Zobrazeno 1 - 10
of 147
pro vyhledávání: '"MUKHOPADHYAY, SUBHADEEP"'
Autor:
Mukhopadhyay, Subhadeep
We have entered a new era of machine learning (ML), where the most accurate algorithm with superior predictive power may not even be deployable, unless it is admissible under the regulatory constraints. This has led to great interest in developing fa
Externí odkaz:
http://arxiv.org/abs/2108.07380
Autor:
Mukhopadhyay, Subhadeep
This article introduces a general statistical modeling principle called "Density Sharpening" and applies it to the analysis of discrete count data. The underlying foundation is based on a new theory of nonparametric approximation and smoothing method
Externí odkaz:
http://arxiv.org/abs/2108.07372
Autor:
Mukhopadhyay, Subhadeep, Wang, Kaijun
This paper is dedicated to the "50 Years of the Relevance Problem" - a long-neglected topic that begs attention from practical statisticians who are concerned with the problem of drawing inference from large-scale heterogeneous data.
Comment: Re
Comment: Re
Externí odkaz:
http://arxiv.org/abs/2004.09588
To handle the ubiquitous problem of "dependence learning," copulas are quickly becoming a pervasive tool across a wide range of data-driven disciplines encompassing neuroscience, finance, econometrics, genomics, social science, machine learning, heal
Externí odkaz:
http://arxiv.org/abs/1912.05503
Autor:
Mukhopadhyay, Subhadeep, Wang, Kaijun
Complex networks or graphs are ubiquitous in sciences and engineering: biological networks, brain networks, transportation networks, social networks, and the World Wide Web, to name a few. Spectral graph theory provides a set of useful techniques and
Externí odkaz:
http://arxiv.org/abs/1901.07090
Autor:
Mukhopadhyay, Subhadeep, Wang, Kaijun
Publikováno v:
In Econometrics and Statistics January 2023 25:93-109
Autor:
Mukhopadhyay, Subhadeep
Consider a big data multiple testing task, where, due to storage and computational bottlenecks, one is given a very large collection of p-values by splitting into manageable chunks and distributing over thousands of computer nodes. This paper is conc
Externí odkaz:
http://arxiv.org/abs/1805.02075
Autor:
Mukhopadhyay, Subhadeep
What do we teach and what should we teach? An honest answer to this question is painful, very painful--what we teach lags decades behind what we practice. How can we reduce this `gap' to prepare a data science workforce of trained next-generation sta
Externí odkaz:
http://arxiv.org/abs/1708.04098
Autor:
Mukhopadhyay, Subhadeep
The goal of this paper is to show that there exists a simple, yet universal statistical logic of spectral graph analysis by recasting it into a nonparametric function estimation problem. The prescribed viewpoint appears to be good enough to accommoda
Externí odkaz:
http://arxiv.org/abs/1602.03861
Autor:
Mukhopadhyay, Subhadeep
Bump-hunting or mode identification is a fundamental problem that arises in almost every scientific field of data-driven discovery. Surprisingly, very few data modeling tools are available for automatic (not requiring manual case-by-base investigatio
Externí odkaz:
http://arxiv.org/abs/1509.06428