Selected Publications
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2023. Assortment Planning for Recommendations at Checkout under Inventory Constraints. Mathematics of Operations Research.
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2023. Model-Free Non-Stationary Reinforcement Learning: Near-Optimal Regret and Applications in Multi-Agent Reinforcement Learning and Inventory Control. Management Science.
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2023. Non-stationary Experimental Design under Linear Trends. NeurIPS.
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2023. Non-Stationary Reinforcement Learning: The Blessing of (More) Optimism. Management Science.
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2023. Offline Planning and Online Learning under Recovering Rewards. Management Science.
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2023. Online Matching with Bayesian Rewards. Operations Research.
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2023. Stochastic Multi-armed Bandits: Optimal Trade-off among Optimality, Consistency, and Tail Risk. NeurIPS 2023 Spotlight (top 3%).
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2022. Bandits atop Reinforcement Learning: Tackling Online Inventory Models with Cyclic Demands. Management Science.
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2022. Calibrating Sales Forecast in a Pandemic Using Competitive Online Non-Parametric Regression. Management Science.
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2022. Constructing Demand Curves from a Single Observation of Bundle Sales. WINE 2022.
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2022. Context-Based Dynamic Pricing with Partially Linear Demand Model. NeurIPS 2022.
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2022. Design and Analysis of Switchback Experiments. Management Science.
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2022. Estimating and Exploiting the Impact of Photo Layout: A Structural Approach. Management Science.
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2022. Learning Mixed Multinomial Logits with Provable Guarantees. NeurIPS 2022.
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2022. Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation. COLT 2022.
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2022. Phase Transitions in Bandits with Switching Constraints. Management Science.
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2022. A Simple and Optimal Policy Design with Safety against Heavy-tailed Risk for Multi-armed Bandits. NeurIPS 2022.
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2022. Sobolev Norm Learning Rates for Conditional Mean Embeddings. AISTAT 2022.
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2021. Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability. Mathematics of Operations Research.
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2021. Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models. Management Science.
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2021. Distributionally Robust Linear and Discrete Optimization with Marginals. Operations Research.
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2021. Dynamic Planning and Learning under Recovering Rewards. ICML 2021.
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2021. Dynamic Pricing and Inventory Control with Fixed Ordering Cost and Incomplete Demand Information. Management Science.
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2021. Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective. COLT 2021.
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2021. Inventory Balancing with Online Learning. Management Science.
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2021. Meta Dynamic Pricing: Transfer Learning Across Experiments. Management Science—Special Issue on Data-Driven Prescriptive Analytics.
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2021. Is Model-Free Learning Nearly Optimal for Non-Stationary RL? ICML 2021.
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2021. Offline Pricing and Demand Learning with Censored Data. Management Science.
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2021. On-line Assortment Optimization with Reusable Resources. Management Science.
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2021. Online Learning and Optimization for Revenue Management Problems with Add-on Discounts. Management Science.
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2021. Online Pricing with Offline Data: Phase Transition and Inverse Square Law. Management Science.
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2021. Privacy-Preserving Dynamic Personalized Pricing with Demand Learning. Management Science.
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2021. Reaping the Benefits of Bundling under High Production Costs. To appear in Proceedings of the 24nd International Conference on Artificial Intelligence and Statistics, AISTATS..
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2020. Dynamic Inventory Allocation with Demand Learning for Seasonal Goods. Production and Operations Management, Special Issue to Honor Professor Hau Lee, Editor Christopher S. Tang.
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2020. Dynamic Pricing (and Assortment) under a Static Calendar. Management Science.
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2020. Hedging the Drift: Learning to Optimize under Non-Stationarity. Management Science—Special Issue on Data-Driven Prescriptive Analytics.
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2020. Multi-Modal Dynamic Pricing. Management Science.
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2020. Online Pricing with Offline Data: Phase Transition and Inverse Square Law. ICML 2020.
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2020. Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism. ICML 2020.
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2020. Shrinking the Upper Confidence Bound: A Dynamic Product Selection Problem for Urban Warehouses. Management Science.
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2020. A Statistical Learning Approach to Personalization in Revenue Management. Management Science.
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2019. Algorithms for Online Matching, Assortment, and Pricing with Tight Weight-Dependent Competitive Ratios. Operations Research.
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2019. Dynamic Learning and Pricing with Model Misspecification. Management Science.
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2019. Learning to Optimize under Non-Stationarity. AISTATS 2019.
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2019. Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints. NeurIPS 2019.
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2019. Sampling-based Approximation Schemes for Capacitated Stochastic Inventory Control Models. Mathematics of Operations Research.
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2018. The Lingering of Gradients: How to Reuse Gradients Over Time. NeurIPS 2018. (31)
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2018. Online Network Revenue Management using Thompson Sampling. Operations Research. 66(6)
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2017. Dynamic Pricing and Demand Learning with Limited Price Experimentation. Operations Research. 65(6)
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2017. Uplift Modeling with Multiple Treatments and General Response Types. SIAM Data Mining 2017.
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2016. Analytics for an Online Retailer: Demand Forecasting and Price Optimization. Manufacturing and Service Operations Management. 18(1)
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2013. Robust Stochastic Lot-Sizing by Means of Histograms. Production and Operations Management. 22