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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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828
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2000
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Papers (including subtopics)

Estimation of Markov Chain via Rank-Constrained Likelihood ICML 2018 Estimation of the Number of Speakers with Variational Bayesian PLDA in the DIHARD Diarization Challenge. Interspeech 2018 Estimation with Incomplete Data: The Linear Case IJCAI 2018 Estimators for Multivariate Information Measures in General Probability Spaces NeurIPS 2018 Evaluating Historical Text Normalization Systems: How Well Do They Generalize? NAACL 2018 Evaluating the Ability of LSTMs to Learn Context-Free Grammars EMNLP 2018 Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach ICLR 2018 Evidential Deep Learning to Quantify Classification Uncertainty NeurIPS 2018 Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks NeurIPS 2018 Evolving Learning for Analysing Mood-Related Infant Vocalisation Interspeech 2018 Exact and Robust Conformal Inference Methods for Predictive Machine Learning with Dependent Data COLT 2018 Exact Low Tubal Rank Tensor Recovery from Gaussian Measurements IJCAI 2018 Exact natural gradient in deep linear networks and its application to the nonlinear case NeurIPS 2018 Expectation-Maximization Algorithms for Itakura-Saito Nonnegative Matrix Factorization Interspeech 2018 Experienced Optimization with Reusable Directional Model for Hyper-Parameter Search IJCAI 2018 Experimental Design under the Bradley-Terry Model IJCAI 2018 Explaining Deep Learning Models -- A Bayesian Non-parametric Approach NeurIPS 2018 Explaining Multi-Criteria Decision Aiding Models with an Extended Shapley Value IJCAI 2018 Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks CVPR 2018 Explicitly modeling case improves neural dependency parsing EMNLP 2018 Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression NeurIPS 2018 Exploiting Stochasticity for the Control of Transitions in Gyre Flows RSS 2018 Exploiting Strategy-Space Diversity for Batch Bayesian Optimization AISTATS 2018 Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search ICML 2018 Exploration by Distributional Reinforcement Learning IJCAI 2018