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Strongly Adaptive Online Learning

arXiv 1502.07073 ↗First posted 25 Feb 2015cs.LGcited by 102

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Abstract (arXiv)

Strongly adaptive algorithms are algorithms whose performance on every time interval is close to optimal. We present a reduction that can transform standard low-regret algorithms to strongly adaptive. As a consequence, we derive simple, yet efficient, strongly adaptive algorithms for a handful of problems.

Key facts. Cited by 102 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Feb 2015.

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Citing papers per year: 2021: 20; 2022: 24; 2023: 15; 2024: 17; 2025: 10; 2026: 16.

102 papers.

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  1. 2021Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approachcited by 98
  2. 2023Improved Online Conformal Prediction via Strongly Adaptive Online Learningcited by 76
  3. 2021A Simple Approach for Non-stationary Linear Banditscited by 54
  4. 2024Online conformal prediction with decaying step sizescited by 53
  5. 2021Impossible Tuning Made Possible: A New Expert Algorithm and Its Applicationscited by 52
  6. 2022Parameter-free Mirror Descentcited by 45
  7. 2023Optimal Stochastic Non-smooth Non-convex Optimization through Online-to-Non-convex Conversioncited by 42
  8. 2021Optimal Dynamic Regret in Exp-Concave Online Learningcited by 39
  9. 2021Non-stationary Online Learning with Memory and Non-stochastic Controlcited by 33
  10. 2023A Unifying Perspective on Multi-Calibration: Game Dynamics for Multi-Objective Learningcited by 29
  11. 2022Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyondcited by 27
  12. 2022New Projection-free Algorithms for Online Convex Optimization with Adaptive Regret Guaranteescited by 26
38 more
  1. 2023Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agentscited by 25
  2. 2024Understanding Adam Optimizer via Online Learning of Updates: Adam is FTRL in Disguisecited by 23
  3. 2022Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Banditscited by 22
  4. 2022Adaptive Fairness-Aware Online Meta-Learning for Changing Environmentscited by 17
  5. 2023Efficient Methods for Non-stationary Online Learningcited by 15
  6. 2021Adversarial Tracking Control via Strongly Adaptive Online Learning with Memorycited by 14
  7. 2021Beyond Bandit Feedback in Online Multiclass Classificationcited by 14
  8. 2022Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methodscited by 14
  9. 2021A closer look at temporal variability in dynamic online learningcited by 13
  10. 2021Stochastic Optimization under Distributional Driftcited by 13
  11. 2022Online Control of Unknown Time-Varying Dynamical Systemscited by 13
  12. 2022Dynamic Regret of Online Markov Decision Processescited by 13
  13. 2022Projection-free Adaptive Regret with Membership Oraclescited by 13
  14. 2023Non-stationary Projection-free Online Learning with Dynamic and Adaptive Regret Guaranteescited by 13
  15. 2022Adaptive Online Learning of Quantum Statescited by 12
  16. 2024Discounted Adaptive Online Learning: Towards Better Regularizationcited by 12
  17. 2023Unconstrained Dynamic Regret via Sparse Codingcited by 11
  18. 2023Online Label Shift: Optimal Dynamic Regret meets Practical Algorithmscited by 11
  19. 2023Towards Fair Disentangled Online Learning for Changing Environmentscited by 11
  20. 2021Optimal and Efficient Algorithms for General Mixable Losses against Switching Oraclescited by 10
  21. 2022Momentum Aggregation for Private Non-convex ERMcited by 10
  22. 2024Model Assessment and Selection under Temporal Distribution Shiftcited by 10
  23. 2024Online Linear Regression in Dynamic Environments via Discountingcited by 10
  24. 2021An Optimal Reduction of TV-Denoising to Adaptive Online Learningcited by 9
  25. 2022Damped Online Newton Step for Portfolio Selectioncited by 9
  26. 2022Optimal Comparator Adaptive Online Learning with Switching Costcited by 9
  27. 2023A Stability Principle for Learning under Non-Stationaritycited by 9
  28. 2021Learning and Information in Stochastic Networks and Queuescited by 8
  29. 2022Adaptive Gradient Methods with Local Guaranteescited by 8
  30. 2023Improved Dynamic Regret for Online Frank-Wolfecited by 8
  31. 2024From Linear to Linearizable Optimization: A Novel Framework with Applications to Stationary and Non-stationary DR-submodular Optimizationcited by 8
  32. 2024Multi-model Ensemble Conformal Prediction in Dynamic Environmentscited by 8
  33. 2022Second Order Path Variationals in Non-Stationary Online Learningcited by 7
  34. 2021Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedbackcited by 6
  35. 2022Smoothed Online Convex Optimization Based on Discounted-Normal-Predictorcited by 6
  36. 2022Optimal Dynamic Regret in LQR Controlcited by 6
  37. 2023Adaptive, Doubly Optimal No-Regret Learning in Strongly Monotone and Exp-Concave Games with Gradient Feedbackcited by 6
  38. 2024Universal Online Convex Optimization with 1 Projection per Roundcited by 6

50 most cited of 102 All 102 (JSON)