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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)

Direct Feedback Alignment Provides Learning in Deep Neural Networks NeurIPS 2016 Directly Modeling Missing Data in Sequences with RNNs: Improved Classification of Clinical Time Series MLHC 2016 Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data ICML 2016 DISCO Nets : DISsimilarity COefficients Networks NeurIPS 2016 Discrete Distribution Estimation under Local Privacy ICML 2016 Disfluency Detection Using a Bidirectional LSTM Interspeech 2016 Distributed Clustering of Linear Bandits in Peer to Peer Networks ICML 2016 Distributed Coordinate Descent Method for Learning with Big Data JMLR 2016 Distributed Flexible Nonlinear Tensor Factorization NeurIPS 2016 Distributed Submodular Maximization JMLR 2016 DisturbLabel: Regularizing CNN on the Loss Layer CVPR 2016 Diversity-Promoting Bayesian Learning of Latent Variable Models ICML 2016 Do GMM Phoneme Classifiers Perceive Synthetic Sibilants as Humans Do? Interspeech 2016 Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing JMLR 2016 Doubly Convolutional Neural Networks NeurIPS 2016 Doubly Decomposing Nonparametric Tensor Regression ICML 2016 Doubly Robust Off-policy Value Evaluation for Reinforcement Learning ICML 2016 DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression ICML 2016 DrMAD: Distilling Reverse-Mode Automatic Differentiation for Optimizing Hyperparameters of Deep Neural Networks IJCAI 2016 Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning ICML 2016 Dropping Convexity for Faster Semi-definite Optimization COLT 2016 DSA: Decentralized Double Stochastic Averaging Gradient Algorithm JMLR 2016 DUAL-LOCO: Distributing Statistical Estimation Using Random Projections AISTATS 2016 Dual Space Gradient Descent for Online Learning NeurIPS 2016 d-VMP: Distributed Variational Message Passing PGM 2016