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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)

Uncertainty Visualization via Low-Dimensional Posterior Projections CVPR 2024 Uncertainty-Weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation WACV 2024 Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised Learning ICLR 2024 Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguise ICML 2024 Understanding and Addressing the Under-Translation Problem from the Perspective of Decoding Objective ACL 2024 Understanding and Diagnosing Deep Reinforcement Learning ICML 2024 Understanding and Improving Optimization in Predictive Coding Networks AAAI 2024 Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective CVPR 2024 Understanding and Improving Training-free Loss-based Diffusion Guidance NeurIPS 2024 Understanding and Leveraging the Learning Phases of Neural Networks AAAI 2024 Understanding and Minimising Outlier Features in Transformer Training NeurIPS 2024 Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression ICLR 2024 Understanding Certified Training with Interval Bound Propagation ICLR 2024 Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory ICLR 2024 Understanding Domain Generalization: A Noise Robustness Perspective ICLR 2024 Understanding Emergent Abilities of Language Models from the Loss Perspective NeurIPS 2024 Understanding Entropic Regularization in GANs JMLR 2024 Understanding Expressivity of GNN in Rule Learning ICLR 2024 Understanding Forgetting in Continual Learning with Linear Regression ICML 2024 Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure NeurIPS 2024 Understanding Generalization of Federated Learning via Stability: Heterogeneity Matters AISTATS 2024 Understanding Heterophily for Graph Neural Networks ICML 2024 Understanding Higher-Order Correlations Among Semantic Components in Embeddings EMNLP 2024 Understanding In-Context Learning from Repetitions ICLR 2024 Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective NeurIPS 2024