Skip to content
conftrace_

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
'5
'10
'15
'20
'25

Papers (including subtopics)

Learning in Multi-Player Stochastic Games UAI 2021 Learning in Non-Cooperative Configurable Markov Decision Processes NeurIPS 2021 Learning Interaction Kernels for Agent Systems on Riemannian Manifolds ICML 2021 Learning Invariant Representations using Inverse Contrastive Loss AAAI 2021 Learning Large Neighborhood Search Policy for Integer Programming NeurIPS 2021 Learning Latent Architectural Distribution in Differentiable Neural Architecture Search via Variational Information Maximization ICCV 2021 Learning Logic Rules for Document-Level Relation Extraction EMNLP 2021 Learning Maximum Margin Markov Networks from examples with missing labels ACML 2021 Learning Model-Based Privacy Protection under Budget Constraints AAAI 2021 Learning Model Preconditions for Planning with Multiple Models CoRL 2021 Learning Motion Priors for 4D Human Body Capture in 3D Scenes ICCV 2021 Learning Nash Equilibria in Zero-Sum Stochastic Games via Entropy-Regularized Policy Approximation IJCAI 2021 Learning Neural Event Functions for Ordinary Differential Equations ICLR 2021 Learning Neural Network Subspaces ICML 2021 Learning Nonparametric Volterra Kernels with Gaussian Processes NeurIPS 2021 Learning of Structurally Unambiguous Probabilistic Grammars AAAI 2021 Learning Optical Flow From a Few Matches CVPR 2021 Learning Optimal Predictive Checklists NeurIPS 2021 Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes AISTATS 2021 Learning Partially Observed Linear Dynamical Systems from Logarithmic Number of Samples L4DC 2021 Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs NeurIPS 2021 Learning Prediction Intervals for Regression: Generalization and Calibration AISTATS 2021 Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression CVPR 2021 Learning proposals for probabilistic programs with inference combinators UAI 2021 Learning Randomly Perturbed Structured Predictors for Direct Loss Minimization ICML 2021