Peter J. Bickel

Learning from learning machines: a new generation of AI technology to meet the needs of science

Nov 27, 2021
Luca Pion-Tonachini, Kristofer Bouchard, Hector Garcia Martin, Sean Peisert, W. Bradley Holtz, Anil Aswani, Dipankar Dwivedi, Haruko Wainwright, Ghanshyam Pilania, Benjamin Nachman, Babetta L. Marrone, Nicola Falco, Prabhat, Daniel Arnold, Alejandro Wolf-Yadlin, Sarah Powers, Sharlee Climer, Quinn Jackson, Ty Carlson, Michael Sohn, Petrus Zwart, Neeraj Kumar, Amy Justice, Claire Tomlin, Daniel Jacobson, Gos Micklem, Georgios V. Gkoutos, Peter J. Bickel, Jean-Baptiste Cazier, Juliane Müller, Bobbie-Jo Webb-Robertson, Rick Stevens, Mark Anderson, Ken Kreutz-Delgado, Michael W. Mahoney, James B. Brown

Figure 2 for Learning from learning machines: a new generation of AI technology to meet the needs of science

Figure 3 for Learning from learning machines: a new generation of AI technology to meet the needs of science

Figure 4 for Learning from learning machines: a new generation of AI technology to meet the needs of science

Access Paper or Ask Questions

Sparse Canonical Correlation Analysis via Concave Minimization

Sep 17, 2019
Omid S. Solari, James B. Brown, Peter J. Bickel

Figure 2 for Sparse Canonical Correlation Analysis via Concave Minimization

Figure 3 for Sparse Canonical Correlation Analysis via Concave Minimization

Figure 4 for Sparse Canonical Correlation Analysis via Concave Minimization

* 45 Pages

Access Paper or Ask Questions

Hierarchical community detection by recursive bi-partitioning

Oct 02, 2018
Tianxi Li, Sharmodeep Bhattacharyya, Purnamrita Sarkar, Peter J. Bickel, Elizaveta Levina

Figure 2 for Hierarchical community detection by recursive bi-partitioning

Figure 3 for Hierarchical community detection by recursive bi-partitioning

Figure 4 for Hierarchical community detection by recursive bi-partitioning

Access Paper or Ask Questions

Two provably consistent divide and conquer clustering algorithms for large networks

Aug 18, 2017
Soumendu Sundar Mukherjee, Purnamrita Sarkar, Peter J. Bickel

Figure 2 for Two provably consistent divide and conquer clustering algorithms for large networks

Figure 3 for Two provably consistent divide and conquer clustering algorithms for large networks

Figure 4 for Two provably consistent divide and conquer clustering algorithms for large networks

* 41 pages, comments are most welcome

Access Paper or Ask Questions

Spectral Clustering and Block Models: A Review And A New Algorithm

Aug 07, 2015
Sharmodeep Bhattacharyya, Peter J. Bickel

* 27 pages

Access Paper or Ask Questions

Role of normalization in spectral clustering for stochastic blockmodels

Jun 25, 2015
Purnamrita Sarkar, Peter J. Bickel

Figure 2 for Role of normalization in spectral clustering for stochastic blockmodels

Figure 3 for Role of normalization in spectral clustering for stochastic blockmodels

Figure 4 for Role of normalization in spectral clustering for stochastic blockmodels

* Annals of Statistics 2015, Vol. 43, No. 3, 962-990
* Published at http://dx.doi.org/10.1214/14-AOS1285 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Access Paper or Ask Questions

Community Detection in Networks using Graph Distance

Jan 24, 2014
Sharmodeep Bhattacharyya, Peter J. Bickel

Figure 2 for Community Detection in Networks using Graph Distance

Figure 3 for Community Detection in Networks using Graph Distance

Figure 4 for Community Detection in Networks using Graph Distance

* Presented in Networks with Community Structure Workshop, Eurandom, January, 2014. arXiv admin note: text overlap with arXiv:math/0504589 by other authors

Access Paper or Ask Questions

Hypothesis Testing for Automated Community Detection in Networks

Nov 20, 2013
Peter J. Bickel, Purnamrita Sarkar

Figure 2 for Hypothesis Testing for Automated Community Detection in Networks

Figure 3 for Hypothesis Testing for Automated Community Detection in Networks

Figure 4 for Hypothesis Testing for Automated Community Detection in Networks

Access Paper or Ask Questions

Pseudo-likelihood methods for community detection in large sparse networks

Nov 05, 2013
Arash A. Amini, Aiyou Chen, Peter J. Bickel, Elizaveta Levina

Figure 2 for Pseudo-likelihood methods for community detection in large sparse networks

Figure 3 for Pseudo-likelihood methods for community detection in large sparse networks

Figure 4 for Pseudo-likelihood methods for community detection in large sparse networks

* Annals of Statistics 2013, Vol. 41, No. 4, 2097-2122
* Published in at http://dx.doi.org/10.1214/13-AOS1138 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Access Paper or Ask Questions

Large Vector Auto Regressions

Jun 20, 2011
Song Song, Peter J. Bickel

Figure 2 for Large Vector Auto Regressions

Figure 3 for Large Vector Auto Regressions

Figure 4 for Large Vector Auto Regressions

Access Paper or Ask Questions

1

2

>>