23 citations · 34 across the 10 of their papers we have counts for
10 papers
Online Control via Counterfactual Tracking
Yunzong Xu
We study online control of a known linear dynamical system with adversarial costs and bounded disturbances, measuring regret against a general class of benchmark policies. We intro…
Finite-Time Queue Peak Laws in Stochastic Networks: Logarithmic Scaling After Geometric Thresholds
Hao Liang, Cheng Tang, Yunzong Xu
We study finite-horizon queue peaks in generalized switches, a standard stochastic-network model in which many queues share constrained service resources. Arrivals may be dependent…
Optimal Hidden-Target Learning for Online Inventory Optimization on General Convex Sets
Anthony Pineci, Yunzong Xu
Online inventory optimization (OIO) is online convex optimization with physical memory: inventory carryover makes the feasible action set depend on the past. A natural principle, u…
Greedy Algorithm for Structured Bandits: A Sharp Characterization of Asymptotic Success / Failure
Aleksandrs Slivkins, Yunzong Xu, Shiliang Zuo
We study the greedy (exploitation-only) algorithm in bandit problems with a known reward structure. We allow arbitrary finite reward structures, while prior work focused on a few s…
Offline Reinforcement Learning: Fundamental Barriers for Value Function Approximation
Dylan J. Foster, Akshay Krishnamurthy, David Simchi-Levi +1
We consider the offline reinforcement learning problem, where the aim is to learn a decision making policy from logged data. Offline RL -- particularly when coupled with (value) fu…
Instance-Dependent Complexity of Contextual Bandits and Reinforcement Learning: A Disagreement-Based Perspective
Dylan J. Foster, Alexander Rakhlin, David Simchi-Levi +1
In the classical multi-armed bandit problem, instance-dependent algorithms attain improved performance on "easy" problems with a gap between the best and second-best arm. Are simil…
Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizability
David Simchi-Levi, Yunzong Xu
We consider the general (stochastic) contextual bandit problem under the realizability assumption, i.e., the expected reward, as a function of contexts and actions, belongs to a ge…
Blind Network Revenue Management and Bandits with Knapsacks under Limited Switches
David Simchi-Levi, Yunzong Xu, Jinglong Zhao
This paper studies the impact of limited switches on resource-constrained dynamic pricing with demand learning. We focus on the classical price-based blind network revenue manageme…
Online Pricing with Offline Data: Phase Transition and Inverse Square Law
Jinzhi Bu, David Simchi-Levi, Yunzong Xu
This paper investigates the impact of pre-existing offline data on online learning, in the context of dynamic pricing. We study a single-product dynamic pricing problem over a sell…
Phase Transitions in Bandits with Switching Constraints
David Simchi-Levi, Yunzong Xu
We consider the classical stochastic multi-armed bandit problem with a constraint that limits the total cost incurred by switching between actions to be no larger than a given swit…