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Optimization & Theory

43,292 papers (3 classified directly here)

Subtopics · a paper can sit in several, so these overlap

Optimization (17,731) Learning Theory (6,809) Bayesian Inference (6,267) Theory (5,579) Statistical Learning (4,564) Neural Network Optimization (4,155) Stochastic Processes (2,948) Stochastic Methods (1,283) Loss Functions (1,220) Distributed Learning (1,202) Online Algorithms (1,013) Statistics (822) Generalization (376) Information Theory (370) Sample Complexity (282) Sparse Optimization (241) Game Theory (151) Convex Optimization (132) Optimal Transport (104) Robust Optimization (84) Distributed Optimization (73) Approximation Algorithms (33)

Papers per year

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52
352
828
3,646
5,854
2000
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Papers (including subtopics)

GLIME: General, Stable and Local LIME Explanation NeurIPS 2023 Global Convergence Analysis of Local SGD for Two-layer Neural Network without Overparameterization NeurIPS 2023 Global Convergence of Over-parameterized Deep Equilibrium Models AISTATS 2023 Global Convergence of Sub-gradient Method for Robust Matrix Recovery: Small Initialization, Noisy Measurements, and Over-parameterization JMLR 2023 Global Convergence of Two-Timescale Actor-Critic for Solving Linear Quadratic Regulator AAAI 2023 Global-Local Regularization Via Distributional Robustness AISTATS 2023 Globally injective and bijective neural operators NeurIPS 2023 Globally Optimal Training of Neural Networks with Threshold Activation Functions ICLR 2023 Global Optimality in Bivariate Gradient-based DAG Learning NeurIPS 2023 Global optimality of Elman-type RNNs in the mean-field regime ICML 2023 Global Optimization with Parametric Function Approximation ICML 2023 Global Perception Based Autoregressive Neural Processes ICCV 2023 Global Update Tracking: A Decentralized Learning Algorithm for Heterogeneous Data NeurIPS 2023 GNN&GBDT-Guided Fast Optimizing Framework for Large-scale Integer Programming ICML 2023 Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity NeurIPS 2023 Going beyond persistent homology using persistent homology NeurIPS 2023 GPMO: Gradient Perturbation-Based Contrastive Learning for Molecule Optimization IJCAI 2023 GPU-accelerated Guided Source Separation for Meeting Transcription Interspeech 2023 Gradient-Adaptive Pareto Optimization for Constrained Reinforcement Learning AAAI 2023 Gradient-Based Feature Learning under Structured Data NeurIPS 2023 Gradient-Based Quantification of Epistemic Uncertainty for Deep Object Detectors WACV 2023 Gradient-Based Uncertainty Attribution for Explainable Bayesian Deep Learning CVPR 2023 Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space ICML 2023 Gradient Descent Converges Linearly for Logistic Regression on Separable Data ICML 2023 Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks ICML 2023