5 papers
Beyond Global Divergences: A Local-Mass Perspective on Bayesian Inference
Hanli Xu, Fengxiang He, Sarat Moka
Global objectives, such as KL divergence and ELBO, are widely used in Bayesian inference for measuring distributional discrepancy. This paper studies their local-mass behaviour tha…
Efficient Rare-Event Simulation for Random Geometric Graphs via Importance Sampling
Sarat Moka, Christian Hirsch, Volker Schmidt +1
Random geometric graphs defined on Euclidean subspaces, also called Gilbert graphs, are widely used to model spatially embedded networks across various domains. In such graphs, nod…
Uniform Sampling of Proper Graph Colorings via Soft Coloring and Partial Rejection Sampling
Sarat Moka, Ava Vahedi
We present a new algorithm for the exact uniform sampling of proper \(k\)-colorings of a graph on \(n\) vertices with maximum degree~\(Î\). The algorithm is based on partial rejec…
Parsimonious Subset Selection for Generalized Linear Models with Biomedical Applications
Anant Mathur, Benoit Liquet, Samuel Muller +1
High-dimensional biomedical studies require models that are simultaneously accurate, sparse, and interpretable, yet exact best subset selection for generalized linear models is com…
A Scalable Gradient-Based Optimization Framework for Sparse Minimum-Variance Portfolio Selection
Sarat Moka, Matias Quiroz, Vali Asimit +1
Portfolio optimization involves selecting asset weights to minimize a risk-reward objective, such as the portfolio variance in the classical minimum-variance framework. Sparse port…