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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

1
52
352
828
3,646
5,854
2000
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'25

Papers (including subtopics)

Learning Layer-wise Equivariances Automatically using Gradients NeurIPS 2023 Learning Lightweight Neural Networks via Channel-Split Recurrent Convolution WACV 2023 Learning Locomotion Skills from MPC in Sensor Space L4DC 2023 Learning Logical Reasoning Using an Intelligent Tutoring System: A Hybrid Approach to Student Modeling AAAI 2023 Learning Logic Programs by Discovering Where Not to Search AAAI 2023 Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets ICLR 2023 Learning Markov Random Fields for Combinatorial Structures via Sampling through Lovász Local Lemma AAAI 2023 Learning Mean-Field Games with Discounted and Average Costs JMLR 2023 Learning Mixtures of Gaussians Using the DDPM Objective NeurIPS 2023 Learning Multi-agent Behaviors from Distributed and Streaming Demonstrations NeurIPS 2023 Learning Narrow One-Hidden-Layer ReLU Networks COLT 2023 Learning Neural PDE Solvers with Parameter-Guided Channel Attention ICML 2023 Learning Neural Volumetric Representations of Dynamic Humans in Minutes CVPR 2023 Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation ICML 2023 Learning on Manifolds: Universal Approximations Properties using Geometric Controllability Conditions for Neural ODEs L4DC 2023 Learning Optimal Feedback Operators and their Sparse Polynomial Approximations JMLR 2023 Learning Optimal Policy for Simultaneous Machine Translation via Binary Search ACL 2023 Learning Partial Correlation Based Deep Visual Representation for Image Classification CVPR 2023 Learning Physical Models that Can Respect Conservation Laws ICML 2023 Learning Physics-Informed Neural Networks without Stacked Back-propagation AISTATS 2023 Learning Preconditioners for Conjugate Gradient PDE Solvers ICML 2023 Learning Probabilistic Topological Representations Using Discrete Morse Theory ICLR 2023 Learning Provably Robust Estimators for Inverse Problems via Jittering NeurIPS 2023 Learning Proximal Operators to Discover Multiple Optima ICLR 2023 Learning-Rate-Free Learning by D-Adaptation ICML 2023