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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)

GO Gradient for Expectation-Based Objectives ICLR 2019 GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver ICML 2019 Good Initializations of Variational Bayes for Deep Models ICML 2019 Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning NeurIPS 2019 GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism NeurIPS 2019 GPSfM: Global Projective SFM Using Algebraic Constraints on Multi-View Fundamental Matrices CVPR 2019 Gradient-based Adaptive Markov Chain Monte Carlo NeurIPS 2019 Gradient-Based Inference for Networks with Output Constraints AAAI 2019 Gradient based sample selection for online continual learning NeurIPS 2019 Gradient-based Training of Slow Feature Analysis by Differentiable Approximate Whitening ACML 2019 Gradient descent aligns the layers of deep linear networks ICLR 2019 Gradient Descent Finds Global Minima of Deep Neural Networks ICML 2019 Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds COLT 2019 Gradient Descent Provably Optimizes Over-parameterized Neural Networks ICLR 2019 Gradient Dynamics of Shallow Univariate ReLU Networks NeurIPS 2019 Gradient Harmonized Single-Stage Detector AAAI 2019 Gradient Information for Representation and Modeling NeurIPS 2019 Graph-based Discriminators: Sample Complexity and Expressiveness NeurIPS 2019 Graph-Based Semi-Supervised Learning with Non-ignorable Non-response NeurIPS 2019 Graph Convolutional Gaussian Processes ICML 2019 Graphical Lasso and Thresholding: Equivalence and Closed-form Solutions JMLR 2019 Graph Policy Gradients for Large Scale Robot Control CoRL 2019 Graph Reduction with Spectral and Cut Guarantees JMLR 2019 Graph Resistance and Learning from Pairwise Comparisons ICML 2019 Graph Structured Prediction Energy Networks NeurIPS 2019