4 papers
Beyond Worst-Case Coreset Bounds for -Clustering via Determinantal Sampling
Diptarka Chakraborty, Satyaki Mukherjee, Gaurav Vallabhdas Revankar +1
Massive datasets in modern machine learning have made data reduction a central challenge, particularly for clustering tasks where memory and computational constraints demand compac…
Fast determinantal sampling on general spaces and diffusion geometry
Hoang-Son Tran, Pranav Gupta, Subhroshekhar Ghosh
Determinantal point processes have recently emerged as a kernel-based alternative to standard independent sampling for constructing efficient minibatches, coresets, and other compa…
Small coresets via negative dependence: DPPs, linear statistics, and concentration
Rémi Bardenet, Subhroshekhar Ghosh, Hugo Simon-Onfroy +1
Determinantal point processes (DPPs) are random configurations of points with tunable negative dependence. Because sampling is tractable, DPPs are natural candidates for subsamplin…
Learning Networks from Gaussian Graphical Models and Gaussian Free Fields
Subhro Ghosh, Soumendu Sundar Mukherjee, Hoang-Son Tran +1
We investigate the problem of estimating the structure of a weighted network from repeated measurements of a Gaussian Graphical Model (GGM) on the network. In this vein, we conside…