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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)

An Information-Theoretic Analysis for Thompson Sampling with Many Actions NeurIPS 2018 An intriguing failing of convolutional neural networks and the CoordConv solution NeurIPS 2018 A Nonconvex Proximal Splitting Algorithm under Moreau-Yosida Regularization AISTATS 2018 A Non-convolutive NMF Model for Speech Dereverberation Interspeech 2018 An Online Learning Approach to Generative Adversarial Networks ICLR 2018 An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks ICML 2018 An Optimal Learning Algorithm for Online Unconstrained Submodular Maximization COLT 2018 An Optimization Based Approach for Solving Spoken CALL Shared Task Interspeech 2018 An Order-based Algorithm for Learning Structure of Bayesian Networks PGM 2018 A no-regret generalization of hierarchical softmax to extreme multi-label classification NeurIPS 2018 A Novel Normalization Method for Autocorrelation Function for Pitch Detection and for Speech Activity Detection Interspeech 2018 Anticipating Traffic Accidents With Adaptive Loss and Large-Scale Incident DB CVPR 2018 A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks ICLR 2018 A partial orthogonalization method for simulating covariance and concentration graph matrices PGM 2018 A Physically-Consistent Bayesian Non-Parametric Mixture Model for Dynamical System Learning CoRL 2018 A PID Controller Approach for Stochastic Optimization of Deep Networks CVPR 2018 Approximate Bayesian Computation with Kullback-Leibler Divergence as Data Discrepancy AISTATS 2018 Approximate Knowledge Compilation by Online Collapsed Importance Sampling NeurIPS 2018 Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions ICML 2018 Approximate message passing for amplitude based optimization ICML 2018 Approximate Nearest Neighbors in Limited Space COLT 2018 Approximating Real-Time Recurrent Learning with Random Kronecker Factors NeurIPS 2018 Approximation Algorithms for Cascading Prediction Models ICML 2018 Approximation Guarantees for Adaptive Sampling ICML 2018 Approximation Guarantees of Stochastic Greedy Algorithms for Subset Selection IJCAI 2018