71 citations · 210 across the 21 of their papers we have counts for
22 papers
Regret Bounds for Markov Decision Processes with Recursive Optimized Certainty Equivalents
Wenhao Xu, Xuefeng Gao, Xuedong He
The optimized certainty equivalent (OCE) is a family of risk measures that cover important examples such as entropic risk, conditional value-at-risk and mean-variance models. In th…
Square-root regret bounds for continuous-time episodic Markov decision processes
Xuefeng Gao, Xun Yu Zhou
We study reinforcement learning for continuous-time Markov decision processes (MDPs) in the finite-horizon episodic setting. In contrast to discrete-time MDPs, the inter-transition…
Logarithmic regret bounds for continuous-time average-reward Markov decision processes
Xuefeng Gao, Xun Yu Zhou
We consider reinforcement learning for continuous-time Markov decision processes (MDPs) in the infinite-horizon, average-reward setting. In contrast to discrete-time MDPs, a contin…
Debiasing Samples from Online Learning Using Bootstrap
Ningyuan Chen, Xuefeng Gao, Yi Xiong
It has been recently shown in the literature that the sample averages from online learning experiments are biased when used to estimate the mean reward. To correct the bias, off-po…
Sublinear Regret for Learning POMDPs
Yi Xiong, Ningyuan Chen, Xuefeng Gao +1
We study the model-based undiscounted reinforcement learning for partially observable Markov decision processes (POMDPs). The oracle we consider is the optimal policy of the POMDP…
State-Dependent Temperature Control for Langevin Diffusions
Xuefeng Gao, Zuo Quan Xu, Xun Yu Zhou
We study the temperature control problem for Langevin diffusions in the context of non-convex optimization. The classical optimal control of such a problem is of the bang-bang type…
Decentralized Stochastic Gradient Langevin Dynamics and Hamiltonian Monte Carlo
Mert Gürbüzbalaban, Xuefeng Gao, Yuanhan Hu +1
Stochastic gradient Langevin dynamics (SGLD) and stochastic gradient Hamiltonian Monte Carlo (SGHMC) are two popular Markov Chain Monte Carlo (MCMC) algorithms for Bayesian inferen…
Non-Convex Optimization via Non-Reversible Stochastic Gradient Langevin Dynamics
Yuanhan Hu, Xiaoyu Wang, Xuefeng Gao +2
Stochastic Gradient Langevin Dynamics (SGLD) is a powerful algorithm for optimizing a non-convex objective, where a controlled and properly scaled Gaussian noise is added to the st…
Regime Switching Bandits
Xiang Zhou, Yi Xiong, Ningyuan Chen +1
We study a multi-armed bandit problem where the rewards exhibit regime switching. Specifically, the distributions of the random rewards generated from all arms are modulated by a c…
Affine Point Processes: Refinements to Large-Time Asymptotics
Xuefeng Gao, Lingjiong Zhu
Affine point processes are a class of simple point processes with self- and mutually-exciting properties, and they have found useful applications in several areas. In this paper, w…
Breaking Reversibility Accelerates Langevin Dynamics for Global Non-Convex Optimization
Xuefeng Gao, Mert Gurbuzbalaban, Lingjiong Zhu
Langevin dynamics (LD) has been proven to be a powerful technique for optimizing a non-convex objective as an efficient algorithm to find local minima while eventually visiting a g…
Global Convergence of Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Stochastic Optimization: Non-Asymptotic Performance Bounds and Momentum-Based Acceleration
Xuefeng Gao, Mert Gürbüzbalaban, Lingjiong Zhu
Stochastic gradient Hamiltonian Monte Carlo (SGHMC) is a variant of stochastic gradient with momentum where a controlled and properly scaled Gaussian noise is added to the stochast…
Optimal Market Making in the Presence of Latency
Xuefeng Gao, Yunhan Wang
This paper studies optimal market making for large-tick assets in the presence of latency. We consider a random walk model for the asset price, and formulate the market maker's opt…
Bounded-Velocity Stochastic Control for Dynamic Resource Allocation
Xuefeng Gao, Yingdong Lu, Mayank Sharma +2
We consider a general class of dynamic resource allocation problems within a stochastic optimal control framework. This class of problems arises in a wide variety of applications,…
Transform Analysis for Hawkes Processes with Applications in Dark Pool Trading
Xuefeng Gao, Xiang Zhou, Lingjiong Zhu
Hawkes processes are a class of simple point processes that are self-exciting and have clustering effect, with wide applications in finance, social networks and many other fields.…
Functional central limit theorems for stationary Hawkes processes and application to infinite-server queues
Xuefeng Gao, Lingjiong Zhu
A univariate Hawkes process is a simple point process that is self-exciting and has clustering effect. The intensity of this point process is given by the sum of a baseline intensi…
Large deviations and applications for Markovian Hawkes processes with a large initial intensity
Xuefeng Gao, Lingjiong Zhu
Hawkes process is a class of simple point processes that is self-exciting and has clustering effect. The intensity of this point process depends on its entire past history. It has…
Limit theorems for Markovian Hawkes processes with a large initial intensity
Xuefeng Gao, Lingjiong Zhu
Hawkes process is a simple point process that is self-exciting and has clustering effect. The intensity of this point process depends on its entire past history. It has wide applic…
Hydrodynamic limit of order book dynamics
Xuefeng Gao, S. J. Deng
In this paper, we establish a fluid limit for a two--sided Markov order book model. Our main result states that in a certain asymptotic regime, a pair of measure-valued processes r…
Validity of heavy-traffic steady-state approximations in many-server queues with abandonment
J. G. Dai, A. B. Dieker, Xuefeng Gao
We consider GI/Ph/n+M parallel-server systems with a renewal arrival process, a phase-type service time distribution, n homogenous servers, and an exponential patience time distrib…
Positive recurrence of piecewise Ornstein-Uhlenbeck processes and common quadratic Lyapunov functions
A. B. Dieker, Xuefeng Gao
We study the positive recurrence of piecewise Ornstein-Uhlenbeck (OU) diffusion processes, which arise from many-server queueing systems with phase-type service requirements. These…
Sensitivity analysis for diffusion processes constrained to an orthant
A. B. Dieker, X. Gao
This paper studies diffusion processes constrained to the positive orthant under infinitesimal changes in the drift. Our first main result states that any constrained function and…