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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)

Can Graph Neural Networks Count Substructures? NeurIPS 2020 Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study NeurIPS 2020 Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference NeurIPS 2020 Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory NeurIPS 2020 Can the Brain Do Backpropagation? --- Exact Implementation of Backpropagation in Predictive Coding Networks NeurIPS 2020 Can Weight Sharing Outperform Random Architecture Search? An Investigation With TuNAS CVPR 2020 Can We Learn Heuristics for Graphical Model Inference Using Reinforcement Learning? CVPR 2020 Can We Predict the Election Outcome from Sampled Votes? AAAI 2020 Capsule Routing via Variational Bayes AAAI 2020 CAQL: Continuous Action Q-Learning ICLR 2020 CARP: Compression Through Adaptive Recursive Partitioning for Multi-Dimensional Images CVPR 2020 CARS: Continuous Evolution for Efficient Neural Architecture Search CVPR 2020 Cascading Convolutional Color Constancy AAAI 2020 CASTER: Predicting Drug Interactions with Chemical Substructure Representation AAAI 2020 CASTLE: Regularization via Auxiliary Causal Graph Discovery NeurIPS 2020 Catching Cheats: Detecting Strategic Manipulation in Distributed Optimisation of Electric Vehicle Aggregators (Extended Abstract) IJCAI 2020 Causal Bayesian Optimization AISTATS 2020 Causal Discovery from Heterogeneous/Nonstationary Data JMLR 2020 Causal Estimation with Functional Confounders NeurIPS 2020 Causal inference in degenerate systems: An impossibility result AISTATS 2020 Cautious Limit Learning ALT 2020 CCA-Flow: Deep Multi-view Subspace Learning with Inverse Autoregressive Flow ACML 2020 CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation ACL 2020 CD-UAP: Class Discriminative Universal Adversarial Perturbation AAAI 2020 Certifiably Adversarially Robust Detection of Out-of-Distribution Data NeurIPS 2020