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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)

Safe Control under Uncertainty with Probabilistic Signal Temporal Logic RSS 2016 Safe Policy Improvement by Minimizing Robust Baseline Regret NeurIPS 2016 Sample Complexity of Automated Mechanism Design NeurIPS 2016 Sampling for Bayesian Program Learning NeurIPS 2016 Satisfying Real-world Goals with Dataset Constraints NeurIPS 2016 Scalable Adaptive Stochastic Optimization Using Random Projections NeurIPS 2016 Scalable Approximate Bayesian Inference for Outlier Detection under Informative Sampling JMLR 2016 Scalable Discrete Sampling as a Multi-Armed Bandit Problem ICML 2016 Scalable Gaussian Process Classification via Expectation Propagation AISTATS 2016 Scalable Gaussian Processes for Characterizing Multidimensional Change Surfaces AISTATS 2016 Scalable geometric density estimation AISTATS 2016 Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters ICML 2016 Scalable MAP inference in Bayesian networks based on a Map-Reduce approach PGM 2016 Scalable MCMC for Mixed Membership Stochastic Blockmodels AISTATS 2016 Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages NeurIPS 2016 Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes NeurIPS 2016 Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-statistics JMLR 2016 Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much NeurIPS 2016 Scene Labeling Using Sparse Precision Matrix CVPR 2016 Score Permutation Based Finite Sample Inference for Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) Models AISTATS 2016 SDCA without Duality, Regularization, and Individual Convexity ICML 2016 SDNA: Stochastic Dual Newton Ascent for Empirical Risk Minimization ICML 2016 SDP Relaxation with Randomized Rounding for Energy Disaggregation NeurIPS 2016 SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques NeurIPS 2016 Selective inference for group-sparse linear models NeurIPS 2016