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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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Papers (including subtopics)

Compositional Diffusion-Based Continuous Constraint Solvers CoRL 2023 Compositional Generalization from First Principles NeurIPS 2023 Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks L4DC 2023 Compositional Neural Certificates for Networked Dynamical Systems L4DC 2023 Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees NeurIPS 2023 Compositional Score Modeling for Simulation-Based Inference ICML 2023 Comprehensive Algorithm Portfolio Evaluation using Item Response Theory JMLR 2023 Compressed Decentralized Learning of Conditional Mean Embedding Operators in Reproducing Kernel Hilbert Spaces AAAI 2023 Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data ICML 2023 Compressing Transformers: Features Are Low-Rank, but Weights Are Not! AAAI 2023 Compression, Generalization and Learning JMLR 2023 Compress Then Test: Powerful Kernel Testing in Near-linear Time AISTATS 2023 Computably Continuous Reinforcement-Learning Objectives Are PAC-Learnable AAAI 2023 Computational Asymmetries in Robust Classification ICML 2023 Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy NeurIPS 2023 Computational Doob h-transforms for Online Filtering of Discretely Observed Diffusions ICML 2023 Computationally Budgeted Continual Learning: What Does Matter? CVPR 2023 Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection AutoML 2023 Computationally-Efficient Neural Image Compression with Shallow Decoders ICCV 2023 Compute-Efficient Churn Reduction for Conversational Agents EMNLP 2023 Compute-Efficient Deep Learning: Algorithmic Trends and Opportunities JMLR 2023 Computer Vision to the Rescue: Infant Postural Symmetry Estimation From Incongruent Annotations WACV 2023 Computing all Optimal Partial Transports ICLR 2023 Computing Approximate ℓ_p Sensitivities NeurIPS 2023 Computing Divergences between Discrete Decomposable Models AAAI 2023