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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)

Deterministic Variational Inference for Robust Bayesian Neural Networks ICLR 2019 DetNAS: Backbone Search for Object Detection NeurIPS 2019 DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation NeurIPS 2019 Diagnosing and Enhancing VAE Models ICLR 2019 Diagnosing Bottlenecks in Deep Q-learning Algorithms ICML 2019 Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks NeurIPS 2019 Dichromatic Model Based Temporal Color Constancy for AC Light Sources CVPR 2019 Differentiable Cloth Simulation for Inverse Problems NeurIPS 2019 Differentiable Convex Optimization Layers NeurIPS 2019 Differentiable Dynamic Normalization for Learning Deep Representation ICML 2019 Differentiable Game Mechanics JMLR 2019 Differentiable Kernel Evolution ICCV 2019 Differentiable Linearized ADMM ICML 2019 Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning - Extended Abtract IJCAI 2019 Differentiable Ranking and Sorting using Optimal Transport NeurIPS 2019 Differentiable reservoir computing JMLR 2019 Differentiable Sampling with Flexible Reference Word Order for Neural Machine Translation NAACL 2019 Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory ICML 2019 Differentially Private Algorithms for Learning Mixtures of Separated Gaussians NeurIPS 2019 Differentially Private Anonymized Histograms NeurIPS 2019 Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate NeurIPS 2019 Differentially Private Bayesian Linear Regression NeurIPS 2019 Differentially Private Empirical Risk Minimization with Non-convex Loss Functions ICML 2019 Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View AAAI 2019 Differentially Private Iterative Gradient Hard Thresholding for Sparse Learning IJCAI 2019