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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)

On Undisputed Sets in Abstract Argumentation AAAI 2023 On Universal Portfolios with Continuous Side Information AISTATS 2023 On User-Level Private Convex Optimization ICML 2023 On using distribution-based compositionality assessment to evaluate compositional generalisation in machine translation EMNLP 2023 OpenGSL: A Comprehensive Benchmark for Graph Structure Learning NeurIPS 2023 Open Problem: Is There a First-Order Method that Only Converges to Local Minimax Optima? COLT 2023 Open Problem: Learning sparse linear concepts by priming the features COLT 2023 Open problem: log(n) factor in "Local Glivenko-Cantelli" COLT 2023 Open Problem: Polynomial linearly-convergent method for g-convex optimization? COLT 2023 Open Problem: The Sample Complexity of Multi-Distribution Learning for VC Classes COLT 2023 OpenProteinSet: Training data for structural biology at scale NeurIPS 2023 Open Set Action Recognition via Multi-Label Evidential Learning CVPR 2023 Open-Set Likelihood Maximization for Few-Shot Learning CVPR 2023 Operation-Level Early Stopping for Robustifying Differentiable NAS NeurIPS 2023 Operator Learning for Nonlinear Adaptive Control L4DC 2023 Operator learning with PCA-Net: upper and lower complexity bounds JMLR 2023 Opposite Online Learning via Sequentially Integrated Stochastic Gradient Descent Estimators AAAI 2023 OPT-GAN: A Broad-Spectrum Global Optimizer for Black-Box Problems by Learning Distribution AAAI 2023 Optimal Activation Functions for the Random Features Regression Model ICLR 2023 Optimal Algorithms for Latent Bandits with Cluster Structure AISTATS 2023 Optimal Algorithms for the Inhomogeneous Spiked Wigner Model NeurIPS 2023 Optimal and Fair Encouragement Policy Evaluation and Learning NeurIPS 2023 Optimal and Private Learning from Human Response Data AISTATS 2023 Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces JMLR 2023 Optimal approximation using complex-valued neural networks NeurIPS 2023