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04/10/2023
Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Risk
We study the trade-off between expectation and tail risk for regret dist...
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06/07/2022
A Simple and Optimal Policy Design with Safety against Heavy-tailed Risk for Multi-armed Bandits
We design new policies that ensure both worst-case optimality for expect...
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04/16/2022
PAC-Bayesian Based Adaptation for Regularized Learning
In this paper, we propose a PAC-Bayesian a posteriori parameter selectio...
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11/21/2021
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
We consider the offline reinforcement learning problem, where the aim is...
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11/01/2021
Dynamic Pricing and Demand Learning on a Large Network of Products: A PAC-Bayesian Approach
We consider a seller offering a large network of N products over a time ...
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06/28/2021
Dynamic Planning and Learning under Recovering Rewards
Motivated by emerging applications such as live-streaming e-commerce, pr...
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05/16/2021
Sobolev Norm Learning Rates for Conditional Mean Embeddings
We develop novel learning rates for conditional mean embeddings by apply...
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10/07/2020
Near-Optimal Regret Bounds for Model-Free RL in Non-Stationary Episodic MDPs
We consider model-free reinforcement learning (RL) in non-stationary Mar...
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10/07/2020
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
In the classical multi-armed bandit problem, instance-dependent algorith...
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09/27/2020
Privacy-Preserving Dynamic Personalized Pricing with Demand Learning
The prevalence of e-commerce has made detailed customers' personal infor...
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08/31/2020
Design and Analysis of Switchback Experiments
In switchback experiments, a firm sequentially exposes an experimental u...
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06/30/2020
Provably More Efficient Q-Learning in the Full-Feedback/One-Sided-Feedback Settings
We propose two new Q-learning algorithms, Full-Q-Learning (FQL) and Elim...
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06/24/2020
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
We consider un-discounted reinforcement learning (RL) in Markov decision...
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05/02/2020
Online Learning and Optimization for Revenue Management Problems with Add-on Discounts
We study in this paper a revenue management problem with add-on discount...
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03/28/2020
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability
We consider the general (stochastic) contextual bandit problem under the...
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11/04/2019
Network Revenue Management with Limited Switches: Known and Unknown Demand Distributions
This work is motivated by a practical concern from our retail partner. W...
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10/19/2019
Online Pricing with Offline Data: Phase Transition and Inverse Square Law
This paper investigates the impact of pre-existing offline data on onlin...
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08/26/2019
Multi-stage and Multi-customer Assortment Optimization with Inventory Constraints
We consider an assortment optimization problem where a customer chooses ...
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07/20/2019
A Competitive Analysis of Online Knapsack Problems with Unit Density
We study an online knapsack problem where the items arrive sequentially ...
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06/07/2019
Reinforcement Learning under Drift
We propose algorithms with state-of-the-art dynamic regret bounds for un...
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05/26/2019
Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints
We consider the classical stochastic multi-armed bandit problem with a c...
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05/12/2019
Algorithms for Online Matching, Assortment, and Pricing with Tight Weight-dependent Competitive Ratios
Motivated by the dynamic assortment offerings and item pricings occurrin...
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03/19/2019
Conservative Exploration for Semi-Bandits with Linear Generalization: A Product Selection Problem for Urban Warehouses
The recent rising popularity of ultra-fast delivery services on retail p...
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03/04/2019
Hedging the Drift: Learning to Optimize under Non-Stationarity
We introduce general data-driven decision-making algorithms that achieve...
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02/28/2019
Meta Dynamic Pricing: Learning Across Experiments
We study the problem of learning across a sequence of price experiments ...
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01/09/2019
The Lingering of Gradients: How to Reuse Gradients over Time
Classically, the time complexity of a first-order method is estimated by...
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11/02/2018
Dynamic Pricing under a Static Calendar
This work is motivated by our collaboration with a large Consumer Packag...
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10/25/2018
On Policies for Single-leg Revenue Management with Limited Demand Information
In this paper we study the single-leg revenue management problem, with n...
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10/11/2018
Inventory Balancing with Online Learning
We study a general problem of allocating limited resources to heterogene...
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10/06/2018
Learning to Optimize under Non-Stationarity
We introduce algorithms that achieve state-of-the-art dynamic regret bou...
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09/12/2017
A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling
Randomized experiments have been critical tools of decision making for d...
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05/23/2017
