56 citations · 228 across the 31 of their papers we have counts for
6 papers · 1 filter
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…
Generative Principal Component Analysis
Zhaoqiang Liu, Jiulong Liu, Subhroshekhar Ghosh +2
In this paper, we study the problem of principal component analysis with generative modeling assumptions, adopting a general model for the observed matrix that encompasses notable…
Gaussian Determinantal Processes: a new model for directionality in data
Subhro Ghosh, Philippe Rigollet
Determinantal point processes (a.k.a. DPPs) have recently become popular tools for modeling the phenomenon of negative dependence, or repulsion, in data. However, our understanding…
Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors
Zhaoqiang Liu, Subhroshekhar Ghosh, Jonathan Scarlett
Compressive phase retrieval is a popular variant of the standard compressive sensing problem in which the measurements only contain magnitude information. In this paper, motivated…
Fractal Gaussian Networks: A sparse random graph model based on Gaussian Multiplicative Chaos
Subhroshekhar Ghosh, Krishnakumar Balasubramanian, Xiaochuan Yang
We propose a novel stochastic network model, called Fractal Gaussian Network (FGN), that embodies well-defined and analytically tractable fractal structures. Such fractal structure…
Learning from DPPs via Sampling: Beyond HKPV and symmetry
Rémi Bardenet, Subhroshekhar Ghosh
Determinantal point processes (DPPs) have become a significant tool for recommendation systems, feature selection, or summary extraction, harnessing the intrinsic ability of these…