214 citations · 262 across the 32 of their papers we have counts for
34 papers
Architecture Alignment With Sparse Priors in Tabular Foundation Models
Tianqi Zhao, Tianyi Zhuang, Shuo Duan +3
Tabular foundation models (TFMs) are increasingly popular because they deliver strong predictions on new datasets through in-context learning, without task-specific training or ext…
The Snake Algorithm: A Rejection-Free Sampler for Binary Matrices with Fixed Margins
Zipei Nie, Guanyang Wang, Peng Zhang
We study uniform sampling of binary matrices with fixed row and column sums, a recurring problem in ecological null models, Rasch-model testing, network analysis, and combinatorics…
Sharp small-deviation inequalities for sums of independent nonnegative random variables
Weibo Fu, Yanjun Han, Guanyang Wang +3
Let be independent nonnegative random variables with , and write . For , we prove that \[ \mathbb{P}\left(S<\mathbb{E} S+δ\…
Spectral Gap for the Binary Fixed-Margin Swap Chain
Weibo Fu, Qian Qin, Guanyang Wang
We prove an explicit spectral-gap lower bound for the lazy swap chain on binary matrices with prescribed row and column sums. This chain is a standard sampler for fixed-margin null…
Couple to Control: Joint Initial Noise Design in Diffusion Models
Jing Jia, Liyue Shen, Guanyang Wang
Diffusion models typically generate image batches from independent Gaussian initial noises. We argue that this independence assumption is only one choice within a broader class of…
Optimal Quantum Speedups for Repeatedly Nested Expectation Estimation
Yihang Sun, Guanyang Wang, Jose Blanchet
We study the estimation of repeatedly nested expectations (RNEs) with a constant horizon (number of nestings) using quantum computing. We propose a quantum algorithm that achieves…
Beyond Accuracy: Evaluating Posterior Fidelity of Diffusion Inverse Solvers
Xiaoyu Qiu, Taewon Yang, Zhanhao Liu +2
Uncertainty evaluation is critical in scientific and engineering inverse problems. However, existing benchmarks on Diffusion Inverse Solvers (DIS) primarily focus on reconstruction…
Weak Diffusion Priors Can Still Achieve Strong Inverse-Problem Performance
Jing Jia, Wei Yuan, Sifan Liu +2
Can a diffusion model trained on bedrooms recover human faces? Diffusion models are widely used as priors for inverse problems, but standard approaches usually assume a high-fideli…
TabClustPFN: A Prior-data Fitted Network for Tabular Data Clustering
Tianqi Zhao, Guanyang Wang, Yan Shuo Tan +1
Prior-data Fitted Networks (PFNs) have reframed supervised tabular learning as single-pass in-context inference without per-dataset optimization. Extending this paradigm to cluster…
Connecting Quantum Computing with Classical Stochastic Simulation
Jose Blanchet, Mark S. Squillante, Mario Szegedy +1
This tutorial paper introduces quantum approaches to Monte Carlo computation with applications in computational finance. We outline the basics of quantum computing using Grover's a…
Antithetic Noise in Diffusion Models
Jing Jia, Sifan Liu, Bowen Song +3
We systematically study antithetic initial noise in diffusion models, discovering that pairing each noise sample with its negation consistently produces strong negative correlation…
CCS: Controllable and Constrained Sampling with Diffusion Models via Initial Noise Perturbation
Bowen Song, Zecheng Zhang, Zhaoxu Luo +6
Diffusion models have emerged as powerful tools for generative tasks, producing high-quality outputs across diverse domains. However, how the generated data responds to the initial…
Quantum speedup of non-linear Monte Carlo problems
Jose Blanchet, Yassine Hamoudi, Mario Szegedy +1
The mean of a random variable can be understood as a linear functional on the space of probability distributions. Quantum computing is known to provide a quadratic speedup over cla…
Markov chain Monte Carlo without evaluating the target: an auxiliary variable approach
Wei Yuan, Guanyang Wang
In sampling tasks, it is common for target distributions to be known up to a normalizing constant. However, in many situations, even evaluating the unnormalized distribution can be…
When are Unbiased Monte Carlo Estimators More Preferable than Biased Ones?
Guanyang Wang, Jose Blanchet, Peter W. Glynn
Due to the potential benefits of parallelization, designing unbiased Monte Carlo estimators, primarily in the setting of randomized multilevel Monte Carlo, has recently become very…
Quadratic Speed-up in Infinite Variance Quantum Monte Carlo
Jose Blanchet, Mario Szegedy, Guanyang Wang
In this study, we give an extension of Montanaro's arXiv/archive:1504.06987 quantum Monte Carlo method, tailored for computing expected values of random variables that exhibit infi…
Spectral gap bounds for reversible hybrid Gibbs chains
Qian Qin, Nianqiao Ju, Guanyang Wang
Hybrid Gibbs samplers represent a prominent class of approximated Gibbs algorithms that utilize Markov chains to approximate conditional distributions, with the Metropolis-within-G…
Pattern detection in bipartite networks: a review of terminology, applications and methods
Zachary Neal, Annabel Cadieux, Diego Garlaschelli +7
Two dimensional matrices with binary (0/1) entries are a common data structure in many research fields. Examples include ecology, economics, mathematics, physics, psychometrics and…
Optimal randomized multilevel Monte Carlo for repeatedly nested expectations
Yasa Syed, Guanyang Wang
The estimation of repeatedly nested expectations is a challenging task that arises in many real-world systems. However, existing methods generally suffer from high computational co…
Spectral Telescope: Convergence Rate Bounds for Random-Scan Gibbs Samplers Based on a Hierarchical Structure
Qian Qin, Guanyang Wang
Random-scan Gibbs samplers possess a natural hierarchical structure. The structure connects Gibbs samplers targeting higher dimensional distributions to those targeting lower dimen…
Repeated Averages on Graphs
Ramis Movassagh, Mario Szegedy, Guanyang Wang
Sourav Chatterjee, Persi Diaconis, Allan Sly and Lingfu Zhang, prompted by a question of Ramis Movassagh, renewed the study of a process proposed in the early 1980s by Jean Bourgai…
Unbiased Multilevel Monte Carlo methods for intractable distributions: MLMC meets MCMC
Guanyang Wang, Tianze Wang
Constructing unbiased estimators from Markov chain Monte Carlo (MCMC) outputs is a difficult problem that has recently received a lot of attention in the statistics and machine lea…
Unbiased Optimal Stopping via the MUSE
Zhengqing Zhou, Guanyang Wang, Jose Blanchet +1
We propose a new unbiased estimator for estimating the utility of the optimal stopping problem. The MUSE, short for Multilevel Unbiased Stopping Estimator, constructs the unbiased…
Discussion of `A Gibbs sampler for a class of random convex polytopes'
Persi Diaconis, Guanyang Wang
This is a contribution for the discussion on "A Gibbs sampler for a class of random convex polytopes" by Pierre E. Jacob, Ruobin Gong, Paul T. Edlefsen and Arthur P. Dempster to ap…