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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)

Sampling from Non-Log-Concave Distributions via Variance-Reduced Gradient Langevin Dynamics AISTATS 2019 Sampling Wisely: Deep Image Embedding by Top-K Precision Optimization ICCV 2019 Satisfaction and Implication of Integrity Constraints in Ontology-based Data Access IJCAI 2019 SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver ICML 2019 Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy AAAI 2019 Scalable Approximations for Generalized Linear Problems JMLR 2019 Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes NeurIPS 2019 Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models NeurIPS 2019 Scalable Bayesian Learning for State Space Models using Variational Inference with SMC Samplers AISTATS 2019 Scalable Bayesian Non-linear Matrix Completion IJCAI 2019 Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning IJCAI 2019 Scalable Collapsed Inference for High-Dimensional Topic Models NAACL 2019 Scalable Distributed DL Training: Batching Communication and Computation AAAI 2019 Scalable Gaussian Process Inference with Finite-data Mean and Variance Guarantees AISTATS 2019 Scalable Global Optimization via Local Bayesian Optimization NeurIPS 2019 Scalable inference of topic evolution via models for latent geometric structures NeurIPS 2019 Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets ICML 2019 Scalable Semi-Supervised SVM via Triply Stochastic Gradients IJCAI 2019 Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference NeurIPS 2019 Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data NeurIPS 2019 Scalable Thompson Sampling via Optimal Transport AISTATS 2019 Scalable Training of Inference Networks for Gaussian-Process Models ICML 2019 Scalable Unbalanced Optimal Transport using Generative Adversarial Networks ICLR 2019 Scalable Verified Training for Provably Robust Image Classification ICCV 2019 Scaling Community Cellular Networks with CommunityCellularManager NSDI 2019