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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)

Towards Achieving Sub-linear Regret and Hard Constraint Violation in Model-free RL AISTATS 2024
Distributionally Robust Quickest Change Detection using Wasserstein Uncertainty Sets AISTATS 2024
Submodular Minimax Optimization: Finding Effective Sets AISTATS 2024
Effect of Ambient-Intrinsic Dimension Gap on Adversarial Vulnerability AISTATS 2024
Information-theoretic Analysis of Bayesian Test Data Sensitivity AISTATS 2024
Orthogonal Gradient Boosting for Simpler Additive Rule Ensembles AISTATS 2024
When No-Rejection Learning is Consistent for Regression with Rejection AISTATS 2024
Robust variance-regularized risk minimization with concomitant scaling AISTATS 2024
Fast and Adversarial Robust Kernelized SDU Learning AISTATS 2024
Learning Sampling Policy to Achieve Fewer Queries for Zeroth-Order Optimization AISTATS 2024
Online non-parametric likelihood-ratio estimation by Pearson-divergence functional minimization AISTATS 2024
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates AISTATS 2024
Best-of-Both-Worlds Algorithms for Linear Contextual Bandits AISTATS 2024
Fixed-Budget Real-Valued Combinatorial Pure Exploration of Multi-Armed Bandit AISTATS 2024
Scalable Learning of Item Response Theory Models AISTATS 2024
Risk Seeking Bayesian Optimization under Uncertainty for Obtaining Extremum AISTATS 2024
Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models AISTATS 2024
Gaussian process regression with Sliced Wasserstein Weisfeiler-Lehman graph kernels AISTATS 2024
Intrinsic Gaussian Vector Fields on Manifolds AISTATS 2024
A Unifying Variational Framework for Gaussian Process Motion Planning AISTATS 2024
MMD-based Variable Importance for Distributional Random Forest AISTATS 2024
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning AISTATS 2024
Generalization Bounds for Label Noise Stochastic Gradient Descent AISTATS 2024
Importance Matching Lemma for Lossy Compression with Side Information AISTATS 2024
Certified private data release for sparse Lipschitz functions AISTATS 2024