97 citations · 234 across the 26 of their papers we have counts for
28 papers
Detecting Jumps on a Tree: a Hierarchical Normalized Stable Process Model for Evolution of Discrete Distributions
Hanxi Sun, Heejung Shim, Vinayak Rao
This work focuses on clustering populations whose hierarchical dependency structure can be described by a tree. A particular example that is the focus of this work is a phylogeneti…
Bayesian Repulsive Mixture Modeling with Matérn Point Processes
Hanxi Sun, Boqian Zhang, Minhyeok Kim +1
Mixture models are a standard tool in statistical analyses, widely used for density modeling and model-based clustering. In this work, we propose a Bayesian mixture model with repu…
Marginally Constrained Nonparametric Bayesian Inference through Gaussian Processes
Bingjing Tang, Vinayak Rao
Nonparametric Bayesian models are used routinely as flexible and powerful models of complex data. Many times, a statistician may have additional informative beliefs about data dist…
Data Augmentation MCMC for Bayesian Inference from Privatized Data
Nianqiao Ju, Jordan A. Awan, Ruobin Gong +1
Differentially private mechanisms protect privacy by introducing additional randomness into the data. Restricting access to only the privatized data makes it challenging to perform…
Set Twister for Single-hop Node Classification
Yangze Zhou, Vinayak Rao, Bruno Ribeiro
Node classification is a central task in relational learning, with the current state-of-the-art hinging on two key principles: (i) predictions are permutation-invariant to the orde…
Contextual Unsupervised Outlier Detection in Sequences
Mohamed A. Zahran, Leonardo Teixeira, Vinayak Rao +1
This work proposes an unsupervised learning framework for trajectory (sequence) outlier detection that combines ranking tests with user sequence models. The overall framework ident…
Privacy-Aware Rejection Sampling
Jordan Awan, Vinayak Rao
Differential privacy (DP) offers strong theoretical privacy guarantees, but implementations of DP mechanisms may be vulnerable to side-channel attacks, such as timing attacks. When…
Bayesian Joint Chance Constrained Optimization: Approximations and Statistical Consistency
Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
This paper considers data-driven chance-constrained stochastic optimization problems in a Bayesian framework. Bayesian posteriors afford a principled mechanism to incorporate data…
PAC-Bayes Bounds on Variational Tempered Posteriors for Markov Models
Imon Banerjee, Vinayak A. Rao, Harsha Honnappa
Datasets displaying temporal dependencies abound in science and engineering applications, with Markov models representing a simplified and popular view of the temporal dependence s…
Variational Bayesian Methods for Stochastically Constrained System Design Problems
Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
We study system design problems stated as parameterized stochastic programs with a chance-constraint set. We adopt a Bayesian approach that requires the computation of a posterior…
Asymptotic Consistency of Loss-Calibrated Variational Bayes
Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
This paper establishes the asymptotic consistency of the {\it loss-calibrated variational Bayes} (LCVB) method. LCVB was proposed in~\cite{LaSiGh2011} as a method for approximately…
Community detection over a heterogeneous population of non-aligned networks
Guilherme Gomes, Vinayak Rao, Jennifer Neville
Clustering and community detection with multiple graphs have typically focused on aligned graphs, where there is a mapping between nodes across the graphs (e.g., multi-view, multi-…
An Exact Auxiliary Variable Gibbs Sampler for a Class of Diffusions
Qi Wang, Vinayak Rao, Yee Whye Teh
Stochastic differential equations (SDEs) or diffusions are continuous-valued continuous-time stochastic processes widely used in the applied and mathematical sciences. Simulating p…
On the Statistical Consistency of Risk-Sensitive Bayesian Decision-Making
Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
We study data-driven decision-making problems in the Bayesian framework, where the expectation in the Bayes risk is replaced by a risk-sensitive entropic risk measure. We focus on…
Relational Pooling for Graph Representations
Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao +1
This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and diffusions. Our approach, denoted Relational Po…
Asymptotic Consistency of Rényi-Approximate Posteriors
Prateek Jaiswal, Vinayak A. Rao, Harsha Honnappa
We study the asymptotic consistency properties of -Rényi approximate posteriors, a class of variational Bayesian methods that approximate an intractable Bayesian posterior with…
Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs
Ryan L. Murphy, Balasubramaniam Srinivasan, Vinayak Rao +1
We consider a simple and overarching representation for permutation-invariant functions of sequences (or multiset functions). Our approach, which we call Janossy pooling, expresses…
Multi-level hypothesis testing for populations of heterogeneous networks
Guilherme Gomes, Vinayak Rao, Jennifer Neville
In this work, we consider hypothesis testing and anomaly detection on datasets where each observation is a weighted network. Examples of such data include brain connectivity networ…
Flexible Mixture Modeling on Constrained Spaces
Putu Ayu Sudyanti, Vinayak Rao
This paper addresses challenges in flexibly modeling multimodal data that lie on constrained spaces. Such data are commonly found in spatial applications, such as climatology and c…
Group-Representative Functional Network Estimation from Multi-Subject fMRI Data via MRF-based Image Segmentation
Aditi Iyer, Bingjing Tang, Vinayak Rao +1
We propose a novel two-phase approach to functional network estimation of multi-subject functional Magnetic Resonance Imaging (fMRI) data, which applies model-based image segmentat…
Efficient parameter sampling for Markov jump processes
Boqian Zhang, Vinayak Rao
Markov jump processes (MJPs) are continuous-time stochastic processes widely used in a variety of applied disciplines. Inference for MJPs typically proceeds via Markov chain Monte…
Data augmentation for models based on rejection sampling
Vinayak Rao, Lizhen Lin, David Dunson
We present a data augmentation scheme to perform Markov chain Monte Carlo inference for models where data generation involves a rejection sampling algorithm. Our idea, which seems…
Multiscale Shrinkage and Lévy Processes
Xin Yuan, Vinayak Rao, Shaobo Han +1
A new shrinkage-based construction is developed for a compressible vector , for cases in which the components of $\xv$ are naturally associated with…
Bayesian nonparametric inference on the Stiefel manifold
Lizhen Lin, Vinayak Rao, David B. Dunson
The Stiefel manifold is the space of all orthonormal matrices, with the hypersphere and the space of all orthogonal matrices constituting special cases…