{"total":18,"rows":[{"id":"2205.13451","title":"Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback","created":"2022-05-26","cited_by_count":17,"area":null},{"id":"2207.07974","title":"Online Prediction in Sub-linear Space","created":"2022-07-16","cited_by_count":16,"area":null},{"id":"2303.01673","title":"Near Optimal Memory-Regret Tradeoff for Online Learning","created":"2023-03-03","cited_by_count":15,"area":null},{"id":"2302.04658","title":"The Sample Complexity of Approximate Rejection Sampling with Applications to Smoothed Online Learning","created":"2023-02-09","cited_by_count":13,"area":null},{"id":"2402.14987","title":"On the Performance of Empirical Risk Minimization with Smoothed Data","created":"2024-02-22","cited_by_count":13,"area":null},{"id":"2306.13119","title":"Adversarial Resilience in Sequential Prediction via Abstention","created":"2023-06-22","cited_by_count":11,"area":null},{"id":"2205.13056","title":"Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions","created":"2022-05-25","cited_by_count":10,"area":null},{"id":"2206.09642","title":"Beyond IID: data-driven decision-making in heterogeneous environments","created":"2022-06-20","cited_by_count":9,"area":null},{"id":"2301.11187","title":"Smoothed Online Learning for Prediction in Piecewise Affine Systems","created":"2023-01-26","cited_by_count":9,"area":null},{"id":"2210.09385","title":"Adaptive Oracle-Efficient Online Learning","created":"2022-10-17","cited_by_count":7,"area":null},{"id":"2303.04845","title":"Smoothed Analysis of Sequential Probability Assignment","created":"2023-03-08","cited_by_count":7,"area":null},{"id":"2302.05430","title":"Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making","created":"2023-02-10","cited_by_count":5,"area":null},{"id":"2302.14248","title":"Time-uniform confidence bands for the CDF under nonstationarity","created":"2023-02-28","cited_by_count":2,"area":null},{"id":"2504.10598","title":"Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks","created":"2025-04-14","cited_by_count":2,"area":null},{"id":"2506.00135","title":"Tradeoffs between Mistakes and ERM Oracle Calls in Online and Transductive Online Learning","created":"2025-05-30","cited_by_count":2,"area":null},{"id":"2605.07155","title":"Regret-Oracle Complexity Tradeoffs in Agnostic Online Learning","created":"2026-05-08","cited_by_count":1,"area":null},{"id":"2606.13576","title":"Learning with Simulators: No Regret in a Computationally Bounded World","created":"2026-06-11","cited_by_count":1,"area":null},{"id":"2610.17818","title":"Follow the Perturbed Leader for Linear Bandits: $\\sqrt{T}$ Regret over General Convex Sets","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}