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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

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52
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828
3,646
5,854
2000
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Papers (including subtopics)

Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality NeurIPS 2020 Quantile Graphical Models: a Bayesian Approach JMLR 2020 Quantitative Propagation of Chaos for SGD in Wide Neural Networks NeurIPS 2020 Quantitative stability of optimal transport maps and linearization of the 2-Wasserstein space AISTATS 2020 Quantization Aware Training with Absolute-Cosine Regularization for Automatic Speech Recognition Interspeech 2020 Quantized Compressive Sampling of Stochastic Gradients for Efficient Communication in Distributed Deep Learning AAAI 2020 Quantized Decentralized Stochastic Learning over Directed Graphs ICML 2020 Quantized Variational Inference NeurIPS 2020 Quantum Algorithms for Deep Convolutional Neural Networks ICLR 2020 Quasi-Newton Solver for Robust Non-Rigid Registration CVPR 2020 Querying to Find a Safe Policy under Uncertain Safety Constraints in Markov Decision Processes AAAI 2020 Query Rewriting for Ontology-Mediated Conditional Answers AAAI 2020 R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games ICML 2020 Radial and Directional Posteriors for Bayesian Deep Learning AAAI 2020 Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning AISTATS 2020 Rammer: Enabling Holistic Deep Learning Compiler Optimizations with rTasks OSDI 2020 RandAugment: Practical Automated Data Augmentation with a Reduced Search Space NeurIPS 2020 Random Fourier Features via Fast Surrogate Leverage Weighted Sampling AAAI 2020 Random Hypervolume Scalarizations for Provable Multi-Objective Black Box Optimization ICML 2020 Random Intersection Graphs and Missing Data AAAI 2020 Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation IJCAI 2020 Randomized Block-Diagonal Preconditioning for Parallel Learning ICML 2020 Randomized Exploration for Non-Stationary Stochastic Linear Bandits UAI 2020 Randomized Smoothing of All Shapes and Sizes ICML 2020 Randomized tests for high-dimensional regression: A more efficient and powerful solution NeurIPS 2020