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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)

Ensemble of Students Taught by Probabilistic Teachers to Improve Speech Emotion Recognition Interspeech 2020 Ensembles of Locally Independent Prediction Models AAAI 2020 Ensembling geophysical models with Bayesian Neural Networks NeurIPS 2020 Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form NeurIPS 2020 Entrywise convergence of iterative methods for eigenproblems NeurIPS 2020 Equalization Loss for Long-Tailed Object Recognition CVPR 2020 ERMI at PARSEME Shared Task 2020: Embedding-Rich Multiword Expression Identification COLING 2020 Error-Bounded Correction of Noisy Labels ICML 2020 Error bounds in estimating the out-of-sample prediction error using leave-one-out cross validation in high-dimensions AISTATS 2020 Error Estimation for Sketched SVD via the Bootstrap ICML 2020 Escaping Saddle-Point Faster under Interpolation-like Conditions NeurIPS 2020 Escaping Saddle Points Faster with Stochastic Momentum ICLR 2020 Escaping the Gravitational Pull of Softmax NeurIPS 2020 ES-MAML: Simple Hessian-Free Meta Learning ICLR 2020 Estimate Sequences for Stochastic Composite Optimization: Variance Reduction, Acceleration, and Robustness to Noise JMLR 2020 Estimating decision tree learnability with polylogarithmic sample complexity NeurIPS 2020 Estimating Gradients for Discrete Random Variables by Sampling without Replacement ICLR 2020 Estimating Model Uncertainty of Neural Networks in Sparse Information Form ICML 2020 Estimating Mutual Information Between Dense Word Embeddings ACL 2020 Estimating predictive uncertainty for rumour verification models ACL 2020 Estimating Principal Components under Adversarial Perturbations COLT 2020 Estimating Q(s,s’) with Deep Deterministic Dynamics Gradients ICML 2020 Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks NeurIPS 2020 Estimating Stochastic Linear Combination of Non-Linear Regressions AAAI 2020 Estimating the Error of Randomized Newton Methods: A Bootstrap Approach ICML 2020