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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)

Predictor-corrector algorithms for stochastic optimization under gradual distribution shift ICLR 2023 Predict-then-Calibrate: A New Perspective of Robust Contextual LP NeurIPS 2023 PreDiff: Precipitation Nowcasting with Latent Diffusion Models NeurIPS 2023 Preference-Controlled Multi-Objective Reinforcement Learning for Conditional Text Generation AAAI 2023 Preferential Subsampling for Stochastic Gradient Langevin Dynamics AISTATS 2023 Prefix Propagation: Parameter-Efficient Tuning for Long Sequences ACL 2023 Pre-RMSNorm and Pre-CRMSNorm Transformers: Equivalent and Efficient Pre-LN Transformers NeurIPS 2023 Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction EMNLP 2023 Pre-training for Speech Translation: CTC Meets Optimal Transport ICML 2023 Pretraining Without Attention EMNLP 2023 Pricing against a Budget and ROI Constrained Buyer AISTATS 2023 Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems ICML 2023 Primal-Attention: Self-attention through Asymmetric Kernel SVD in Primal Representation NeurIPS 2023 Principled and Efficient Motif Finding for Structure Learning of Lifted Graphical Models AAAI 2023 Principled Approaches for Private Adaptation from a Public Source AISTATS 2023 Principled Weight Initialisation for Input-Convex Neural Networks NeurIPS 2023 PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning NeurIPS 2023 Prioritizing Samples in Reinforcement Learning with Reducible Loss NeurIPS 2023 PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning. NeurIPS 2023 Prior Specification for Bayesian Matrix Factorization via Prior Predictive Matching JMLR 2023 PRISE: Demystifying Deep Lucas-Kanade With Strongly Star-Convex Constraints for Multimodel Image Alignment CVPR 2023 Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design ICML 2023 Privacy-Aware Rejection Sampling JMLR 2023 Privacy-Preserved Evolutionary Graph Modeling via Gromov-Wasserstein Autoregression AAAI 2023 Privacy-preserving Sparse Generalized Eigenvalue Problem AISTATS 2023