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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)

Sharing Less is More: Lifelong Learning in Deep Networks with Selective Layer Transfer ICML 2021 Sharp Analysis of a Simple Model for Random Forests AISTATS 2021 Sharper Generalization Bounds for Clustering ICML 2021 Sharper Generalization Bounds for Learning with Gradient-dominated Objective Functions ICLR 2021 Sharp Impossibility Results for Hyper-graph Testing NeurIPS 2021 Sharpness-aware Minimization for Efficiently Improving Generalization ICLR 2021 Shift Invariance Can Reduce Adversarial Robustness NeurIPS 2021 Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks ICML 2021 Shortformer: Better Language Modeling using Shorter Inputs ACL 2021 Show Me How To Revise: Improving Lexically Constrained Sentence Generation with XLNet AAAI 2021 SHPOS: A Theoretical Guaranteed Accelerated Particle Optimization Sampling Method IJCAI 2021 Shuffling Recurrent Neural Networks AAAI 2021 Sifting through the noise: Universal first-order methods for stochastic variational inequalities NeurIPS 2021 SigGPDE: Scaling Sparse Gaussian Processes on Sequential Data ICML 2021 Signaling in Bayesian Network Congestion Games: the Subtle Power of Symmetry AAAI 2021 Signatured Deep Fictitious Play for Mean Field Games with Common Noise ICML 2021 Significance of Gradient Information in Bayesian Optimization AISTATS 2021 Sim and Real: Better Together NeurIPS 2021 SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement NeurIPS 2021 SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios NeurIPS 2021 Simple and Effective Stochastic Neural Networks AAAI 2021 Simple and Fast Algorithms for Interactive Machine Learning with Random Counter-examples JMLR 2021 Simple Augmentation Goes a Long Way: ADRL for DNN Quantization ICLR 2021 Simple combinatorial algorithms for combinatorial bandits: corruptions and approximations UAI 2021 Simple induction of (deterministic) probabilistic finite-state automata for phonotactics by stochastic gradient descent ACL 2021