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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)

Improving Maximum k-plex Solver via Second-Order Reduction and Graph Color Bounding AAAI 2021 Improving Model Generalization: A Chinese Named Entity Recognition Case Study ACL 2021 Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity ICML 2021 Improving Multimodal fusion via Mutual Dependency Maximisation EMNLP 2021 Improving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis EMNLP 2021 Improving Neural Network Efficiency via Post-Training Quantization With Adaptive Floating-Point ICCV 2021 Improving predictions of Bayesian neural nets via local linearization AISTATS 2021 Improving Privacy Guarantee and Efficiency of Latent Dirichlet Allocation Model Training Under Differential Privacy EMNLP 2021 Improving Relational Regularized Autoencoders with Spherical Sliced Fused Gromov Wasserstein ICLR 2021 Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program) JMLR 2021 Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations WACV 2021 Improving Robustness to Model Inversion Attacks via Mutual Information Regularization AAAI 2021 Improving Simultaneous Translation by Incorporating Pseudo-References with Fewer Reorderings EMNLP 2021 Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions AAAI 2021 Improving the Performance-Compatibility Tradeoff with Personalized Objective Functions (Student Abstract) AAAI 2021 Improving Ultrametrics Embeddings Through Coresets ICML 2021 Improving uncertainty calibration of deep neural networks via truth discovery and geometric optimization UAI 2021 iNAS: Integral NAS for Device-Aware Salient Object Detection ICCV 2021 Incentive-Aware PAC Learning AAAI 2021 Incentivized Bandit Learning with Self-Reinforcing User Preferences ICML 2021 Incentivizing Compliance with Algorithmic Instruments ICML 2021 Incorporating Learnable Membrane Time Constant To Enhance Learning of Spiking Neural Networks ICCV 2021 Incorporating Queueing Dynamics into Schedule-Driven Traffic Control IJCAI 2021 Incorporating tone in the calculation of phonotactic probability ACL 2021 Incorporating Unlabeled Data into Distributionally Robust Learning JMLR 2021