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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)

Improved Random Features for Dot Product Kernels JMLR 2024 Improved Regret Bounds for Non-Convex Online-Within-Online Meta Learning ICLR 2024 Improved Regret Bounds of (Multinomial) Logistic Bandits via Regret-to-Confidence-Set Conversion AISTATS 2024 Improved Regret for Bandit Convex Optimization with Delayed Feedback NeurIPS 2024 Improved Regret of Linear Ensemble Sampling NeurIPS 2024 Improved Sample Complexity Analysis of Natural Policy Gradient Algorithm with General Parameterization for Infinite Horizon Discounted Reward Markov Decision Processes AISTATS 2024 Improved Sample Complexity Bounds for Diffusion Model Training NeurIPS 2024 Improved Sample Complexity for Multiclass PAC Learning NeurIPS 2024 Improved sampling via learned diffusions ICLR 2024 Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm ICML 2024 Improved statistical and computational complexity of the mean-field Langevin dynamics under structured data ICLR 2024 Improved Techniques for Training Consistency Models ICLR 2024 Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training ICML 2024 Improving Adaptive Online Learning Using Refined Discretization ALT 2024 Improving Adaptivity via Over-Parameterization in Sequence Models NeurIPS 2024 Improving Adversarial Energy-Based Model via Diffusion Process ICML 2024 Improving Alignment and Robustness with Circuit Breakers NeurIPS 2024 Improving Antibody Humanness Prediction using Patent Data ICML 2024 Improving Computational Complexity in Statistical Models with Local Curvature Information ICML 2024 Improving Convergence and Generalization Using Parameter Symmetries ICLR 2024 Improving Deep Learning Optimization through Constrained Parameter Regularization NeurIPS 2024 Improving Depth Completion via Depth Feature Upsampling CVPR 2024 Improving Diffusion Models for Inverse Problems Using Optimal Posterior Covariance ICML 2024 Improving Discriminative Capability of Reward Models in RLHF Using Contrastive Learning EMNLP 2024 Improving Domain Generalization with Domain Relations ICLR 2024