{"total":33,"rows":[{"id":"2102.07698","title":"How to Learn when Data Reacts to Your Model: Performative Gradient Descent","created":"2021-02-15","cited_by_count":71,"area":null},{"id":"2206.13063","title":"On the Complexity of Adversarial Decision Making","created":"2022-06-27","cited_by_count":33,"area":null},{"id":"2301.08215","title":"Tight Guarantees for Interactive Decision Making with the Decision-Estimation Coefficient","created":"2023-01-19","cited_by_count":30,"area":null},{"id":"2207.05849","title":"Contextual Bandits with Smooth Regret: Efficient Learning in Continuous Action Spaces","created":"2022-07-12","cited_by_count":18,"area":null},{"id":"2310.00806","title":"Bayesian Design Principles for Frequentist Sequential Learning","created":"2023-10-01","cited_by_count":18,"area":null},{"id":"2107.04518","title":"Optimal Gradient-based Algorithms for Non-concave Bandit Optimization","created":"2021-07-09","cited_by_count":15,"area":null},{"id":"2103.04550","title":"No Weighted-Regret Learning in Adversarial Bandits with Delays","created":"2021-03-08","cited_by_count":12,"area":null},{"id":"2406.06506","title":"Online Newton Method for Bandit Convex Optimisation","created":"2024-06-10","cited_by_count":12,"area":null},{"id":"2410.05117","title":"Assouad, Fano, and Le Cam with Interaction: A Unifying Lower Bound Framework and Characterization for Bandit Learnability","created":"2024-10-07","cited_by_count":12,"area":null},{"id":"2209.12897","title":"Quantum Speedups of Optimizing Approximately Convex Functions with Applications to Logarithmic Regret Stochastic Convex Bandits","created":"2022-09-26","cited_by_count":11,"area":null},{"id":"2210.05584","title":"On Adaptivity in Non-stationary Stochastic Optimization With Bandit Feedback","created":"2022-10-11","cited_by_count":11,"area":null},{"id":"2302.04452","title":"An Information-Theoretic Analysis of Nonstationary Bandit Learning","created":"2023-02-09","cited_by_count":11,"area":null},{"id":"2302.05371","title":"A Second-Order Method for Stochastic Bandit Convex Optimisation","created":"2023-02-10","cited_by_count":10,"area":null},{"id":"2402.09152","title":"Improved Regret for Bandit Convex Optimization with Delayed Feedback","created":"2024-02-14","cited_by_count":8,"area":null},{"id":"2302.06025","title":"Statistical Complexity and Optimal Algorithms for Non-linear Ridge Bandits","created":"2023-02-12","cited_by_count":7,"area":null},{"id":"2102.09488","title":"A Bit Better? Quantifying Information for Bandit Learning","created":"2021-02-18","cited_by_count":5,"area":null},{"id":"2106.00444","title":"Minimax Regret for Bandit Convex Optimisation of Ridge Functions","created":"2021-06-01","cited_by_count":3,"area":null},{"id":"2602.02634","title":"A Reduction from Delayed to Immediate Feedback for Online Convex Optimization with Improved Guarantees","created":"2026-02-02","cited_by_count":3,"area":null},{"id":"2202.06150","title":"Adaptive Bandit Convex Optimization with Heterogeneous Curvature","created":"2022-02-12","cited_by_count":2,"area":null},{"id":"2208.06720","title":"A Near-Optimal Algorithm for Univariate Zeroth-Order Budget Convex Optimization","created":"2022-08-13","cited_by_count":2,"area":null},{"id":"2302.08551","title":"Infinite Action Contextual Bandits with Reusable Data Exhaust","created":"2023-02-16","cited_by_count":2,"area":null},{"id":"2505.20734","title":"Adversarial bandit optimization for approximately linear functions","created":"2025-05-27","cited_by_count":2,"area":null},{"id":"2406.18672","title":"A simple and improved algorithm for noisy, convex, zeroth-order optimisation","created":"2024-06-26","cited_by_count":1,"area":null},{"id":"2506.02980","title":"Non-stationary Bandit Convex Optimization: A Comprehensive Study","created":"2025-06-03","cited_by_count":1,"area":null},{"id":"2603.26066","title":"Adversarial Bandit Optimization with Globally Bounded Perturbations to Linear Losses","created":"2026-03-27","cited_by_count":1,"area":null},{"id":"2605.22191","title":"Bandit Convex Optimization with Gradient Prediction Adaptivity","created":"2026-05-21","cited_by_count":1,"area":null},{"id":"2606.11711","title":"Capacity-Constrained Online Convex Optimization with Delayed Feedback","created":"2026-06-10","cited_by_count":1,"area":null},{"id":"2607.18652","title":"The Price of Hidden Curvature: Improved Lower Bounds for Bandit Convex Optimization","created":"2026-07-21","cited_by_count":1,"area":null},{"id":"2302.00358","title":"Bandit Convex Optimisation Revisited: FTRL Achieves $\\tilde{O}(t^{1/2})$ Regret","created":"2023-02-01","cited_by_count":0,"area":null},{"id":"2407.06325","title":"CONGO: Compressive Online Gradient Optimization","created":"2024-07-08","cited_by_count":0,"area":null},{"id":"2501.11127","title":"A Regularized Online Newton Method for Stochastic Convex Bandits with Linear Vanishing Noise","created":"2025-01-19","cited_by_count":0,"area":null},{"id":"2608.16216","title":"Beyond Peak Backlog: Conditional Energy and Temporal Geometry in Capacity-Constrained Delayed Bandit Optimization","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2609.10981","title":"Thompson Sampling for Non-Monotone Convex Ridge Bandits: Monotonicity Is Not Needed for Polynomial Regret","created":"2026-09-10","cited_by_count":0,"area":null}],"next_cursor":null}