{"total":21,"rows":[{"id":"2106.03365","title":"Generalized Linear Bandits with Local Differential Privacy","created":"2021-06-07","cited_by_count":29,"area":null},{"id":"2201.03968","title":"Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms","created":"2022-01-10","cited_by_count":24,"area":null},{"id":"2109.06368","title":"Policy Optimization Using Semi-parametric Models for Dynamic Pricing","created":"2021-09-13","cited_by_count":23,"area":null},{"id":"2507.11847","title":"Generalized Linear Bandits: Almost Optimal Regret with One-Pass Update","created":"2025-07-16","cited_by_count":16,"area":null},{"id":"2109.04615","title":"Differential Privacy in Personalized Pricing with Nonparametric Demand Models","created":"2021-09-10","cited_by_count":13,"area":null},{"id":"2111.08221","title":"Fairness-aware Online Price Discrimination with Nonparametric Demand Models","created":"2021-11-16","cited_by_count":7,"area":null},{"id":"2302.02526","title":"On Private and Robust Bandits","created":"2023-02-06","cited_by_count":6,"area":null},{"id":"2406.13130","title":"Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data","created":"2024-06-19","cited_by_count":6,"area":null},{"id":"2311.16528","title":"Utility Fairness in Contextual Dynamic Pricing with Demand Learning","created":"2023-11-28","cited_by_count":4,"area":null},{"id":"2304.12681","title":"Differential Privacy via Distributionally Robust Optimization","created":"2023-04-25","cited_by_count":2,"area":null},{"id":"2404.09413","title":"On the Optimal Regret of Locally Private Linear Contextual Bandit","created":"2024-04-15","cited_by_count":2,"area":null},{"id":"2410.14839","title":"Multi-Task Dynamic Pricing in Credit Market with Contextual Information","created":"2024-10-18","cited_by_count":2,"area":null},{"id":"2605.05609","title":"Optimal Contextual Pricing under Agnostic Non-Lipschitz Demand","created":"2026-05-07","cited_by_count":2,"area":null},{"id":"2206.08111","title":"On Private Online Convex Optimization: Optimal Algorithms in $\\ell_p$-Geometry and High Dimensional Contextual Bandits","created":"2022-06-16","cited_by_count":1,"area":null},{"id":"2209.04419","title":"Majority Vote for Distributed Differentially Private Sign Selection","created":"2022-09-08","cited_by_count":1,"area":null},{"id":"2401.08224","title":"Privacy Preserving Adaptive Experiment Design","created":"2024-01-16","cited_by_count":1,"area":null},{"id":"2606.25170","title":"Minimax PAC Bounds for Learning in Exogenous Contextual MDPs","created":"2026-06-23","cited_by_count":1,"area":null},{"id":"2211.01798","title":"Phase Transitions in Learning and Earning under Price Protection Guarantee","created":"2022-11-03","cited_by_count":0,"area":null},{"id":"2402.03737","title":"Differentially Private High Dimensional Bandits","created":"2024-02-06","cited_by_count":0,"area":null},{"id":"2404.15466","title":"Private Optimal Inventory Policy Learning for Feature-based Newsvendor with Unknown Demand","created":"2024-04-23","cited_by_count":0,"area":null},{"id":"2509.05864","title":"Beyond ATE: Multi-Criteria Design for A/B Testing","created":"2025-09-06","cited_by_count":0,"area":null}],"next_cursor":null}