50 citations · 302 across the 44 of their papers we have counts for
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Likelihood Adjusted Semidefinite Programs for Clustering Heterogeneous Data
Yubo Zhuang, Xiaohui Chen, Yun Yang
Clustering is a widely deployed unsupervised learning tool. Model-based clustering is a flexible framework to tackle data heterogeneity when the clusters have different shapes. Lik…
Mean-field Variational Inference via Wasserstein Gradient Flow
Rentian Yao, Yun Yang
Variational inference, such as the mean-field (MF) approximation, requires certain conjugacy structures for efficient computation. These can impose unnecessary restrictions on the…
Mean-Field Nonparametric Estimation of Interacting Particle Systems
Rentian Yao, Xiaohui Chen, Yun Yang
This paper concerns the nonparametric estimation problem of the distribution-state dependent drift vector field in an interacting -particle system. Observing single-trajectory d…
On the Computational Complexity of Metropolis-Adjusted Langevin Algorithms for Bayesian Posterior Sampling
Rong Tang, Yun Yang
In this paper, we examine the computational complexity of sampling from a Bayesian posterior (or pseudo-posterior) using the Metropolis-adjusted Langevin algorithm (MALA). MALA fir…
Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks
Yifan Chen, Tianning Xu, Dilek Hakkani-Tur +3
Multiple sampling-based methods have been developed for approximating and accelerating node embedding aggregation in graph convolutional networks (GCNs) training. Among them, a lay…
Sketch-and-Lift: Scalable Subsampled Semidefinite Program for -means Clustering
Yubo Zhuang, Xiaohui Chen, Yun Yang
Semidefinite programming (SDP) is a powerful tool for tackling a wide range of computationally hard problems such as clustering. Despite the high accuracy, semidefinite programs ar…