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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)

Differentially Private Conditional Independence Testing AISTATS 2024
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles AISTATS 2024
On the Effect of Key Factors in Spurious Correlation: A theoretical Perspective AISTATS 2024
Manifold-Aligned Counterfactual Explanations for Neural Networks AISTATS 2024
On the Misspecification of Linear Assumptions in Synthetic Controls AISTATS 2024
The sample complexity of ERMs in stochastic convex optimization AISTATS 2024
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learning AISTATS 2024
On cyclical MCMC sampling AISTATS 2024
Proving Linear Mode Connectivity of Neural Networks via Optimal Transport AISTATS 2024
Multi-objective Optimization via Wasserstein-Fisher-Rao Gradient Flow AISTATS 2024
Adaptive importance sampling for heavy-tailed distributions via $α$-divergence minimization AISTATS 2024
A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent AISTATS 2024
Efficient Variational Sequential Information Control AISTATS 2024
Membership Testing in Markov Equivalence Classes via Independence Queries AISTATS 2024
Learning Under Random Distributional Shifts AISTATS 2024
Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimization AISTATS 2024
Online multiple testing with e-values AISTATS 2024
How Good is a Single Basin? AISTATS 2024
On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem AISTATS 2024
Acceleration and Implicit Regularization in Gaussian Phase Retrieval AISTATS 2024
Low-rank MDPs with Continuous Action Spaces AISTATS 2024
An Analytic Solution to Covariance Propagation in Neural Networks AISTATS 2024
On the Vulnerability of Fairness Constrained Learning to Malicious Noise AISTATS 2024
Uncertainty-aware Continuous Implicit Neural Representations for Remote Sensing Object Counting AISTATS 2024
Think Global, Adapt Local: Learning Locally Adaptive K-Nearest Neighbor Kernel Density Estimators AISTATS 2024