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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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52
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828
3,646
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2000
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Papers (including subtopics)

Analyzing the discrepancy principle for kernelized spectral filter learning algorithms JMLR 2021 Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels NeurIPS 2021 Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation ACL 2021 Analyzing the tree-layer structure of Deep Forests ICML 2021 An Analysis of Approval-Based Committee Rules for 2D-Euclidean Elections AAAI 2021 An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias NeurIPS 2021 An Analysis of the Adaptation Speed of Causal Models AISTATS 2021 An Architecture for Accelerated Large-Scale Inference of Transformer-Based Language Models NAACL 2021 An Asynchronous Kalman Filter for Hybrid Event Cameras ICCV 2021 Anatomy of OntoGUM—Adapting GUM to the OntoNotes Scheme to Evaluate Robustness of SOTA Coreference Algorithms EMNLP 2021 An Automatic, Simple Ultrasound Biofeedback Parameter for Distinguishing Accurate and Misarticulated Rhotic Syllables Interspeech 2021 An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning NeurIPS 2021 Anchor-Constrained Viterbi for Set-Supervised Action Segmentation CVPR 2021 Anderson acceleration of coordinate descent AISTATS 2021 A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models NeurIPS 2021 An Efficient Algorithm for Deep Stochastic Contextual Bandits AAAI 2021 An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling AISTATS 2021 An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints NeurIPS 2021 An Elastica Geodesic Approach With Convexity Shape Prior ICCV 2021 An Empirical Comparison of Instance Attribution Methods for NLP NAACL 2021 An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers NeurIPS 2021 An Empirical Study of Bayesian Optimization: Acquisition Versus Partition JMLR 2021 An Empirical Study on Hyperparameter Optimization for Fine-Tuning Pre-trained Language Models ACL 2021 An Empirical Study on Neural Keyphrase Generation NAACL 2021 An End-to-End Differentiable Framework for Contact-Aware Robot Design RSS 2021