Jie Hu
I am an Assistant Professor in the Department of Computer Science and Engineering at Oakland University, based in Rochester, MI. My research focuses on machine learning theory and graph sampling. In particular, I’m interested in designing efficient algorithms for distributed optimization and graph mining, with the goal of pushing the boundaries of how quickly and effectively AI and ML tasks can be accomplished.
Prospective Students: I am recruiting PhD students to start in Winter 2026 with expertise in applied probability, optimization, machine learning algorithms, or Markov chain Monte Carlo.
News
- Aug 2025: Glad to start working at OU as an Assistant Professor.
- Jul 2025: Attended ICML 2025 and made an oral presentation.
Selected Publications
2025
- Jie Hu, Yi-Ting Ma, Do Young Eun
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs (Oral presentation) [PDF]
International Conference on Machine Learning, 2025.Abstract
We propose a history-driven target (HDT) framework in Markov Chain Monte Carlo (MCMC) to improve any random walk algorithm on discrete state spaces, such as general undirected graphs, for efficient sampling from target distribution $\boldsymbol{\mu}$. With broad applications in network science and distributed optimization, recent innovations like the self-repellent random walk (SRRW) achieve near-zero variance by prioritizing under-sampled states through transition kernel modifications based on past visit frequencies. However, SRRW’s reliance on explicit computation of transition probabilities for all neighbors at each step introduces substantial computational overhead, while its strict dependence on time-reversible Markov chains excludes advanced non-reversible MCMC methods. To overcome these limitations, instead of direct modification of transition kernel, HDT introduces a history-dependent target distribution $\boldsymbol{\pi}[\mathbf{x}]$ to replace the original target $\boldsymbol{\mu}$ in any graph sampler, where $\mathbf{x}$ represents the empirical measure of past visits. This design preserves lightweight implementation by requiring only local information between the current and proposed states and achieves compatibility with both reversible and non-reversible MCMC samplers, while retaining unbiased samples with target distribution $\boldsymbol{\mu}$ and near-zero variance performance. Extensive experiments in graph sampling demonstrate consistent performance gains, and a memory-efficient Least Recently Used (LRU) cache ensures scalability to large general graphs.
2024
- Jie Hu, Vishwaraj Doshi, Do Young Eun
Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks (Oral presentation) [PDF]
International Conference on Learning Representations, 2024.Abstract
We study a family of distributed stochastic optimization algorithms where gradients are sampled by a token traversing a network of agents in random-walk fashion. Typically, these random-walks are chosen to be Markov chains that asymptotically sample from a desired target distribution, and play a critical role in the convergence of the optimization iterates. In this paper, we take a novel approach by replacing the standard linear Markovian token by one which follows a nonlinear Markov chain - namely the Self-Repellent Radom Walk (SRRW). Defined for any given ‘base’ Markov chain, the SRRW, parameterized by a positive scalar {\alpha}, is less likely to transition to states that were highly visited in the past, thus the name. In the context of MCMC sampling on a graph, a recent breakthrough in Doshi et al. (2023) shows that the SRRW achieves O(1/{\alpha}) decrease in the asymptotic variance for sampling. We propose the use of a ‘generalized’ version of the SRRW to drive token algorithms for distributed stochastic optimization in the form of stochastic approximation, termed SA-SRRW. We prove that the optimization iterate errors of the resulting SA-SRRW converge to zero almost surely and prove a central limit theorem, deriving the explicit form of the resulting asymptotic covariance matrix corresponding to iterate errors. This asymptotic covariance is always smaller than that of an algorithm driven by the base Markov chain and decreases at rate O(1/{\alpha}^2) - the performance benefit of using SRRW thereby amplified in the stochastic optimization context. Empirical results support our theoretical findings.
2023
- Vishwaraj Doshi, Jie Hu, Do Young Eun
Self-repellent random walks on general graphs-achieving minimal sampling variance via nonlinear Markov chains (Outstanding Paper Award) [PDF]
International Conference on Machine Learning, 2023.Abstract
We consider random walks on discrete state spaces, such as general undirected graphs, where the random walkers are designed to approximate a target quantity over the network topology via sampling and neighborhood exploration in the form of Markov chain Monte Carlo (MCMC) procedures. Given any Markov chain corresponding to a target probability distribution, we design a self-repellent random walk (SRRW) which is less likely to transition to nodes that were highly visited in the past, and more likely to transition to seldom visited nodes. For a class of SRRWs parameterized by a positive real α, we prove that the empirical distribution of the process converges almost surely to the the target (stationary) distribution of the underlying Markov chain kernel. We then provide a central limit theorem and derive the exact form of the arising asymptotic co-variance matrix, which allows us to show that the SRRW with a stronger repellence (larger α) always achieves a smaller asymptotic covariance, in the sense of Loewner ordering of co-variance matrices. Especially for SRRW-driven MCMC algorithms, we show that the decrease in the asymptotic sampling variance is of the order O(1/α), eventually going down to zero. Finally, we provide numerical simulations complimentary to our theoretical results, also empirically testing a version of SRRW with α increasing in time to combine the benefits of smaller asymptotic variance due to large α, with empirically observed faster mixing properties of SRRW with smaller α.
Teaching
- CSI 2470 Introduction to Computer Networks (Fall 2025)
- CSI 2560 Computational Linear Algebra (Winter 2026)