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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
352
828
3,646
5,854
2000
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Papers (including subtopics)

GP-VAE: Deep Probabilistic Time Series Imputation AISTATS 2020 GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis EMNLP 2020 Gradient ℓ_1 Regularization for Quantization Robustness ICLR 2020 Gradient-based Analysis of NLP Models is Manipulable EMNLP 2020 Gradient-Based Optimization for Bayesian Preference Elicitation AAAI 2020 Gradient Boosts the Approximate Vanishing Ideal AAAI 2020 Gradient descent follows the regularization path for general losses COLT 2020 Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers JMLR 2020 Gradient Descent Maximizes the Margin of Homogeneous Neural Networks ICLR 2020 Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks AISTATS 2020 GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values ICML 2020 Gradient-EM Bayesian Meta-Learning NeurIPS 2020 Gradient Estimation with Stochastic Softmax Tricks NeurIPS 2020 Gradient-guided Unsupervised Lexically Constrained Text Generation EMNLP 2020 Gradientless Descent: High-Dimensional Zeroth-Order Optimization ICLR 2020 Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations AAAI 2020 Gradient Perturbation is Underrated for Differentially Private Convex Optimization IJCAI 2020 Gradients as Features for Deep Representation Learning ICLR 2020 Gradient Surgery for Multi-Task Learning NeurIPS 2020 Gradient Temporal-Difference Learning with Regularized Corrections ICML 2020 Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data AAAI 2020 Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters ICML 2020 Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent JMLR 2020 Graph-Guided Architecture Search for Real-Time Semantic Segmentation CVPR 2020 Graphical continuous Lyapunov models UAI 2020