Harsh Vardhan Dubey successfully defended his thesis titled, “Decision-making in Graphical Models and Game-Theoretic Statistics”. Thanks to Profs. Shahriyari, Chu, and Zick for serving on his committee. Congratulations Dr. Dubey!
Category Archives: Research
Paper: Discovering genetic modulators of the protein homeostasis system through multilevel analysis
Thanks to Vishal Sarsani, Tingting Zhao, Shai He, Berent Aldikacti and Peter Chien for a wonderful collaboration on this project.
In this work, we tested how the fitness landscape changes in response to environmental and genetic perturbations using directed and massively parallel transposon mutagenesis in Caulobacter crescentus. We developed a general computational pipeline for the analysis of gene-by-environment interactions in transposon mutagenesis experiments. Experiments exposing cells to a combination of genetic perturbations and dual environmental stressors show that perturbations that are quantitatively dissimilar from the perspective of the fitness landscape are likely to have a synergistic effect on the growth defect.
The paper is open-access and you can read more here.
arXiv: Doubly Non-Central Beta Matrix Factorization for Stable Dimensionality Reduction of Bounded Support Matrix Data
A new paper by Anjali Albert (Nagulpally) and Aaron Schein.
Abstract: We consider the problem of developing interpretable and computationally efficient matrix decomposition methods for matrices whose entries have bounded support. Such matrices are found in large-scale DNA methylation studies and many other settings. Our approach decomposes the data matrix into a Tucker representation wherein the number of columns in the constituent factor matrices is not constrained. We derive a computationally efficient sampling algorithm to solve for the Tucker decomposition. We evaluate the performance of our method using three criteria: predictability, computability, and stability. Empirical results show that our method has similar performance as other state-of-the-art approaches in terms of held-out prediction and computational complexity, but has significantly better performance in terms of stability to changes in hyper-parameters. The improved stability results in higher confidence in the results in applications where the constituent factors are used to generate and test scientific hypotheses such as DNA methylation analysis of cancer samples.
Head over to arXiv to read more: https://arxiv.org/abs/2410.18425
arXiv: Maximum a Posteriori Inference for Factor Graphs via Benders’ Decomposition
A new paper by Harsh Dubey and Ji Ah Lee is now on arXiv.
Abstract: Many Bayesian statistical inference problems come down to computing a maximum a-posteriori (MAP) assignment of latent variables. Yet, standard methods for estimating the MAP assignment do not have a finite time guarantee that the algorithm has converged to a fixed point. Previous research has found that MAP inference can be represented in dual form as a linear programming problem with a non-polynomial number of constraints. A Lagrangian relaxation of the dual yields a statistical inference algorithm as a linear programming problem. However, the decision as to which constraints to remove in the relaxation is often heuristic. We present a method for maximum a-posteriori inference in general Bayesian factor models that sequentially adds constraints to the fully relaxed dual problem using Benders’ decomposition. Our method enables the incorporation of expressive integer and logical constraints in clustering problems such as must-link, cannot-link, and a minimum number of whole samples allocated to each cluster. Using this approach, we derive MAP estimation algorithms for the Bayesian Gaussian mixture model and latent Dirichlet allocation. Empirical results show that our method produces a higher optimal posterior value compared to Gibbs sampling and variational Bayes methods for standard data sets and provides certificate of convergence.
Head over to https://arxiv.org/abs/2410.19131 to read more.
Congratulations to Dr. Ji Ah Lee on her PhD Thesis Defense
Ji Ah Lee successfully defended her thesis titled, “Benders’ Decomposition for Solving the Lagrangian Relaxation of Probabilistic Graphical Models”. Thanks to Profs. Katsoulakis, Liu, and Sheldon for serving on his committee. Congratulations Dr. Lee!