8 papers
Sieve Estimation of Optimal Transport Maps from Paired Data in Gaussian Spaces
Xin Jin, Kit Chan, Riddhi Pratim Ghosh
We study the estimation of infinite-dimensional optimal transport maps from noisy paired observations. The population map pushes a Gaussian reference measure forward to a target pr…
Bayesian Neural Networks vs. Mixture Density Networks: Theoretical and Empirical Insights for Uncertainty-Aware Nonlinear Modeling
Riddhi Pratim Ghosh, Ian Barnett
This paper investigates two prominent probabilistic neural modeling paradigms: Bayesian Neural Networks (BNNs) and Mixture Density Networks (MDNs) for uncertainty-aware nonlinear r…
Characterizing errors in parameter estimation by local measurements
Riddhi Ghosh, Alexei Gilchrist, Daniel Burgarth
The indirect estimation of couplings in quantum dynamics relies on the measurement of the spectrum and the overlap of eigenvectors with some reference states. This data can be obta…
Generalized Tree-Informed Mixed Model Regression
Jeremiah Allis, Xin Jin, Riddhi Ghosh
The standard regression tree method applied to observations within clusters poses both methodological and implementation challenges. Effectively leveraging these data requires meth…
Two-Sample Hypothesis Testing for Large Random Graphs of Unequal Size
Xin Jin, Kit Chan, Ian Barnett +1
Two-sample hypothesis testing for large graphs is popular in cognitive science, probabilistic machine learning and artificial intelligence. While numerous methods have been propose…
A Generalized Estimating Equation Approach to Network Regression
Riddhi Pratim Ghosh, Jukka-Pekka Onnela, Ian Barnett
Regression models applied to network data where node attributes are the dependent variables poses a methodological challenge. As has been well studied, naive regression neither pro…
Selecting a significance level in sequential testing procedures for community detection
Riddhi Pratim Ghosh, Ian Barnett
While there have been numerous sequential algorithms developed to estimate community structure in networks, there is little available guidance and study of what significance level…
Adaptive Bayesian Variable Clustering via Structural Learning of Breast Cancer Data
Riddhi Pratim Ghosh, Arnab Kumar Maity, Mohsen Pourahmadi +1
Clustering of proteins is of interest in cancer cell biology. This article proposes a hierarchical Bayesian model for protein (variable) clustering hinging on correlation structure…