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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)

Elementary Estimators for Sparse Covariance Matrices and other Structured Moments ICML 2014 Elicitation and Identification of Properties COLT 2014 Empirical Minimum Bayes Risk Prediction: How to Extract an Extra Few % Performance from Vision Models with Just Three More Parameters CVPR 2014 Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors NeurIPS 2014 Ensemble-Based Tracking: Aggregating Crowdsourced Structured Time Series Data ICML 2014 Ensemble Methods for Structured Prediction ICML 2014 EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines JMLR 2014 Estimating Dependency Structures for non-Gaussian Components with Linear and Energy Correlations AISTATS 2014 Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm ICML 2014 Estimating Latent-Variable Graphical Models using Moments and Likelihoods ICML 2014 Estimation with Norm Regularization NeurIPS 2014 Event Detection using Multi-Level Relevance Labels and Multiple Features CVPR 2014 Exact Post Model Selection Inference for Marginal Screening NeurIPS 2014 Exclusive Feature Learning on Arbitrary Structures via ℓ_1,2-norm NeurIPS 2014 Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights NeurIPS 2014 Expectation-Maximization for Learning Determinantal Point Processes NeurIPS 2014 Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables AISTATS 2014 Explicit Link Between Periodic Covariance Functions and State Space Models AISTATS 2014 Exploiting easy data in online optimization NeurIPS 2014 Exploiting the Limits of Structure Learning via Inherent Symmetry AISTATS 2014 Exponential Concentration of a Density Functional Estimator NeurIPS 2014 Exponential Family Matrix Completion under Structural Constraints ICML 2014 Extended and Unscented Gaussian Processes NeurIPS 2014 Extracting Latent Structure From Multiple Interacting Neural Populations NeurIPS 2014 Extremal Mechanisms for Local Differential Privacy NeurIPS 2014