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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)

Unsupervised Metric Relocalization Using Transform Consistency Loss CoRL 2020 Unsupervised Multilingual Alignment using Wasserstein Barycenter IJCAI 2020 Unsupervised Opinion Summarization as Copycat-Review Generation ACL 2020 Unsupervised Robust Speech Enhancement Based on Alpha-Stable Fast Multichannel Nonnegative Matrix Factorization Interspeech 2020 Up or Down? Adaptive Rounding for Post-Training Quantization ICML 2020 Using a Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification COLING 2020 Using Context in Neural Machine Translation Training Objectives ACL 2020 Using PRMSE to evaluate automated scoring systems in the presence of label noise ACL 2020 Validated Variational Inference via Practical Posterior Error Bounds AISTATS 2020 Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations AISTATS 2020 VarGrad: A Low-Variance Gradient Estimator for Variational Inference NeurIPS 2020 Variance Reduction and Quasi-Newton for Particle-Based Variational Inference ICML 2020 Variance Reduction for Evolution Strategies via Structured Control Variates AISTATS 2020 Variance reduction for Random Coordinate Descent-Langevin Monte Carlo NeurIPS 2020 Variance Reduction in Stochastic Particle-Optimization Sampling ICML 2020 Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization NeurIPS 2020 Variance Reduction With Sparse Gradients ICLR 2020 Variational Autoencoder with Embedded Student-t Mixture Model for Authorship Attribution COLING 2020 Variational Bayesian Monte Carlo with Noisy Likelihoods NeurIPS 2020 Variational Bayesian Unlearning NeurIPS 2020 Variational Bayes in Private Settings (VIPS) (Extended Abstract) IJCAI 2020 Variational-EM-Based Deep Learning for Noise-Blind Image Deblurring CVPR 2020 Variational Imitation Learning with Diverse-quality Demonstrations ICML 2020 Variational Inference for Computational Imaging Inverse Problems JMLR 2020 Variational Inference for Sequential Data with Future Likelihood Estimates ICML 2020