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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)

The Statistical Performance of Collaborative Inference JMLR 2016 The Sum-Product Theorem: A Foundation for Learning Tractable Models ICML 2016 The Teaching Dimension of Linear Learners ICML 2016 The Teaching Dimension of Linear Learners JMLR 2016 Threshold Bandits, With and Without Censored Feedback NeurIPS 2016 Threshold Learning for Optimal Decision Making NeurIPS 2016 Tight Complexity Bounds for Optimizing Composite Objectives NeurIPS 2016 Tight (Lower) Bounds for the Fixed Budget Best Arm Identification Bandit Problem COLT 2016 Tight Variational Bounds via Random Projections and I-Projections AISTATS 2016 Time series prediction and online learning COLT 2016 Time-Varying Gaussian Process Bandit Optimization AISTATS 2016 Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers NeurIPS 2016 Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity NeurIPS 2016 Towards Automatically-Tuned Neural Networks AutoML 2016 Towards Faster Rates and Oracle Property for Low-Rank Matrix Estimation ICML 2016 Towards Stability and Optimality in Stochastic Gradient Descent AISTATS 2016 Towards Unifying Hamiltonian Monte Carlo and Slice Sampling NeurIPS 2016 TPOT: A Tree-based Pipeline Optimization Tool for Automating Machine Learning AutoML 2016 Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations CVPR 2016 Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient ICML 2016 Tracking the Best Expert in Non-stationary Stochastic Environments NeurIPS 2016 Tractable and Scalable Schatten Quasi-Norm Approximations for Rank Minimization AISTATS 2016 Tractable Operations for Arithmetic Circuits of Probabilistic Models NeurIPS 2016 Train and Test Tightness of LP Relaxations in Structured Prediction ICML 2016 Train faster, generalize better: Stability of stochastic gradient descent ICML 2016