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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)

Distributionally Robust Counterfactual Risk Minimization AAAI 2020 Distributionally Robust Federated Averaging NeurIPS 2020 Distributionally Robust Formulation and Model Selection for the Graphical Lasso AISTATS 2020 Distributionally Robust Local Non-parametric Conditional Estimation NeurIPS 2020 Distributionally Robust Neural Networks ICLR 2020 Distributionally Robust Parametric Maximum Likelihood Estimation NeurIPS 2020 Distributionally Robust Policy Evaluation and Learning in Offline Contextual Bandits ICML 2020 Distributional Robustness with IPMs and links to Regularization and GANs NeurIPS 2020 Distribution-Aware Coordinate Representation for Human Pose Estimation CVPR 2020 Distribution-free binary classification: prediction sets, confidence intervals and calibration NeurIPS 2020 Distribution Free Learning with Local Queries ALT 2020 Divergence-Based Motivation for Online EM and Combining Hidden Variable Models UAI 2020 Diversified Bayesian Nonnegative Matrix Factorization AAAI 2020 Diversified Interactive Recommendation with Implicit Feedback AAAI 2020 Diversity can be Transferred: Output Diversification for White- and Black-box Attacks NeurIPS 2020 Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations NeurIPS 2020 Divide-and-Conquer Learning with Nyström: Optimal Rate and Algorithm AAAI 2020 Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support ICML 2020 DMCP: Differentiable Markov Channel Pruning for Neural Networks CVPR 2020 DNNs as Layers of Cooperating Classifiers AAAI 2020 DNU: Deep Non-Local Unrolling for Computational Spectral Imaging CVPR 2020 Document Summarization with VHTM: Variational Hierarchical Topic-Aware Mechanism AAAI 2020 Does label smoothing mitigate label noise? ICML 2020 Does the Markov Decision Process Fit the Data: Testing for the Markov Property in Sequential Decision Making ICML 2020 Does the Objective Matter? Comparing Training Objectives for Pronoun Resolution EMNLP 2020