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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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'25

Papers (including subtopics)

What Makes You Special? Contrastive Heuristics Based on Qualified Dominance IJCAI 2025 What to Distill? Fast Knowledge Distillation with Adaptive Sampling ICCV 2025 What We Talk About When We Talk About LMs: Implicit Paradigm Shifts and the Ship of Language Models NAACL 2025 When Anchors Meet Cold Diffusion: A Multi-Stage Approach to Lane Detection ICCV 2025 When and why randomised exploration works (in linear bandits) ALT 2025 When Attention Sink Emerges in Language Models: An Empirical View ICLR 2025 When Audio and Text Disagree: Revealing Text Bias in Large Audio-Language Models EMNLP 2025 When can in-context learning generalize out of task distribution? ICML 2025 When Can Proxies Improve the Sample Complexity of Preference Learning? ICML 2025 When Can We Approximate Wide Contrastive Models with Neural Tangent Kernels and Principal Component Analysis? AAAI 2025 When Can We Solve the Weighted Low Rank Approximation Problem in Truly Subquadratic Time? AISTATS 2025 When Confidence Fails: Revisiting Pseudo-Label Selection in Semi-supervised Semantic Segmentation ICCV 2025 When does compositional structure yield compositional generalization? A kernel theory. ICLR 2025 When do neural networks learn world models? ICML 2025 When Dynamic Data Selection Meets Data Augmentation: Achieving Enhanced Training Acceleration ICML 2025 When Every Token Counts: Optimal Segmentation for Low-Resource Language Models COLING 2025 When Evolution Strategy Meets Language Models Tuning COLING 2025 When Graph Neural Networks Meet Dynamic Mode Decomposition ICLR 2025 When Inverse Data Outperforms: Exploring the Pitfalls of Mixed Data in Multi-Stage Fine-Tuning EMNLP 2025 When is Task Vector Provably Effective for Model Editing? A Generalization Analysis of Nonlinear Transformers ICLR 2025 When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs EMNLP 2025 When Long Helps Short: How Context Length in Supervised Fine-tuning Affects Behavior of Large Language Models EMNLP 2025 When LRP Diverges from Leave-One-Out in Transformers EMNLP 2025 When narrower is better: the narrow width limit of Bayesian parallel branching neural networks ICLR 2025 When Should Dense Retrievers Be Updated in Evolving Corpora? Detecting Out-of-Distribution Corpora Using GradNormIR ACL 2025