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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)

Meta-Descent for Online, Continual Prediction AAAI 2019 MetaInit: Initializing learning by learning to initialize NeurIPS 2019 Meta-Interpretive Learning Using HEX-Programs IJCAI 2019 Meta Learning for Hyperparameter Optimization in Dialogue System Interspeech 2019 Meta-Learning For Stochastic Gradient MCMC ICLR 2019 Meta-Learning With Differentiable Convex Optimization CVPR 2019 Meta-Learning with Implicit Gradients NeurIPS 2019 Meta Learning with Relational Information for Short Sequences NeurIPS 2019 MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization NeurIPS 2019 MetaStyle: Three-Way Trade-off among Speed, Flexibility, and Quality in Neural Style Transfer AAAI 2019 Meta-Surrogate Benchmarking for Hyperparameter Optimization NeurIPS 2019 Metatrace Actor-Critic: Online Step-Size Tuning by Meta-gradient Descent for Reinforcement Learning Control IJCAI 2019 Metric-Optimized Example Weights ICML 2019 Metropolis-Hastings Generative Adversarial Networks ICML 2019 MFAS: Multimodal Fusion Architecture Search CVPR 2019 MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models AAAI 2019 MFPCA: Multiscale Functional Principal Component Analysis AAAI 2019 Migration as Submodular Optimization AAAI 2019 Minimal Achievable Sufficient Statistic Learning ICML 2019 Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters ICLR 2019 Minimal Sample Subspace Learning: Theory and Algorithms JMLR 2019 Minimal Solvers for Mini-Loop Closures in 3D Multi-Scan Alignment CVPR 2019 Minimal Variance Sampling in Stochastic Gradient Boosting NeurIPS 2019 Minimax experimental design: Bridging the gap between statistical and worst-case approaches to least squares regression COLT 2019 Minimax Learning of Ergodic Markov Chains ALT 2019