mlsanityData to 7 Oct 2026

Introduction to Online Convex Optimization

arXiv 1909.05207 ↗First posted 7 Sep 2019cs.LG math.OC stat.MLcited by 793

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Cited by 20 in Offline RL and imitation learning from data, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This manuscript portrays optimization as a process. In many practical applications the environment is so complex that it is infeasible to lay out a comprehensive theoretical model and use classical algorithmic theory and mathematical optimization. It is necessary as well as beneficial to take a robust approach, by applying an optimization method that learns as one goes along, learning from experience as more aspects of the problem are observed. This view of optimization as a process has become prominent in varied fields and has led to some spectacular success in modeling and systems that are now part of our daily lives.

Key facts. Cited by 793 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Sep 2019.

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Citing papers per year: 2019: 1; 2020: 1; 2021: 110; 2022: 144; 2023: 150; 2024: 112; 2025: 130; 2026: 145.

793 papers.

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  1. 2021Adaptive Conformal Inference Under Distribution Shiftcited by 360
  2. 2024Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferencescited by 176
  3. 2021Practical and Private (Deep) Learning without Sampling or Shufflingcited by 169
  4. 2024The Road Less Scheduledcited by 158
  5. 2022Adaptive Conformal Predictions for Time Seriescited by 155
  6. 2024A Minimaximalist Approach to Reinforcement Learning from Human Feedbackcited by 141
  7. 2022Conformal Inference for Online Prediction with Arbitrary Distribution Shiftscited by 123
  8. 2023Noise Is Not the Main Factor Behind the Gap Between SGD and Adam on Transformers, but Sign Descent Might Becited by 114
  9. 2022AUC Maximization in the Era of Big Data and AI: A Surveycited by 100
  10. 2021Reward is enough for convex MDPscited by 82
  11. 2021Learning Curve Theorycited by 79
  12. 2023Improved Online Conformal Prediction via Strongly Adaptive Online Learningcited by 76
38 more
  1. 2021Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Datacited by 67
  2. 2021Adaptivity and Non-stationarity: Problem-dependent Dynamic Regret for Online Convex Optimizationcited by 66
  3. 2024Active Preference Optimization for Sample Efficient RLHFcited by 61
  4. 2025It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimizationcited by 60
  5. 2022Making SGD Parameter-Freecited by 50
  6. 2021Public Data-Assisted Mirror Descent for Private Model Trainingcited by 49
  7. 2023Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictionscited by 46
  8. 2022Parameter-free Mirror Descentcited by 45
  9. 2021Distributionally Robust Learningcited by 44
  10. 2022Learning Fast and Slow for Online Time Series Forecastingcited by 43
  11. 2023Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversioncited by 42
  12. 2022Jointly Efficient and Optimal Algorithms for Logistic Banditscited by 41
  13. 2022A Unifying Framework for Online Optimization with Long-Term Constraintscited by 41
  14. 2024Quantifying the Gain in Weak-to-Strong Generalizationcited by 40
  15. 2021Optimal Dynamic Regret in Exp-Concave Online Learningcited by 39
  16. 2023An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learningcited by 39
  17. 2021SGD Generalizes Better Than GD (And Regularization Doesn't Help)cited by 38
  18. 2021Regret and Cumulative Constraint Violation Analysis for Online Convex Optimization with Long Term Constraintscited by 38
  19. 2023Inverse Reinforcement Learning without Reinforcement Learningcited by 35
  20. 2021DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learningcited by 34
  21. 2021Non-stationary Online Learning with Memory and Non-stochastic Controlcited by 33
  22. 2023Optimal Algorithms for Online Convex Optimization with Adversarial Constraintscited by 33
  23. 2024LoRA Done RITE: Robust Invariant Transformation Equilibration for LoRA Optimizationcited by 31
  24. 2021An Online Learning Approach to Interpolation and Extrapolation in Domain Generalizationcited by 30
  25. 2022Contextual Bandits with Packing and Covering Constraints: A Modular Lagrangian Approach via Regressioncited by 30
  26. 2021Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stabilitycited by 29
  27. 2021Efficient Projection-Free Online Convex Optimization with Membership Oraclecited by 29
  28. 2022Learning Infinite-Horizon Average-Reward Markov Decision Processes with Constraintscited by 29
  29. 2023Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximationcited by 29
  30. 2025Think Smarter not Harder: Adaptive Reasoning with Inference Aware Optimizationcited by 29
  31. 2025Amulet: ReAlignment During Test Time for Personalized Preference Adaptation of LLMscited by 29
  32. 2021Online Apprenticeship Learningcited by 28
  33. 2022PDE-Based Optimal Strategy for Unconstrained Online Learningcited by 27
  34. 2022Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyondcited by 27
  35. 2022Generalization Bounds via Convex Analysiscited by 27
  36. 2021MetaGrad: Adaptation using Multiple Learning Rates in Online Learningcited by 26
  37. 2022New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guaranteescited by 26
  38. 2022Adapting to Online Label Shift with Provable Guaranteescited by 26

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