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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

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52
352
828
3,646
5,854
2000
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Papers (including subtopics)

Label Information Bottleneck for Label Enhancement CVPR 2023 Label Propagation with Weak Supervision ICLR 2023 Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency NeurIPS 2023 Label Smoothing for Emotion Detection (Student Abstract) AAAI 2023 LABO: Towards Learning Optimal Label Regularization via Bi-level Optimization ACL 2023 LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding AAAI 2023 LagrangeBench: A Lagrangian Fluid Mechanics Benchmarking Suite NeurIPS 2023 Landscape Learning for Neural Network Inversion ICCV 2023 Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information NeurIPS 2023 Langevin Diffusion Variational Inference AISTATS 2023 Langevin Thompson Sampling with Logarithmic Communication: Bandits and Reinforcement Learning ICML 2023 Language Model Decoding as Likelihood–Utility Alignment EACL 2023 Language Model Pre-training on True Negatives AAAI 2023 Language Model Quality Correlates with Psychometric Predictive Power in Multiple Languages EMNLP 2023 Language to Rewards for Robotic Skill Synthesis CoRL 2023 Large data limit of the MBO scheme for data clustering: convergence of the dynamics JMLR 2023 Large deviations rates for stochastic gradient descent with strongly convex functions AISTATS 2023 Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning NeurIPS 2023 Large language models implicitly learn to straighten neural sentence trajectories to construct a predictive representation of natural language. NeurIPS 2023 Large-Scale Correlation Analysis of Automated Metrics for Topic Models ACL 2023 Large-Scale Distributed Learning via Private On-Device LSH NeurIPS 2023 Large Selective Kernel Network for Remote Sensing Object Detection ICCV 2023 Large-State Reinforcement Learning for Hyper-Heuristics AAAI 2023 Last-Iterate Convergent Policy Gradient Primal-Dual Methods for Constrained MDPs NeurIPS 2023 Last Switch Dependent Bandits with Monotone Payoff Functions ICML 2023