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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)

Learning Theory for Distribution Regression JMLR 2016 Learning to learn by gradient descent by gradient descent NeurIPS 2016 Learning to Select Pre-Trained Deep Representations With Bayesian Evidence Framework CVPR 2016 Learning Tree Structured Potential Games NeurIPS 2016 Learning under uncertainty: a comparison between R-W and Bayesian approach NeurIPS 2016 Learning Using Anti-Training with Sacrificial Data JMLR 2016 Learning Weight Uncertainty With Stochastic Gradient MCMC for Shape Classification CVPR 2016 Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of ERM Principle JMLR 2016 LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems JMLR 2016 Likelihood Ratio Calculation in Acoustic-Phonetic Forensic Voice Comparison: Comparison of Three Statistical Modelling Approaches Interspeech 2016 Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption JMLR 2016 Linear dynamical neural population models through nonlinear embeddings NeurIPS 2016 Linearized Alternating Direction Method of Multipliers for Constrained Nonconvex Regularized Optimization ACML 2016 Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes NeurIPS 2016 Linear Shape Deformation Models With Local Support Using Graph-Based Structured Matrix Factorisation CVPR 2016 Linking losses for density ratio and class-probability estimation ICML 2016 Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences NeurIPS 2016 Local Minimax Complexity of Stochastic Convex Optimization NeurIPS 2016 Loss Bounds and Time Complexity for Speed Priors AISTATS 2016 Loss factorization, weakly supervised learning and label noise robustness ICML 2016 Loss Functions for Top-k Error: Analysis and Insights CVPR 2016 Loss Minimization and Parameter Estimation with Heavy Tails JMLR 2016 Low-Rank Doubly Stochastic Matrix Decomposition for Cluster Analysis JMLR 2016 Low-Rank Matrix Approximation with Stability ICML 2016 Low-rank tensor completion: a Riemannian manifold preconditioning approach ICML 2016