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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)

Learning dynamic polynomial proofs NeurIPS 2019 Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning NeurIPS 2019 Learning Embeddings into Entropic Wasserstein Spaces ICLR 2019 Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations ICML 2019 Learning Flexible Latent Representations via Encapsulated Variational Encoders AAAI 2019 Learning for Single-Shot Confidence Calibration in Deep Neural Networks Through Stochastic Inferences CVPR 2019 Learning from Bad Data via Generation NeurIPS 2019 Learning (from) Deep Hierarchical Structure among Features AAAI 2019 Learning From Noisy Labels by Regularized Estimation of Annotator Confusion CVPR 2019 Learning from Weakly Dependent Data under Dobrushin’s Condition COLT 2019 Learning Generative Models across Incomparable Spaces ICML 2019 Learning Hawkes Processes from a handful of events NeurIPS 2019 Learning Hawkes Processes Under Synchronization Noise ICML 2019 Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction AAAI 2019 Learning Hierarchical Priors in VAEs NeurIPS 2019 Learning in Non-convex Games with an Optimization Oracle COLT 2019 Learning interpretable continuous-time models of latent stochastic dynamical systems ICML 2019 Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting IJCAI 2019 Learning in the Machine: Random Backpropagation and the Deep Learning Channel (Extended Abstract) IJCAI 2019 Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial Crowds EMNLP 2019 Learning Linear Dynamical Systems with Semi-Parametric Least Squares COLT 2019 Learning Local Descriptors With a CDF-Based Dynamic Soft Margin ICCV 2019 Learning Local Search Heuristics for Boolean Satisfiability NeurIPS 2019 Learning Logistic Circuits AAAI 2019 Learning Mixtures of Plackett-Luce Models from Structured Partial Orders NeurIPS 2019