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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)

Predicting Training Time Without Training NeurIPS 2020 Predicting Typological Features in WALS using Language Embeddings and Conditional Probabilities: ÚFAL Submission to the SIGTYP 2020 Shared Task EMNLP 2020 Prediction Intervals: Split Normal Mixture from Quality-Driven Deep Ensembles UAI 2020 Prediction of High Risk of Deviations in Home Care Deliveries PGM 2020 Prediction regions through Inverse Regression JMLR 2020 Prediction with Corrupted Expert Advice NeurIPS 2020 Predictive Coding for Locally-Linear Control ICML 2020 Predictive inference is free with the jackknife+-after-bootstrap NeurIPS 2020 Preference Modeling with Context-Dependent Salient Features ICML 2020 Price of Fairness in Budget Division and Probabilistic Social Choice AAAI 2020 Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm NeurIPS 2020 Prime Sample Attention in Object Detection CVPR 2020 Principled learning method for Wasserstein distributionally robust optimization with local perturbations ICML 2020 Principled Weight Initialization for Hypernetworks ICLR 2020 Prior-aware Composition Inference for Spectral Topic Models AISTATS 2020 Privacy-Preserving Gaussian Process Regression – A Modular Approach to the Application of Homomorphic Encryption AAAI 2020 Privacy-Preserving Gradient Boosting Decision Trees AAAI 2020 Private Bayesian Persuasion with Sequential Games AAAI 2020 Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead ICML 2020 Private Identity Testing for High-Dimensional Distributions NeurIPS 2020 Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity NeurIPS 2020 Privately Answering Classification Queries in the Agnostic PAC Model ALT 2020 Privately detecting changes in unknown distributions ICML 2020 Privately Learning Thresholds: Closing the Exponential Gap COLT 2020 Private Mean Estimation of Heavy-Tailed Distributions COLT 2020