7 papers
Gaussian mixtures and non-parametric likelihoods through the lens of statistical mechanics
Subhroshekhar Ghosh, Adityanand Guntuboyina, Satyaki Mukherjee +1
In this work, we investigate Gaussian Mixture Models ({\it abbrv} GMM) and the related problem of non parametric maximum likelihood estimation ({\it abbrv} NPMLE) from the perspect…
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…
State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
Hoang-Son Tran, Pranav Gupta, Rémi Bardenet +1
Determinantal point processes (DPPs) have emerged as a kernelized alternative to vanilla independent sampling for generating efficient minibatches, coresets and other parsimonious…
On the Statistical Optimality of Optimal Decision Trees
Zineng Xu, Subhro Ghosh, Yan Shuo Tan
While globally optimal empirical risk minimization (ERM) decision trees have become computationally feasible and empirically successful, rigorous theoretical guarantees for their s…
Negative Dependence as a toolbox for machine learning : review and new developments
Hoang-Son Tran, Vladimir Petrovic, Remi Bardenet +1
Negative dependence is becoming a key driver in advancing learning capabilities beyond the limits of traditional independence. Recent developments have evidenced support towards ne…
Filtering through a topological lens: homology for point processes on the time-frequency plane
Juan Manuel Miramont, Kin Aun Tan, Soumendu Sundar Mukherjee +2
We introduce a very general approach to the analysis of signals from their noisy measurements from the perspective of Topological Data Analysis (TDA). While TDA has emerged as a po…