement Learning AISTATS 2024
Auditing Fairness under Unobserved Confounding AISTATS 2024
Probabilistic Modeling for Sequences of Sets in Continuous-Time AISTATS 2024
Causal Q-Aggregation for CATE Model Selection AISTATS 2024
The effect of Leaky ReLUs on the training and generalization of overparameterized networks AISTATS 2024
Decentralized Multi-Level Compositional Optimization Algorithms with Level-Independent Convergence Rate AISTATS 2024
Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate AISTATS 2024
Soft-constrained Schrödinger Bridge: a Stochastic Control Approach AISTATS 2024
Coreset Markov chain Monte Carlo AISTATS 2024
A General Theoretical Paradigm to Understand Learning from Human Preferences AISTATS 2024
Understanding the Generalization Benefits of Late Learning Rate Decay AISTATS 2024
Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion AISTATS 2024
Near-Interpolators: Rapid Norm Growth and the Trade-Off between Interpolation and Generalization AISTATS 2024