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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)

Public Data-Assisted Mirror Descent for Private Model Training ICML 2022 Pulling back information geometry AISTATS 2022 Pushing the Efficiency-Regret Pareto Frontier for Online Learning of Portfolios and Quantum States COLT 2022 Pushing the Envelope of Gradient Boosting Forests via Globally-Optimized Oblique Trees CVPR 2022 Pushing the limits of fairness impossibility: Who's the fairest of them all? NeurIPS 2022 Pushing the Limits of Rule Reasoning in Transformers through Natural Language Satisfiability AAAI 2022 Putting WordNet’s Dictionary Examples in the Context of Definition Modelling: An Empirical Analysis AACL 2022 PYLON: A PyTorch Framework for Learning with Constraints AAAI 2022 QCDCL with Cube Learning or Pure Literal Elimination - What is Best? IJCAI 2022 QLSD: Quantised Langevin Stochastic Dynamics for Bayesian Federated Learning AISTATS 2022 QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning ICML 2022 Quadric Hypersurface Intersection for Manifold Learning in Feature Space AISTATS 2022 Quadtree Attention for Vision Transformers ICLR 2022 Quality-Aware Decoding for Neural Machine Translation NAACL 2022 Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding ICML 2022 Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison UAI 2022 Quantified Reproducibility Assessment of NLP Results ACL 2022 Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference NeurIPS 2022 Quantile regression with ReLU Networks: Estimators and minimax rates JMLR 2022 Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation ICLR 2022 Quantization-Aware Deep Optics for Diffractive Snapshot Hyperspectral Imaging CVPR 2022 Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants NeurIPS 2022 Quantum perceptron revisited: Computational-statistical tradeoffs UAI 2022 Quantum Speedups of Optimizing Approximately Convex Functions with Applications to Logarithmic Regret Stochastic Convex Bandits NeurIPS 2022 Quasi-Newton Methods for Saddle Point Problems NeurIPS 2022