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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)

Learning Set Functions with Implicit Differentiation AAAI 2025 Learning sparse generalized linear models with binary outcomes via iterative hard thresholding COLT 2025 Learning Spatiotemporal Dynamical Systems from Point Process Observations ICLR 2025 Learning stochastic dynamics from snapshots through regularized unbalanced optimal transport ICLR 2025 Learning Structured Representations by Embedding Class Hierarchy with Fast Optimal Transport ICLR 2025 Learning Structured Universe Graph with Outlier OOD Detection for Partial Matching ICLR 2025 Learning Successor Features with Distributed Hebbian Temporal Memory ICLR 2025 Learning the Pareto Front Using Bootstrapped Observation Samples AISTATS 2025 Learning to Align: Addressing Character Frequency Distribution Shifts in Handwritten Text Recognition EMNLP 2025 Learning to Call: A Field Trial of a Collaborative Bandit Algorithm for Optimizing Call Timing in Mobile Maternal Health MLHC 2025 Learning to Collaborate with Unknown Agents in the Absence of Reward AAAI 2025 Learning to Describe Implicit Changes: Noise-robust Pre-training for Image Difference Captioning EMNLP 2025 Learning to Discretize Denoising Diffusion ODEs ICLR 2025 Learning to Generate Projections for Reducing Dimensionality of Heterogeneous Linear Programming Problems ICML 2025 Learning Together to Perform Better: Teaching Small-Scale LLMs to Collaborate via Preferential Rationale Tuning ACL 2025 Learning to Inference Adaptively for Multimodal Large Language Models ICCV 2025 Learning to Instruct: Fine-Tuning a Task-Aware Instruction Optimizer for Black-Box LLMs EMNLP 2025 Learning to Match Unpaired Data with Minimum Entropy Coupling ICML 2025 Learning to Normalize on the SPD Manifold under Bures-Wasserstein Geometry CVPR 2025 Learning-to-Optimize with PAC-Bayesian Guarantees: Theoretical Considerations and Practical Implementation JMLR 2025 Learning to Plan & Reason for Evaluation with Thinking-LLM-as-a-Judge ICML 2025 Learning to Reason from Feedback at Test-Time ACL 2025 Learning to Rewind via Iterative Prediction of Past Weights for Practical Unlearning AAAI 2025 Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation CVPR 2025 Learning to Sample in Stochastic Optimization UAI 2025