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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)

Learning Parities with Neural Networks NeurIPS 2020 Learning piecewise Lipschitz functions in changing environments AISTATS 2020 Learning Polynomials in Few Relevant Dimensions COLT 2020 Learning Rank-1 Diffractive Optics for Single-Shot High Dynamic Range Imaging CVPR 2020 Learning Rate Adaptation for Differentially Private Learning AISTATS 2020 Learning Reasoning Strategies in End-to-End Differentiable Proving ICML 2020 Learning Restricted Boltzmann Machines with Sparse Latent Variables NeurIPS 2020 Learning Robust Decision Policies from Observational Data NeurIPS 2020 Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search NeurIPS 2020 Learning Selection Strategies in Buchberger’s Algorithm ICML 2020 Learning Some Popular Gaussian Graphical Models without Condition Number Bounds NeurIPS 2020 Learning spectrograms with convolutional spectral kernels AISTATS 2020 Learning Stability Certificates from Data CoRL 2020 Learning Structured Distributions From Untrusted Batches: Faster and Simpler NeurIPS 2020 Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective ICML 2020 Learning Student Networks with Few Data AAAI 2020 Learning Sums of Independent Random Variables with Sparse Collective Support JMLR 2020 Learning the Compositional Visual Coherence for Complementary Recommendations IJCAI 2020 Learning the Globally Optimal Distributed LQ Regulator L4DC 2020 Learning the Linear Quadratic Regulator from Nonlinear Observations NeurIPS 2020 Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling ICML 2020 Learning to Auto Weight: Entirely Data-Driven and Highly Efficient Weighting Framework AAAI 2020 Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks ICLR 2020 Learning to Branch for Multi-Task Learning ICML 2020 Learning to Crawl AAAI 2020