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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)

Statistical Indistinguishability of Learning Algorithms ICML 2023 Statistical Inference and A/B Testing for First-Price Pacing Equilibria ICML 2023 Statistical Inference for Fisher Market Equilibrium ICLR 2023 Statistical Inference for Noisy Incomplete Binary Matrix JMLR 2023 Statistical Inference on Multi-armed Bandits with Delayed Feedback ICML 2023 Statistical Learning under Heterogeneous Distribution Shift ICML 2023 Statistical Limits of Adaptive Linear Models: Low-Dimensional Estimation and Inference NeurIPS 2023 Statistically Valid Variable Importance Assessment through Conditional Permutations NeurIPS 2023 Statistical Robustness of Empirical Risks in Machine Learning JMLR 2023 STay-ON-the-Ridge: Guaranteed Convergence to Local Minimax Equilibrium in Nonconvex-Nonconcave Games COLT 2023 StegaNeRF: Embedding Invisible Information within Neural Radiance Fields ICCV 2023 StEik: Stabilizing the Optimization of Neural Signed Distance Functions and Finer Shape Representation NeurIPS 2023 Stein Π-Importance Sampling NeurIPS 2023 Stepdown SLOPE for Controlled Feature Selection AAAI 2023 Stochastic Approximation Algorithms for Systems of Interacting Particles NeurIPS 2023 Stochastic Approximation Approaches to Group Distributionally Robust Optimization NeurIPS 2023 Stochastic Bridges as Effective Regularizers for Parameter-Efficient Tuning ACL 2023 Stochastic Causal Programming for Bounding Treatment Effects CLeaR 2023 Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks NeurIPS 2023 Stochastic Differentially Private and Fair Learning ICLR 2023 Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis NeurIPS 2023 Stochastic Generative Flow Networks UAI 2023 Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods AISTATS 2023 Stochastic Gradient Descent-Induced Drift of Representation in a Two-Layer Neural Network ICML 2023 Stochastic Gradient Descent under Markovian Sampling Schemes ICML 2023