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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)

AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks AAAI 2019 Auxiliary Variational MCMC ICLR 2019 A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification NAACL 2019 A Variational EM Framework With Adaptive Edge Selection for Blind Motion Deblurring CVPR 2019 A Variational Inequality Perspective on Generative Adversarial Networks ICLR 2019 Average-case Analysis of the Assignment Problem with Independent Preferences IJCAI 2019 Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation NeurIPS 2019 Average-Case Information Complexity of Learning ALT 2019 Avoiding Latent Variable Collapse with Generative Skip Models AISTATS 2019 A Walkthrough for the Principle of Logit Separation IJCAI 2019 A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning ICML 2019 Axiomatic Characterization of Data-Driven Influence Measures for Classification AAAI 2019 Backpropagation-Friendly Eigendecomposition NeurIPS 2019 Backprop with Approximate Activations for Memory-efficient Network Training NeurIPS 2019 Bag of Tricks for Image Classification with Convolutional Neural Networks CVPR 2019 Balanced Linear Contextual Bandits AAAI 2019 Balanced Ranking with Diversity Constraints IJCAI 2019 Balancing Efficiency and Fairness in On-Demand Ridesourcing NeurIPS 2019 Balancing Relevance and Diversity in Online Bipartite Matching via Submodularity AAAI 2019 Banded Matrix Operators for Gaussian Markov Models in the Automatic Differentiation Era AISTATS 2019 Bandit Online Learning with Unknown Delays AISTATS 2019 Bandit Principal Component Analysis COLT 2019 Bandits with Feedback Graphs and Switching Costs NeurIPS 2019 Band-Limited Gaussian Processes: The Sinc Kernel NeurIPS 2019 Band-limited Training and Inference for Convolutional Neural Networks ICML 2019