---
_id: '1092'
abstract:
- lang: eng
text: 'A graphical model encodes conditional independence relations via the Markov
properties. For an undirected graph these conditional independence relations can
be represented by a simple polytope known as the graph associahedron, which can
be constructed as a Minkowski sum of standard simplices. We show that there is
an analogous polytope for conditional independence relations coming from a regular
Gaussian model, and it can be defined using multiinformation or relative entropy.
For directed acyclic graphical models we give a construction of this polytope
as a Minkowski sum of matroid polytopes. Finally, we apply this geometric insight
to construct a new ordering-based search algorithm for causal inference via directed
acyclic graphical models. '
author:
- first_name: Fatemeh
full_name: Mohammadi, Fatemeh
id: 2C29581E-F248-11E8-B48F-1D18A9856A87
last_name: Mohammadi
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
- first_name: Charles
full_name: Wang, Charles
last_name: Wang
- first_name: Josephine
full_name: Yu, Josephine
last_name: Yu
citation:
ama: Mohammadi F, Uhler C, Wang C, Yu J. Generalized permutohedra from probabilistic
graphical models. SIAM Journal on Discrete Mathematics. 2018;32(1):64-93.
doi:10.1137/16M107894X
apa: Mohammadi, F., Uhler, C., Wang, C., & Yu, J. (2018). Generalized permutohedra
from probabilistic graphical models. SIAM Journal on Discrete Mathematics.
SIAM. https://doi.org/10.1137/16M107894X
chicago: Mohammadi, Fatemeh, Caroline Uhler, Charles Wang, and Josephine Yu. “Generalized
Permutohedra from Probabilistic Graphical Models.” SIAM Journal on Discrete
Mathematics. SIAM, 2018. https://doi.org/10.1137/16M107894X.
ieee: F. Mohammadi, C. Uhler, C. Wang, and J. Yu, “Generalized permutohedra from
probabilistic graphical models,” SIAM Journal on Discrete Mathematics,
vol. 32, no. 1. SIAM, pp. 64–93, 2018.
ista: Mohammadi F, Uhler C, Wang C, Yu J. 2018. Generalized permutohedra from probabilistic
graphical models. SIAM Journal on Discrete Mathematics. 32(1), 64–93.
mla: Mohammadi, Fatemeh, et al. “Generalized Permutohedra from Probabilistic Graphical
Models.” SIAM Journal on Discrete Mathematics, vol. 32, no. 1, SIAM, 2018,
pp. 64–93, doi:10.1137/16M107894X.
short: F. Mohammadi, C. Uhler, C. Wang, J. Yu, SIAM Journal on Discrete Mathematics
32 (2018) 64–93.
date_created: 2018-12-11T11:50:06Z
date_published: 2018-01-01T00:00:00Z
date_updated: 2021-01-12T06:48:13Z
day: '01'
doi: 10.1137/16M107894X
extern: '1'
intvolume: ' 32'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1606.01814
month: '01'
oa: 1
oa_version: Preprint
page: 64-93
publication: SIAM Journal on Discrete Mathematics
publication_status: published
publisher: SIAM
publist_id: '6284'
quality_controlled: '1'
status: public
title: Generalized permutohedra from probabilistic graphical models
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 32
year: '2018'
...
---
_id: '2015'
abstract:
- lang: eng
text: We consider the problem of learning a Bayesian network or directed acyclic
graph model from observational data. A number of constraint‐based, score‐based
and hybrid algorithms have been developed for this purpose. Statistical consistency
guarantees of these algorithms rely on the faithfulness assumption, which has
been shown to be restrictive especially for graphs with cycles in the skeleton.
We here propose the sparsest permutation (SP) algorithm, showing that learning
Bayesian networks is possible under strictly weaker assumptions than faithfulness.
This comes at a computational price, thereby indicating a statistical‐computational
trade‐off for causal inference algorithms. In the Gaussian noiseless setting,
we prove that the SP algorithm boils down to finding the permutation of the variables
with the sparsest Cholesky decomposition of the inverse covariance matrix, which
is equivalent to ℓ0‐penalized maximum likelihood estimation. We end with a simulation
study showing that in line with the proven stronger consistency guarantees, and
the SP algorithm compares favourably to standard causal inference algorithms in
terms of accuracy for a given sample size.
article_number: e183
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Garvesh
full_name: Raskutti, Garvesh
last_name: Raskutti
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
citation:
ama: Raskutti G, Uhler C. Learning directed acyclic graphs based on sparsest permutations.
STAT. 2018;7(1). doi:10.1002/sta4.183
apa: Raskutti, G., & Uhler, C. (2018). Learning directed acyclic graphs based
on sparsest permutations. STAT. Wiley. https://doi.org/10.1002/sta4.183
chicago: Raskutti, Garvesh, and Caroline Uhler. “Learning Directed Acyclic Graphs
Based on Sparsest Permutations.” STAT. Wiley, 2018. https://doi.org/10.1002/sta4.183.
ieee: G. Raskutti and C. Uhler, “Learning directed acyclic graphs based on sparsest
permutations,” STAT, vol. 7, no. 1. Wiley, 2018.
ista: Raskutti G, Uhler C. 2018. Learning directed acyclic graphs based on sparsest
permutations. STAT. 7(1), e183.
mla: Raskutti, Garvesh, and Caroline Uhler. “Learning Directed Acyclic Graphs Based
on Sparsest Permutations.” STAT, vol. 7, no. 1, e183, Wiley, 2018, doi:10.1002/sta4.183.
short: G. Raskutti, C. Uhler, STAT 7 (2018).
date_created: 2018-12-11T11:55:13Z
date_published: 2018-04-17T00:00:00Z
date_updated: 2021-01-12T06:54:44Z
day: '17'
doi: 10.1002/sta4.183
extern: '1'
external_id:
arxiv:
- '1307.0366'
intvolume: ' 7'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1307.0366
month: '04'
oa: 1
oa_version: Preprint
publication: STAT
publication_status: published
publisher: Wiley
publist_id: '5061'
quality_controlled: '1'
status: public
title: Learning directed acyclic graphs based on sparsest permutations
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 7
year: '2018'
...
---
_id: '1089'
abstract:
- lang: eng
text: We discuss properties of distributions that are multivariate totally positive
of order two (MTP2) related to conditional independence. In particular, we show
that any independence model generated by an MTP2 distribution is a compositional
semigraphoid which is upward-stable and singleton-transitive. In addition, we
prove that any MTP2 distribution satisfying an appropriate support condition is
faithful to its concentration graph. Finally, we analyze factorization properties
of MTP2 distributions and discuss ways of constructing MTP2 distributions; in
particular we give conditions on the log-linear parameters of a discrete distribution
which ensure MTP2 and characterize conditional Gaussian distributions which satisfy
MTP2.
article_processing_charge: No
author:
- first_name: Shaun
full_name: Fallat, Shaun
last_name: Fallat
- first_name: Steffen
full_name: Lauritzen, Steffen
last_name: Lauritzen
- first_name: Kayvan
full_name: Sadeghi, Kayvan
last_name: Sadeghi
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
- first_name: Nanny
full_name: Wermuth, Nanny
last_name: Wermuth
- first_name: Piotr
full_name: Zwiernik, Piotr
last_name: Zwiernik
citation:
ama: Fallat S, Lauritzen S, Sadeghi K, Uhler C, Wermuth N, Zwiernik P. Total positivity
in Markov structures. Annals of Statistics. 2017;45(3):1152-1184. doi:10.1214/16-AOS1478
apa: Fallat, S., Lauritzen, S., Sadeghi, K., Uhler, C., Wermuth, N., & Zwiernik,
P. (2017). Total positivity in Markov structures. Annals of Statistics.
Institute of Mathematical Statistics. https://doi.org/10.1214/16-AOS1478
chicago: Fallat, Shaun, Steffen Lauritzen, Kayvan Sadeghi, Caroline Uhler, Nanny
Wermuth, and Piotr Zwiernik. “Total Positivity in Markov Structures.” Annals
of Statistics. Institute of Mathematical Statistics, 2017. https://doi.org/10.1214/16-AOS1478.
ieee: S. Fallat, S. Lauritzen, K. Sadeghi, C. Uhler, N. Wermuth, and P. Zwiernik,
“Total positivity in Markov structures,” Annals of Statistics, vol. 45,
no. 3. Institute of Mathematical Statistics, pp. 1152–1184, 2017.
ista: Fallat S, Lauritzen S, Sadeghi K, Uhler C, Wermuth N, Zwiernik P. 2017. Total
positivity in Markov structures. Annals of Statistics. 45(3), 1152–1184.
mla: Fallat, Shaun, et al. “Total Positivity in Markov Structures.” Annals of
Statistics, vol. 45, no. 3, Institute of Mathematical Statistics, 2017, pp.
1152–84, doi:10.1214/16-AOS1478.
short: S. Fallat, S. Lauritzen, K. Sadeghi, C. Uhler, N. Wermuth, P. Zwiernik, Annals
of Statistics 45 (2017) 1152–1184.
date_created: 2018-12-11T11:50:05Z
date_published: 2017-06-01T00:00:00Z
date_updated: 2023-09-20T11:46:53Z
day: '01'
department:
- _id: CaUh
doi: 10.1214/16-AOS1478
external_id:
isi:
- '000404395900008'
intvolume: ' 45'
isi: 1
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1510.01290
month: '06'
oa: 1
oa_version: Submitted Version
page: 1152 - 1184
project:
- _id: 2530CA10-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Y 903-N35
name: 'Gaussian Graphical Models: Theory and Applications'
publication: Annals of Statistics
publication_identifier:
issn:
- '00905364'
publication_status: published
publisher: Institute of Mathematical Statistics
publist_id: '6288'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Total positivity in Markov structures
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 45
year: '2017'
...
---
_id: '2016'
abstract:
- lang: eng
text: The Ising model is one of the simplest and most famous models of interacting
systems. It was originally proposed to model ferromagnetic interactions in statistical
physics and is now widely used to model spatial processes in many areas such as
ecology, sociology, and genetics, usually without testing its goodness-of-fit.
Here, we propose an exact goodness-of-fit test for the finite-lattice Ising model.
The theory of Markov bases has been developed in algebraic statistics for exact
goodness-of-fit testing using a Monte Carlo approach. However, this beautiful
theory has fallen short of its promise for applications, because finding a Markov
basis is usually computationally intractable. We develop a Monte Carlo method
for exact goodness-of-fit testing for the Ising model which avoids computing a
Markov basis and also leads to a better connectivity of the Markov chain and hence
to a faster convergence. We show how this method can be applied to analyze the
spatial organization of receptors on the cell membrane.
article_processing_charge: No
arxiv: 1
author:
- first_name: Abraham
full_name: Martin Del Campo Sanchez, Abraham
last_name: Martin Del Campo Sanchez
- first_name: Sarah A
full_name: Cepeda Humerez, Sarah A
id: 3DEE19A4-F248-11E8-B48F-1D18A9856A87
last_name: Cepeda Humerez
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
citation:
ama: Martin Del Campo Sanchez A, Cepeda Humerez SA, Uhler C. Exact goodness-of-fit
testing for the Ising model. Scandinavian Journal of Statistics. 2017;44(2):285-306.
doi:10.1111/sjos.12251
apa: Martin Del Campo Sanchez, A., Cepeda Humerez, S. A., & Uhler, C. (2017).
Exact goodness-of-fit testing for the Ising model. Scandinavian Journal of
Statistics. Wiley-Blackwell. https://doi.org/10.1111/sjos.12251
chicago: Martin Del Campo Sanchez, Abraham, Sarah A Cepeda Humerez, and Caroline
Uhler. “Exact Goodness-of-Fit Testing for the Ising Model.” Scandinavian Journal
of Statistics. Wiley-Blackwell, 2017. https://doi.org/10.1111/sjos.12251.
ieee: A. Martin Del Campo Sanchez, S. A. Cepeda Humerez, and C. Uhler, “Exact goodness-of-fit
testing for the Ising model,” Scandinavian Journal of Statistics, vol.
44, no. 2. Wiley-Blackwell, pp. 285–306, 2017.
ista: Martin Del Campo Sanchez A, Cepeda Humerez SA, Uhler C. 2017. Exact goodness-of-fit
testing for the Ising model. Scandinavian Journal of Statistics. 44(2), 285–306.
mla: Martin Del Campo Sanchez, Abraham, et al. “Exact Goodness-of-Fit Testing for
the Ising Model.” Scandinavian Journal of Statistics, vol. 44, no. 2, Wiley-Blackwell,
2017, pp. 285–306, doi:10.1111/sjos.12251.
short: A. Martin Del Campo Sanchez, S.A. Cepeda Humerez, C. Uhler, Scandinavian
Journal of Statistics 44 (2017) 285–306.
date_created: 2018-12-11T11:55:13Z
date_published: 2017-06-01T00:00:00Z
date_updated: 2023-09-19T15:13:27Z
day: '01'
department:
- _id: GaTk
doi: 10.1111/sjos.12251
external_id:
arxiv:
- '1410.1242'
isi:
- '000400985000001'
intvolume: ' 44'
isi: 1
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1410.1242
month: '06'
oa: 1
oa_version: Preprint
page: 285 - 306
publication: Scandinavian Journal of Statistics
publication_identifier:
issn:
- '03036898'
publication_status: published
publisher: Wiley-Blackwell
publist_id: '5060'
quality_controlled: '1'
related_material:
record:
- id: '6473'
relation: part_of_dissertation
status: public
scopus_import: '1'
status: public
title: Exact goodness-of-fit testing for the Ising model
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 44
year: '2017'
...
---
_id: '698'
abstract:
- lang: eng
text: 'Extracellular matrix signals from the microenvironment regulate gene expression
patterns and cell behavior. Using a combination of experiments and geometric models,
we demonstrate correlations between cell geometry, three-dimensional (3D) organization
of chromosome territories, and gene expression. Fluorescence in situ hybridization
experiments showed that micropatterned fibroblasts cultured on anisotropic versus
isotropic substrates resulted in repositioning of specific chromosomes, which
contained genes that were differentially regulated by cell geometries. Experiments
combined with ellipsoid packing models revealed that the mechanosensitivity of
chromosomes was correlated with their orientation in the nucleus. Transcription
inhibition experiments suggested that the intermingling degree was more sensitive
to global changes in transcription than to chromosome radial positioning and its
orientations. These results suggested that cell geometry modulated 3D chromosome
arrangement, and their neighborhoods correlated with gene expression patterns
in a predictable manner. This is central to understanding geometric control of
genetic programs involved in cellular homeostasis and the associated diseases. '
author:
- first_name: Yejun
full_name: Wang, Yejun
last_name: Wang
- first_name: Mallika
full_name: Nagarajan, Mallika
last_name: Nagarajan
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
- first_name: Gv
full_name: Shivashankar, Gv
last_name: Shivashankar
citation:
ama: Wang Y, Nagarajan M, Uhler C, Shivashankar G. Orientation and repositioning
of chromosomes correlate with cell geometry dependent gene expression. Molecular
Biology of the Cell. 2017;28(14):1997-2009. doi:10.1091/mbc.E16-12-0825
apa: Wang, Y., Nagarajan, M., Uhler, C., & Shivashankar, G. (2017). Orientation
and repositioning of chromosomes correlate with cell geometry dependent gene expression.
Molecular Biology of the Cell. American Society for Cell Biology. https://doi.org/10.1091/mbc.E16-12-0825
chicago: Wang, Yejun, Mallika Nagarajan, Caroline Uhler, and Gv Shivashankar. “Orientation
and Repositioning of Chromosomes Correlate with Cell Geometry Dependent Gene Expression.”
Molecular Biology of the Cell. American Society for Cell Biology, 2017.
https://doi.org/10.1091/mbc.E16-12-0825.
ieee: Y. Wang, M. Nagarajan, C. Uhler, and G. Shivashankar, “Orientation and repositioning
of chromosomes correlate with cell geometry dependent gene expression,” Molecular
Biology of the Cell, vol. 28, no. 14. American Society for Cell Biology, pp.
1997–2009, 2017.
ista: Wang Y, Nagarajan M, Uhler C, Shivashankar G. 2017. Orientation and repositioning
of chromosomes correlate with cell geometry dependent gene expression. Molecular
Biology of the Cell. 28(14), 1997–2009.
mla: Wang, Yejun, et al. “Orientation and Repositioning of Chromosomes Correlate
with Cell Geometry Dependent Gene Expression.” Molecular Biology of the Cell,
vol. 28, no. 14, American Society for Cell Biology, 2017, pp. 1997–2009, doi:10.1091/mbc.E16-12-0825.
short: Y. Wang, M. Nagarajan, C. Uhler, G. Shivashankar, Molecular Biology of the
Cell 28 (2017) 1997–2009.
date_created: 2018-12-11T11:47:59Z
date_published: 2017-07-07T00:00:00Z
date_updated: 2021-01-12T08:11:17Z
day: '07'
ddc:
- '519'
department:
- _id: CaUh
doi: 10.1091/mbc.E16-12-0825
file:
- access_level: open_access
checksum: de01dac9e30970cfa6ae902480a4e04d
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:10:53Z
date_updated: 2020-07-14T12:47:46Z
file_id: '4844'
file_name: IST-2017-892-v1+1_Mol._Biol._Cell-2017-Wang-1997-2009.pdf
file_size: 1086097
relation: main_file
file_date_updated: 2020-07-14T12:47:46Z
has_accepted_license: '1'
intvolume: ' 28'
issue: '14'
language:
- iso: eng
month: '07'
oa: 1
oa_version: Published Version
page: 1997 - 2009
project:
- _id: 2530CA10-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Y 903-N35
name: 'Gaussian Graphical Models: Theory and Applications'
publication: Molecular Biology of the Cell
publication_identifier:
issn:
- '10591524'
publication_status: published
publisher: American Society for Cell Biology
publist_id: '7001'
pubrep_id: '892'
quality_controlled: '1'
scopus_import: 1
status: public
title: Orientation and repositioning of chromosomes correlate with cell geometry dependent
gene expression
tmp:
image: /images/cc_by_nc_sa.png
legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC
BY-NC-SA 4.0)
short: CC BY-NC-SA (4.0)
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 28
year: '2017'
...
---
_id: '1208'
abstract:
- lang: eng
text: We study parameter estimation in linear Gaussian covariance models, which
are p-dimensional Gaussian models with linear constraints on the covariance matrix.
Maximum likelihood estimation for this class of models leads to a non-convex optimization
problem which typically has many local maxima. Using recent results on the asymptotic
distribution of extreme eigenvalues of the Wishart distribution, we provide sufficient
conditions for any hill climbing method to converge to the global maximum. Although
we are primarily interested in the case in which n≫p, the proofs of our results
utilize large sample asymptotic theory under the scheme n/p→γ>1. Remarkably,
our numerical simulations indicate that our results remain valid for p as small
as 2. An important consequence of this analysis is that, for sample sizes n≃14p,
maximum likelihood estimation for linear Gaussian covariance models behaves as
if it were a convex optimization problem. © 2016 The Royal Statistical Society
and Blackwell Publishing Ltd.
article_processing_charge: No
author:
- first_name: Piotr
full_name: Zwiernik, Piotr
last_name: Zwiernik
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
- first_name: Donald
full_name: Richards, Donald
last_name: Richards
citation:
ama: 'Zwiernik P, Uhler C, Richards D. Maximum likelihood estimation for linear
Gaussian covariance models. Journal of the Royal Statistical Society Series
B: Statistical Methodology. 2017;79(4):1269-1292. doi:10.1111/rssb.12217'
apa: 'Zwiernik, P., Uhler, C., & Richards, D. (2017). Maximum likelihood estimation
for linear Gaussian covariance models. Journal of the Royal Statistical Society.
Series B: Statistical Methodology. Wiley-Blackwell. https://doi.org/10.1111/rssb.12217'
chicago: 'Zwiernik, Piotr, Caroline Uhler, and Donald Richards. “Maximum Likelihood
Estimation for Linear Gaussian Covariance Models.” Journal of the Royal Statistical
Society. Series B: Statistical Methodology. Wiley-Blackwell, 2017. https://doi.org/10.1111/rssb.12217.'
ieee: 'P. Zwiernik, C. Uhler, and D. Richards, “Maximum likelihood estimation for
linear Gaussian covariance models,” Journal of the Royal Statistical Society.
Series B: Statistical Methodology, vol. 79, no. 4. Wiley-Blackwell, pp. 1269–1292,
2017.'
ista: 'Zwiernik P, Uhler C, Richards D. 2017. Maximum likelihood estimation for
linear Gaussian covariance models. Journal of the Royal Statistical Society. Series
B: Statistical Methodology. 79(4), 1269–1292.'
mla: 'Zwiernik, Piotr, et al. “Maximum Likelihood Estimation for Linear Gaussian
Covariance Models.” Journal of the Royal Statistical Society. Series B: Statistical
Methodology, vol. 79, no. 4, Wiley-Blackwell, 2017, pp. 1269–92, doi:10.1111/rssb.12217.'
short: 'P. Zwiernik, C. Uhler, D. Richards, Journal of the Royal Statistical Society.
Series B: Statistical Methodology 79 (2017) 1269–1292.'
date_created: 2018-12-11T11:50:43Z
date_published: 2017-09-01T00:00:00Z
date_updated: 2023-09-20T11:17:21Z
day: '01'
department:
- _id: CaUh
doi: 10.1111/rssb.12217
external_id:
isi:
- '000411712300012'
intvolume: ' 79'
isi: 1
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1408.5604
month: '09'
oa: 1
oa_version: Submitted Version
page: 1269 - 1292
project:
- _id: 2530CA10-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Y 903-N35
name: 'Gaussian Graphical Models: Theory and Applications'
publication: 'Journal of the Royal Statistical Society. Series B: Statistical Methodology'
publication_identifier:
issn:
- '13697412'
publication_status: published
publisher: Wiley-Blackwell
publist_id: '6142'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Maximum likelihood estimation for linear Gaussian covariance models
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 79
year: '2017'
...
---
_id: '1088'
abstract:
- lang: eng
text: Cell geometry is tightly coupled to gene expression patterns within the tissue
microenvironment. This perspective synthesizes evidence that the 3D organization
of chromosomes is a critical intermediate for geometric control of genomic programs.
Using a combination of experiments and modeling we outline approaches to decipher
the mechano-genomic code that governs cellular homeostasis and reprogramming.
author:
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
- first_name: G V
full_name: Shivashankar, G V
last_name: Shivashankar
citation:
ama: Uhler C, Shivashankar GV. Geometric control and modeling of genome reprogramming.
BioArchitecture. 2016;6(4):76-84. doi:10.1080/19490992.2016.1201620
apa: Uhler, C., & Shivashankar, G. V. (2016). Geometric control and modeling
of genome reprogramming. BioArchitecture. Taylor & Francis. https://doi.org/10.1080/19490992.2016.1201620
chicago: Uhler, Caroline, and G V Shivashankar. “Geometric Control and Modeling
of Genome Reprogramming.” BioArchitecture. Taylor & Francis, 2016.
https://doi.org/10.1080/19490992.2016.1201620.
ieee: C. Uhler and G. V. Shivashankar, “Geometric control and modeling of genome
reprogramming,” BioArchitecture, vol. 6, no. 4. Taylor & Francis, pp.
76–84, 2016.
ista: Uhler C, Shivashankar GV. 2016. Geometric control and modeling of genome reprogramming.
BioArchitecture. 6(4), 76–84.
mla: Uhler, Caroline, and G. V. Shivashankar. “Geometric Control and Modeling of
Genome Reprogramming.” BioArchitecture, vol. 6, no. 4, Taylor & Francis,
2016, pp. 76–84, doi:10.1080/19490992.2016.1201620.
short: C. Uhler, G.V. Shivashankar, BioArchitecture 6 (2016) 76–84.
date_created: 2018-12-11T11:50:05Z
date_published: 2016-07-27T00:00:00Z
date_updated: 2021-01-12T06:48:11Z
day: '27'
doi: 10.1080/19490992.2016.1201620
extern: '1'
intvolume: ' 6'
issue: '4'
language:
- iso: eng
month: '07'
oa_version: None
page: 76 - 84
publication: BioArchitecture
publication_status: published
publisher: Taylor & Francis
publist_id: '6289'
quality_controlled: '1'
status: public
title: Geometric control and modeling of genome reprogramming
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 6
year: '2016'
...
---
_id: '1480'
abstract:
- lang: eng
text: 'Exponential varieties arise from exponential families in statistics. These
real algebraic varieties have strong positivity and convexity properties, familiar
from toric varieties and their moment maps. Among them are varieties of inverses
of symmetric matrices satisfying linear constraints. This class includes Gaussian
graphical models. We develop a general theory of exponential varieties. These
are derived from hyperbolic polynomials and their integral representations. We
compare the multidegrees and ML degrees of the gradient map for hyperbolic polynomials. '
author:
- first_name: Mateusz
full_name: Michałek, Mateusz
last_name: Michałek
- first_name: Bernd
full_name: Sturmfels, Bernd
last_name: Sturmfels
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
- first_name: Piotr
full_name: Zwiernik, Piotr
last_name: Zwiernik
citation:
ama: Michałek M, Sturmfels B, Uhler C, Zwiernik P. Exponential varieties. Proceedings
of the London Mathematical Society. 2016;112(1):27-56. doi:10.1112/plms/pdv066
apa: Michałek, M., Sturmfels, B., Uhler, C., & Zwiernik, P. (2016). Exponential
varieties. Proceedings of the London Mathematical Society. Oxford University
Press. https://doi.org/10.1112/plms/pdv066
chicago: Michałek, Mateusz, Bernd Sturmfels, Caroline Uhler, and Piotr Zwiernik.
“Exponential Varieties.” Proceedings of the London Mathematical Society.
Oxford University Press, 2016. https://doi.org/10.1112/plms/pdv066.
ieee: M. Michałek, B. Sturmfels, C. Uhler, and P. Zwiernik, “Exponential varieties,”
Proceedings of the London Mathematical Society, vol. 112, no. 1. Oxford
University Press, pp. 27–56, 2016.
ista: Michałek M, Sturmfels B, Uhler C, Zwiernik P. 2016. Exponential varieties.
Proceedings of the London Mathematical Society. 112(1), 27–56.
mla: Michałek, Mateusz, et al. “Exponential Varieties.” Proceedings of the London
Mathematical Society, vol. 112, no. 1, Oxford University Press, 2016, pp.
27–56, doi:10.1112/plms/pdv066.
short: M. Michałek, B. Sturmfels, C. Uhler, P. Zwiernik, Proceedings of the London
Mathematical Society 112 (2016) 27–56.
date_created: 2018-12-11T11:52:16Z
date_published: 2016-01-07T00:00:00Z
date_updated: 2021-01-12T06:51:02Z
day: '07'
department:
- _id: CaUh
doi: 10.1112/plms/pdv066
intvolume: ' 112'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1412.6185
month: '01'
oa: 1
oa_version: Preprint
page: 27 - 56
publication: Proceedings of the London Mathematical Society
publication_status: published
publisher: Oxford University Press
publist_id: '5714'
quality_controlled: '1'
scopus_import: 1
status: public
title: Exponential varieties
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 112
year: '2016'
...
---
_id: '1293'
abstract:
- lang: eng
text: For a graph G with p vertices the closed convex cone S⪰0(G) consists of all
real positive semidefinite p×p matrices whose sparsity pattern is given by G,
that is, those matrices with zeros in the off-diagonal entries corresponding to
nonedges of G. The extremal rays of this cone and their associated ranks have
applications to matrix completion problems, maximum likelihood estimation in Gaussian
graphical models in statistics, and Gauss elimination for sparse matrices. While
the maximum rank of an extremal ray in S⪰0(G), known as the sparsity order of
G, has been characterized for different classes of graphs, we here study all possible
extremal ranks of S⪰0(G). We investigate when the geometry of the (±1)-cut polytope
of G yields a polyhedral characterization of the set of extremal ranks of S⪰0(G).
For a graph G without K5 minors, we show that appropriately chosen normal vectors
to the facets of the (±1)-cut polytope of G specify the off-diagonal entries of
extremal matrices in S⪰0(G). We also prove that for appropriately chosen scalars
the constant term of the linear equation of each facet-supporting hyperplane is
the rank of its corresponding extremal matrix in S⪰0(G). Furthermore, we show
that if G is series-parallel then this gives a complete characterization of all
possible extremal ranks of S⪰0(G). Consequently, the sparsity order problem for
series-parallel graphs can be solved in terms of polyhedral geometry.
acknowledgement: We wish to thank Alexander Engström and Bernd Sturmfels for various
valuable discussions and insights. We also thank the two anonymous referees for
their thoughtful feedback on the paper. CU was partially supported by the Austrian
Science Fund (FWF) Y 903-N35.
author:
- first_name: Liam T
full_name: Solus, Liam T
id: 2AADA620-F248-11E8-B48F-1D18A9856A87
last_name: Solus
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
- first_name: Ruriko
full_name: Yoshida, Ruriko
last_name: Yoshida
citation:
ama: Solus LT, Uhler C, Yoshida R. Extremal positive semidefinite matrices whose
sparsity pattern is given by graphs without K5 minors. Linear Algebra and Its
Applications. 2016;509:247-275. doi:10.1016/j.laa.2016.07.026
apa: Solus, L. T., Uhler, C., & Yoshida, R. (2016). Extremal positive semidefinite
matrices whose sparsity pattern is given by graphs without K5 minors. Linear
Algebra and Its Applications. Elsevier. https://doi.org/10.1016/j.laa.2016.07.026
chicago: Solus, Liam T, Caroline Uhler, and Ruriko Yoshida. “Extremal Positive Semidefinite
Matrices Whose Sparsity Pattern Is given by Graphs without K5 Minors.” Linear
Algebra and Its Applications. Elsevier, 2016. https://doi.org/10.1016/j.laa.2016.07.026.
ieee: L. T. Solus, C. Uhler, and R. Yoshida, “Extremal positive semidefinite matrices
whose sparsity pattern is given by graphs without K5 minors,” Linear Algebra
and Its Applications, vol. 509. Elsevier, pp. 247–275, 2016.
ista: Solus LT, Uhler C, Yoshida R. 2016. Extremal positive semidefinite matrices
whose sparsity pattern is given by graphs without K5 minors. Linear Algebra and
Its Applications. 509, 247–275.
mla: Solus, Liam T., et al. “Extremal Positive Semidefinite Matrices Whose Sparsity
Pattern Is given by Graphs without K5 Minors.” Linear Algebra and Its Applications,
vol. 509, Elsevier, 2016, pp. 247–75, doi:10.1016/j.laa.2016.07.026.
short: L.T. Solus, C. Uhler, R. Yoshida, Linear Algebra and Its Applications 509
(2016) 247–275.
date_created: 2018-12-11T11:51:11Z
date_published: 2016-11-15T00:00:00Z
date_updated: 2021-01-12T06:49:40Z
day: '15'
department:
- _id: CaUh
doi: 10.1016/j.laa.2016.07.026
intvolume: ' 509'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/1506.06702.pdf
month: '11'
oa: 1
oa_version: Preprint
page: 247 - 275
project:
- _id: 2530CA10-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: Y 903-N35
name: 'Gaussian Graphical Models: Theory and Applications'
publication: Linear Algebra and Its Applications
publication_status: published
publisher: Elsevier
publist_id: '6024'
quality_controlled: '1'
scopus_import: 1
status: public
title: Extremal positive semidefinite matrices whose sparsity pattern is given by
graphs without K5 minors
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 509
year: '2016'
...
---
_id: '2014'
abstract:
- lang: eng
text: The concepts of faithfulness and strong-faithfulness are important for statistical
learning of graphical models. Graphs are not sufficient for describing the association
structure of a discrete distribution. Hypergraphs representing hierarchical log-linear
models are considered instead, and the concept of parametric (strong-) faithfulness
with respect to a hypergraph is introduced. Strong-faithfulness ensures the existence
of uniformly consistent parameter estimators and enables building uniformly consistent
procedures for a hypergraph search. The strength of association in a discrete
distribution can be quantified with various measures, leading to different concepts
of strong-faithfulness. Lower and upper bounds for the proportions of distributions
that do not satisfy strong-faithfulness are computed for different parameterizations
and measures of association.
author:
- first_name: Anna
full_name: Klimova, Anna
id: 31934120-F248-11E8-B48F-1D18A9856A87
last_name: Klimova
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
- first_name: Tamás
full_name: Rudas, Tamás
last_name: Rudas
citation:
ama: Klimova A, Uhler C, Rudas T. Faithfulness and learning hypergraphs from discrete
distributions. Computational Statistics & Data Analysis. 2015;87(7):57-72.
doi:10.1016/j.csda.2015.01.017
apa: Klimova, A., Uhler, C., & Rudas, T. (2015). Faithfulness and learning hypergraphs
from discrete distributions. Computational Statistics & Data Analysis.
Elsevier. https://doi.org/10.1016/j.csda.2015.01.017
chicago: Klimova, Anna, Caroline Uhler, and Tamás Rudas. “Faithfulness and Learning
Hypergraphs from Discrete Distributions.” Computational Statistics & Data
Analysis. Elsevier, 2015. https://doi.org/10.1016/j.csda.2015.01.017.
ieee: A. Klimova, C. Uhler, and T. Rudas, “Faithfulness and learning hypergraphs
from discrete distributions,” Computational Statistics & Data Analysis,
vol. 87, no. 7. Elsevier, pp. 57–72, 2015.
ista: Klimova A, Uhler C, Rudas T. 2015. Faithfulness and learning hypergraphs from
discrete distributions. Computational Statistics & Data Analysis. 87(7), 57–72.
mla: Klimova, Anna, et al. “Faithfulness and Learning Hypergraphs from Discrete
Distributions.” Computational Statistics & Data Analysis, vol. 87,
no. 7, Elsevier, 2015, pp. 57–72, doi:10.1016/j.csda.2015.01.017.
short: A. Klimova, C. Uhler, T. Rudas, Computational Statistics & Data Analysis
87 (2015) 57–72.
date_created: 2018-12-11T11:55:13Z
date_published: 2015-07-01T00:00:00Z
date_updated: 2021-01-12T06:54:43Z
day: '01'
department:
- _id: CaUh
doi: 10.1016/j.csda.2015.01.017
intvolume: ' 87'
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1404.6617
month: '07'
oa: 1
oa_version: Preprint
page: 57 - 72
publication: Computational Statistics & Data Analysis
publication_status: published
publisher: Elsevier
publist_id: '5062'
quality_controlled: '1'
scopus_import: 1
status: public
title: Faithfulness and learning hypergraphs from discrete distributions
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 87
year: '2015'
...
---
_id: '2011'
abstract:
- lang: eng
text: The protection of privacy of individual-level information in genome-wide association
study (GWAS) databases has been a major concern of researchers following the publication
of “an attack” on GWAS data by Homer et al. (2008). Traditional statistical methods
for confidentiality and privacy protection of statistical databases do not scale
well to deal with GWAS data, especially in terms of guarantees regarding protection
from linkage to external information. The more recent concept of differential
privacy, introduced by the cryptographic community, is an approach that provides
a rigorous definition of privacy with meaningful privacy guarantees in the presence
of arbitrary external information, although the guarantees may come at a serious
price in terms of data utility. Building on such notions, Uhler et al. (2013)
proposed new methods to release aggregate GWAS data without compromising an individual’s
privacy. We extend the methods developed in Uhler et al. (2013) for releasing
differentially-private χ2χ2-statistics by allowing for arbitrary number of cases
and controls, and for releasing differentially-private allelic test statistics.
We also provide a new interpretation by assuming the controls’ data are known,
which is a realistic assumption because some GWAS use publicly available data
as controls. We assess the performance of the proposed methods through a risk-utility
analysis on a real data set consisting of DNA samples collected by the Wellcome
Trust Case Control Consortium and compare the methods with the differentially-private
release mechanism proposed by Johnson and Shmatikov (2013).
acknowledgement: This research was partially supported by NSF Awards EMSW21-RTG and
BCS-0941518 to the Department of Statistics at Carnegie Mellon University, and by
NSF Grant BCS-0941553 to the Department of Statistics at Pennsylvania State University.
This work was also supported in part by the National Center for Research Resources,
Grant UL1 RR033184, and is now at the National Center for Advancing Translational
Sciences, Grant UL1 TR000127 to Pennsylvania State University. The content is solely
the responsibility of the authors and does not necessarily represent the official
views of the NSF and NIH.
author:
- first_name: Fei
full_name: Yu, Fei
last_name: Yu
- first_name: Stephen
full_name: Fienberg, Stephen
last_name: Fienberg
- first_name: Alexandra
full_name: Slaković, Alexandra
last_name: Slaković
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
citation:
ama: Yu F, Fienberg S, Slaković A, Uhler C. Scalable privacy-preserving data sharing
methodology for genome-wide association studies. Journal of Biomedical Informatics.
2014;50:133-141. doi:10.1016/j.jbi.2014.01.008
apa: Yu, F., Fienberg, S., Slaković, A., & Uhler, C. (2014). Scalable privacy-preserving
data sharing methodology for genome-wide association studies. Journal of Biomedical
Informatics. Elsevier. https://doi.org/10.1016/j.jbi.2014.01.008
chicago: Yu, Fei, Stephen Fienberg, Alexandra Slaković, and Caroline Uhler. “Scalable
Privacy-Preserving Data Sharing Methodology for Genome-Wide Association Studies.”
Journal of Biomedical Informatics. Elsevier, 2014. https://doi.org/10.1016/j.jbi.2014.01.008.
ieee: F. Yu, S. Fienberg, A. Slaković, and C. Uhler, “Scalable privacy-preserving
data sharing methodology for genome-wide association studies,” Journal of Biomedical
Informatics, vol. 50. Elsevier, pp. 133–141, 2014.
ista: Yu F, Fienberg S, Slaković A, Uhler C. 2014. Scalable privacy-preserving data
sharing methodology for genome-wide association studies. Journal of Biomedical
Informatics. 50, 133–141.
mla: Yu, Fei, et al. “Scalable Privacy-Preserving Data Sharing Methodology for Genome-Wide
Association Studies.” Journal of Biomedical Informatics, vol. 50, Elsevier,
2014, pp. 133–41, doi:10.1016/j.jbi.2014.01.008.
short: F. Yu, S. Fienberg, A. Slaković, C. Uhler, Journal of Biomedical Informatics
50 (2014) 133–141.
date_created: 2018-12-11T11:55:12Z
date_published: 2014-08-01T00:00:00Z
date_updated: 2021-01-12T06:54:42Z
day: '01'
department:
- _id: CaUh
doi: 10.1016/j.jbi.2014.01.008
intvolume: ' 50'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1401.5193
month: '08'
oa: 1
oa_version: Submitted Version
page: 133 - 141
publication: Journal of Biomedical Informatics
publication_status: published
publisher: Elsevier
publist_id: '5065'
quality_controlled: '1'
scopus_import: 1
status: public
title: Scalable privacy-preserving data sharing methodology for genome-wide association
studies
type: journal_article
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 50
year: '2014'
...
---
_id: '2012'
abstract:
- lang: eng
text: The classical sphere packing problem asks for the best (infinite) arrangement
of non-overlapping unit balls which cover as much space as possible. We define
a generalized version of the problem, where we allow each ball a limited amount
of overlap with other balls. We study two natural choices of overlap measures
and obtain the optimal lattice packings in a parameterized family of lattices
which contains the FCC, BCC, and integer lattice.
acknowledgement: We thank Herbert Edelsbrunner for his valuable discussions and ideas
on the topic of this paper. The second author has been supported by the Max Planck
Center for Visual Computing and Communication
article_number: '1401.0468'
article_processing_charge: No
arxiv: 1
author:
- first_name: Mabel
full_name: Iglesias Ham, Mabel
id: 41B58C0C-F248-11E8-B48F-1D18A9856A87
last_name: Iglesias Ham
- first_name: Michael
full_name: Kerber, Michael
last_name: Kerber
orcid: 0000-0002-8030-9299
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
citation:
ama: Iglesias Ham M, Kerber M, Uhler C. Sphere packing with limited overlap. arXiv.
doi:10.48550/arXiv.1401.0468
apa: Iglesias Ham, M., Kerber, M., & Uhler, C. (n.d.). Sphere packing with limited
overlap. arXiv. https://doi.org/10.48550/arXiv.1401.0468
chicago: Iglesias Ham, Mabel, Michael Kerber, and Caroline Uhler. “Sphere Packing
with Limited Overlap.” ArXiv, n.d. https://doi.org/10.48550/arXiv.1401.0468.
ieee: M. Iglesias Ham, M. Kerber, and C. Uhler, “Sphere packing with limited overlap,”
arXiv. .
ista: Iglesias Ham M, Kerber M, Uhler C. Sphere packing with limited overlap. arXiv,
1401.0468.
mla: Iglesias Ham, Mabel, et al. “Sphere Packing with Limited Overlap.” ArXiv,
1401.0468, doi:10.48550/arXiv.1401.0468.
short: M. Iglesias Ham, M. Kerber, C. Uhler, ArXiv (n.d.).
date_created: 2018-12-11T11:55:12Z
date_published: 2014-01-01T00:00:00Z
date_updated: 2023-10-18T08:06:45Z
day: '01'
department:
- _id: HeEd
- _id: CaUh
doi: 10.48550/arXiv.1401.0468
external_id:
arxiv:
- '1401.0468'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://cccg.ca/proceedings/2014/papers/paper23.pdf
month: '01'
oa: 1
oa_version: Submitted Version
publication: arXiv
publication_status: submitted
publist_id: '5064'
status: public
title: Sphere packing with limited overlap
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2014'
...
---
_id: '2013'
abstract:
- lang: eng
text: "An asymptotic theory is developed for computing volumes of regions in the
parameter space of a directed Gaussian graphical model that are obtained by bounding
partial correlations. We study these volumes using the method of real log canonical
thresholds from algebraic geometry. Our analysis involves the computation of the
singular loci of correlation hypersurfaces. Statistical applications include the
strong-faithfulness assumption for the PC algorithm and the quantification of
confounder bias in causal inference. A detailed analysis is presented for trees,
bow ties, tripartite graphs, and complete graphs.\r\n"
acknowledgement: This work was supported in part by the US National Science Foundation
(DMS-0968882) and the Defense Advanced Research Projects Agency (DARPA) Deep Learning
program (FA8650-10-C-7020).
author:
- first_name: Shaowei
full_name: Lin, Shaowei
last_name: Lin
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
- first_name: Bernd
full_name: Sturmfels, Bernd
last_name: Sturmfels
- first_name: Peter
full_name: Bühlmann, Peter
last_name: Bühlmann
citation:
ama: Lin S, Uhler C, Sturmfels B, Bühlmann P. Hypersurfaces and their singularities
in partial correlation testing. Foundations of Computational Mathematics.
2014;14(5):1079-1116. doi:10.1007/s10208-014-9205-0
apa: Lin, S., Uhler, C., Sturmfels, B., & Bühlmann, P. (2014). Hypersurfaces
and their singularities in partial correlation testing. Foundations of Computational
Mathematics. Springer. https://doi.org/10.1007/s10208-014-9205-0
chicago: Lin, Shaowei, Caroline Uhler, Bernd Sturmfels, and Peter Bühlmann. “Hypersurfaces
and Their Singularities in Partial Correlation Testing.” Foundations of Computational
Mathematics. Springer, 2014. https://doi.org/10.1007/s10208-014-9205-0.
ieee: S. Lin, C. Uhler, B. Sturmfels, and P. Bühlmann, “Hypersurfaces and their
singularities in partial correlation testing,” Foundations of Computational
Mathematics, vol. 14, no. 5. Springer, pp. 1079–1116, 2014.
ista: Lin S, Uhler C, Sturmfels B, Bühlmann P. 2014. Hypersurfaces and their singularities
in partial correlation testing. Foundations of Computational Mathematics. 14(5),
1079–1116.
mla: Lin, Shaowei, et al. “Hypersurfaces and Their Singularities in Partial Correlation
Testing.” Foundations of Computational Mathematics, vol. 14, no. 5, Springer,
2014, pp. 1079–116, doi:10.1007/s10208-014-9205-0.
short: S. Lin, C. Uhler, B. Sturmfels, P. Bühlmann, Foundations of Computational
Mathematics 14 (2014) 1079–1116.
date_created: 2018-12-11T11:55:12Z
date_published: 2014-10-10T00:00:00Z
date_updated: 2021-01-12T06:54:43Z
day: '10'
department:
- _id: CaUh
doi: 10.1007/s10208-014-9205-0
intvolume: ' 14'
issue: '5'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1209.0285
month: '10'
oa: 1
oa_version: Submitted Version
page: 1079 - 1116
publication: Foundations of Computational Mathematics
publication_status: published
publisher: Springer
publist_id: '5063'
quality_controlled: '1'
scopus_import: 1
status: public
title: Hypersurfaces and their singularities in partial correlation testing
type: journal_article
user_id: 4435EBFC-F248-11E8-B48F-1D18A9856A87
volume: 14
year: '2014'
...
---
_id: '2017'
abstract:
- lang: eng
text: ' Gaussian graphical models have received considerable attention during
the past four decades from the statistical and machine learning communities. In
Bayesian treatments of this model, the G-Wishart distribution serves as the conjugate
prior for inverse covariance matrices satisfying graphical constraints. While
it is straightforward to posit the unnormalized densities, the normalizing constants
of these distributions have been known only for graphs that are chordal, or decomposable.
Up until now, it was unknown whether the normalizing constant for a general graph
could be represented explicitly, and a considerable body of computational literature
emerged that attempted to avoid this apparent intractability. We close this question
by providing an explicit representation of the G-Wishart normalizing constant
for general graphs.'
acknowledgement: |-
A.L.'s research was supported by Statistics for Innovation sfi2 in Oslo.
D.R.'s research was partially supported by the U.S. National Science Foun-dation grant DMS-1309808; and by a Romberg Guest Professorship at the Heidelberg University Graduate School for Mathematical and Computational Methods in the Sciences, funded by German Universities Excellence Initiative grant GSC 220/2.
author:
- first_name: Caroline
full_name: Caroline Uhler
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
- first_name: Alex
full_name: Lenkoski, Alex
last_name: Lenkoski
- first_name: Donald
full_name: Richards, Donald
last_name: Richards
citation:
ama: Uhler C, Lenkoski A, Richards D. Exact formulas for the normalizing constants
of Wishart distributions for graphical models. ArXiv. 2014.
apa: Uhler, C., Lenkoski, A., & Richards, D. (2014). Exact formulas for the
normalizing constants of Wishart distributions for graphical models. ArXiv.
ArXiv.
chicago: Uhler, Caroline, Alex Lenkoski, and Donald Richards. “ Exact Formulas for
the Normalizing Constants of Wishart Distributions for Graphical Models.” ArXiv.
ArXiv, 2014.
ieee: C. Uhler, A. Lenkoski, and D. Richards, “ Exact formulas for the normalizing
constants of Wishart distributions for graphical models,” ArXiv. ArXiv,
2014.
ista: Uhler C, Lenkoski A, Richards D. 2014. Exact formulas for the normalizing
constants of Wishart distributions for graphical models. ArXiv, .
mla: Uhler, Caroline, et al. “ Exact Formulas for the Normalizing Constants of Wishart
Distributions for Graphical Models.” ArXiv, ArXiv, 2014.
short: C. Uhler, A. Lenkoski, D. Richards, ArXiv (2014).
date_created: 2018-12-11T11:55:14Z
date_published: 2014-06-18T00:00:00Z
date_updated: 2021-01-12T06:54:44Z
day: '18'
extern: 1
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1406.4901
month: '06'
oa: 1
publication: ArXiv
publication_status: published
publisher: ArXiv
publist_id: '5058'
quality_controlled: 0
status: public
title: ' Exact formulas for the normalizing constants of Wishart distributions for
graphical models'
type: preprint
year: '2014'
...
---
_id: '2047'
abstract:
- lang: eng
text: Following the publication of an attack on genome-wide association studies
(GWAS) data proposed by Homer et al., considerable attention has been given to
developing methods for releasing GWAS data in a privacy-preserving way. Here,
we develop an end-to-end differentially private method for solving regression
problems with convex penalty functions and selecting the penalty parameters by
cross-validation. In particular, we focus on penalized logistic regression with
elastic-net regularization, a method widely used to in GWAS analyses to identify
disease-causing genes. We show how a differentially private procedure for penalized
logistic regression with elastic-net regularization can be applied to the analysis
of GWAS data and evaluate our method’s performance.
acknowledgement: This research was partially supported by BCS- 0941518 to the Department
of Statistics at Carnegie Mellon University.
alternative_title:
- LNCS
arxiv: 1
author:
- first_name: Fei
full_name: Yu, Fei
last_name: Yu
- first_name: Michal
full_name: Rybar, Michal
id: 2B3E3DE8-F248-11E8-B48F-1D18A9856A87
last_name: Rybar
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
- first_name: Stephen
full_name: Fienberg, Stephen
last_name: Fienberg
citation:
ama: 'Yu F, Rybar M, Uhler C, Fienberg S. Differentially-private logistic regression
for detecting multiple-SNP association in GWAS databases. In: Domingo Ferrer J,
ed. Lecture Notes in Computer Science (Including Subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics). Vol 8744. Springer;
2014:170-184. doi:10.1007/978-3-319-11257-2_14'
apa: 'Yu, F., Rybar, M., Uhler, C., & Fienberg, S. (2014). Differentially-private
logistic regression for detecting multiple-SNP association in GWAS databases.
In J. Domingo Ferrer (Ed.), Lecture Notes in Computer Science (including subseries
Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(Vol. 8744, pp. 170–184). Ibiza, Spain: Springer. https://doi.org/10.1007/978-3-319-11257-2_14'
chicago: Yu, Fei, Michal Rybar, Caroline Uhler, and Stephen Fienberg. “Differentially-Private
Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases.”
In Lecture Notes in Computer Science (Including Subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in Bioinformatics), edited by Josep
Domingo Ferrer, 8744:170–84. Springer, 2014. https://doi.org/10.1007/978-3-319-11257-2_14.
ieee: F. Yu, M. Rybar, C. Uhler, and S. Fienberg, “Differentially-private logistic
regression for detecting multiple-SNP association in GWAS databases,” in Lecture
Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence
and Lecture Notes in Bioinformatics), Ibiza, Spain, 2014, vol. 8744, pp. 170–184.
ista: 'Yu F, Rybar M, Uhler C, Fienberg S. 2014. Differentially-private logistic
regression for detecting multiple-SNP association in GWAS databases. Lecture Notes
in Computer Science (including subseries Lecture Notes in Artificial Intelligence
and Lecture Notes in Bioinformatics). PSD: Privacy in Statistical Databases, LNCS,
vol. 8744, 170–184.'
mla: Yu, Fei, et al. “Differentially-Private Logistic Regression for Detecting Multiple-SNP
Association in GWAS Databases.” Lecture Notes in Computer Science (Including
Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics),
edited by Josep Domingo Ferrer, vol. 8744, Springer, 2014, pp. 170–84, doi:10.1007/978-3-319-11257-2_14.
short: F. Yu, M. Rybar, C. Uhler, S. Fienberg, in:, J. Domingo Ferrer (Ed.), Lecture
Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence
and Lecture Notes in Bioinformatics), Springer, 2014, pp. 170–184.
conference:
end_date: 2014-09-19
location: Ibiza, Spain
name: 'PSD: Privacy in Statistical Databases'
start_date: 2014-09-17
date_created: 2018-12-11T11:55:24Z
date_published: 2014-01-01T00:00:00Z
date_updated: 2021-01-12T06:54:57Z
day: '01'
department:
- _id: KrPi
- _id: CaUh
doi: 10.1007/978-3-319-11257-2_14
editor:
- first_name: Josep
full_name: Domingo Ferrer, Josep
last_name: Domingo Ferrer
external_id:
arxiv:
- '1407.8067'
intvolume: ' 8744'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1407.8067
month: '01'
oa: 1
oa_version: Submitted Version
page: 170 - 184
project:
- _id: 25636330-B435-11E9-9278-68D0E5697425
grant_number: 11-NSF-1070
name: ROOTS Genome-wide Analysis of Root Traits
publication: Lecture Notes in Computer Science (including subseries Lecture Notes
in Artificial Intelligence and Lecture Notes in Bioinformatics)
publication_status: published
publisher: Springer
publist_id: '5004'
quality_controlled: '1'
scopus_import: 1
status: public
title: Differentially-private logistic regression for detecting multiple-SNP association
in GWAS databases
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 8744
year: '2014'
...
---
_id: '2009'
abstract:
- lang: eng
text: Traditional statistical methods for confidentiality protection of statistical
databases do not scale well to deal with GWAS databases especially in terms of
guarantees regarding protection from linkage to external information. The more
recent concept of differential privacy, introduced by the cryptographic community,
is an approach which provides a rigorous definition of privacy with meaningful
privacy guarantees in the presence of arbitrary external information, although
the guarantees may come at a serious price in terms of data utility. Building
on such notions, we propose new methods to release aggregate GWAS data without
compromising an individual’s privacy. We present methods for releasing differentially
private minor allele frequencies, chi-square statistics and p-values. We compare
these approaches on simulated data and on a GWAS study of canine hair length involving
685 dogs. We also propose a privacy-preserving method for finding genome-wide
associations based on a differentially-private approach to penalized logistic
regression.
article_processing_charge: No
author:
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
- first_name: Aleksandra
full_name: Slavkovic, Aleksandra
last_name: Slavkovic
- first_name: Stephen
full_name: Fienberg, Stephen
last_name: Fienberg
citation:
ama: Uhler C, Slavkovic A, Fienberg S. Privacy-preserving data sharing for genome-wide
association studies. Journal of Privacy and Confidentiality . 2013;5(1):137-166.
doi:10.29012/jpc.v5i1.629
apa: Uhler, C., Slavkovic, A., & Fienberg, S. (2013). Privacy-preserving data
sharing for genome-wide association studies. Journal of Privacy and Confidentiality
. Carnegie Mellon University. https://doi.org/10.29012/jpc.v5i1.629
chicago: Uhler, Caroline, Aleksandra Slavkovic, and Stephen Fienberg. “Privacy-Preserving
Data Sharing for Genome-Wide Association Studies.” Journal of Privacy and Confidentiality
. Carnegie Mellon University, 2013. https://doi.org/10.29012/jpc.v5i1.629.
ieee: C. Uhler, A. Slavkovic, and S. Fienberg, “Privacy-preserving data sharing
for genome-wide association studies,” Journal of Privacy and Confidentiality
, vol. 5, no. 1. Carnegie Mellon University, pp. 137–166, 2013.
ista: Uhler C, Slavkovic A, Fienberg S. 2013. Privacy-preserving data sharing for
genome-wide association studies. Journal of Privacy and Confidentiality . 5(1),
137–166.
mla: Uhler, Caroline, et al. “Privacy-Preserving Data Sharing for Genome-Wide Association
Studies.” Journal of Privacy and Confidentiality , vol. 5, no. 1, Carnegie
Mellon University, 2013, pp. 137–66, doi:10.29012/jpc.v5i1.629.
short: C. Uhler, A. Slavkovic, S. Fienberg, Journal of Privacy and Confidentiality 5
(2013) 137–166.
date_created: 2018-12-11T11:55:11Z
date_published: 2013-08-01T00:00:00Z
date_updated: 2021-01-12T06:54:41Z
day: '01'
department:
- _id: CaUh
doi: 10.29012/jpc.v5i1.629
intvolume: ' 5'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://repository.cmu.edu/jpc/vol5/iss1/6
month: '08'
oa: 1
oa_version: Published Version
page: 137 - 166
publication: 'Journal of Privacy and Confidentiality '
publication_status: published
publisher: Carnegie Mellon University
publist_id: '5067'
quality_controlled: '1'
status: public
title: Privacy-preserving data sharing for genome-wide association studies
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 5
year: '2013'
...
---
_id: '2010'
abstract:
- lang: eng
text: Many algorithms for inferring causality rely heavily on the faithfulness assumption.
The main justification for imposing this assumption is that the set of unfaithful
distributions has Lebesgue measure zero, since it can be seen as a collection
of hypersurfaces in a hypercube. However, due to sampling error the faithfulness
condition alone is not sufficient for statistical estimation, and strong-faithfulness
has been proposed and assumed to achieve uniform or high-dimensional consistency.
In contrast to the plain faithfulness assumption, the set of distributions that
is not strong-faithful has nonzero Lebesgue measure and in fact, can be surprisingly
large as we show in this paper. We study the strong-faithfulness condition from
a geometric and combinatorial point of view and give upper and lower bounds on
the Lebesgue measure of strong-faithful distributions for various classes of directed
acyclic graphs. Our results imply fundamental limitations for the PC-algorithm
and potentially also for other algorithms based on partial correlation testing
in the Gaussian case.
arxiv: 1
author:
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
- first_name: Garvesh
full_name: Raskutti, Garvesh
last_name: Raskutti
- first_name: Peter
full_name: Bühlmann, Peter
last_name: Bühlmann
- first_name: Bin
full_name: Yu, Bin
last_name: Yu
citation:
ama: Uhler C, Raskutti G, Bühlmann P, Yu B. Geometry of the faithfulness assumption
in causal inference. The Annals of Statistics. 2013;41(2):436-463. doi:10.1214/12-AOS1080
apa: Uhler, C., Raskutti, G., Bühlmann, P., & Yu, B. (2013). Geometry of the
faithfulness assumption in causal inference. The Annals of Statistics.
Institute of Mathematical Statistics. https://doi.org/10.1214/12-AOS1080
chicago: Uhler, Caroline, Garvesh Raskutti, Peter Bühlmann, and Bin Yu. “Geometry
of the Faithfulness Assumption in Causal Inference.” The Annals of Statistics.
Institute of Mathematical Statistics, 2013. https://doi.org/10.1214/12-AOS1080.
ieee: C. Uhler, G. Raskutti, P. Bühlmann, and B. Yu, “Geometry of the faithfulness
assumption in causal inference,” The Annals of Statistics, vol. 41, no.
2. Institute of Mathematical Statistics, pp. 436–463, 2013.
ista: Uhler C, Raskutti G, Bühlmann P, Yu B. 2013. Geometry of the faithfulness
assumption in causal inference. The Annals of Statistics. 41(2), 436–463.
mla: Uhler, Caroline, et al. “Geometry of the Faithfulness Assumption in Causal
Inference.” The Annals of Statistics, vol. 41, no. 2, Institute of Mathematical
Statistics, 2013, pp. 436–63, doi:10.1214/12-AOS1080.
short: C. Uhler, G. Raskutti, P. Bühlmann, B. Yu, The Annals of Statistics 41 (2013)
436–463.
date_created: 2018-12-11T11:55:11Z
date_published: 2013-04-01T00:00:00Z
date_updated: 2021-01-12T06:54:42Z
day: '01'
department:
- _id: CaUh
doi: 10.1214/12-AOS1080
external_id:
arxiv:
- '1207.0547'
intvolume: ' 41'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: www.doi.org/10.1214/12-AOS1080
month: '04'
oa: 1
oa_version: Published Version
page: 436 - 463
publication: The Annals of Statistics
publication_status: published
publisher: Institute of Mathematical Statistics
publist_id: '5066'
quality_controlled: '1'
scopus_import: 1
status: public
title: Geometry of the faithfulness assumption in causal inference
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 41
year: '2013'
...
---
_id: '2280'
abstract:
- lang: eng
text: The problem of packing ellipsoids of different sizes and shapes into an ellipsoidal
container so as to minimize a measure of overlap between ellipsoids is considered.
A bilevel optimization formulation is given, together with an algorithm for the
general case and a simpler algorithm for the special case in which all ellipsoids
are in fact spheres. Convergence results are proved and computational experience
is described and illustrated. The motivating application-chromosome organization
in the human cell nucleus-is discussed briefly, and some illustrative results
are presented.
arxiv: 1
author:
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
- first_name: Stephen
full_name: Wright, Stephen
last_name: Wright
citation:
ama: Uhler C, Wright S. Packing ellipsoids with overlap. SIAM Review. 2013;55(4):671-706.
doi:10.1137/120872309
apa: Uhler, C., & Wright, S. (2013). Packing ellipsoids with overlap. SIAM
Review. Society for Industrial and Applied Mathematics . https://doi.org/10.1137/120872309
chicago: Uhler, Caroline, and Stephen Wright. “Packing Ellipsoids with Overlap.”
SIAM Review. Society for Industrial and Applied Mathematics , 2013. https://doi.org/10.1137/120872309.
ieee: C. Uhler and S. Wright, “Packing ellipsoids with overlap,” SIAM Review,
vol. 55, no. 4. Society for Industrial and Applied Mathematics , pp. 671–706,
2013.
ista: Uhler C, Wright S. 2013. Packing ellipsoids with overlap. SIAM Review. 55(4),
671–706.
mla: Uhler, Caroline, and Stephen Wright. “Packing Ellipsoids with Overlap.” SIAM
Review, vol. 55, no. 4, Society for Industrial and Applied Mathematics , 2013,
pp. 671–706, doi:10.1137/120872309.
short: C. Uhler, S. Wright, SIAM Review 55 (2013) 671–706.
date_created: 2018-12-11T11:56:44Z
date_published: 2013-11-07T00:00:00Z
date_updated: 2021-01-12T06:56:30Z
day: '07'
department:
- _id: CaUh
doi: 10.1137/120872309
external_id:
arxiv:
- '1204.0235'
intvolume: ' 55'
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1204.0235
month: '11'
oa: 1
oa_version: Preprint
page: 671 - 706
publication: SIAM Review
publication_status: published
publisher: 'Society for Industrial and Applied Mathematics '
publist_id: '4655'
quality_controlled: '1'
scopus_import: 1
status: public
title: Packing ellipsoids with overlap
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 55
year: '2013'
...
---
_id: '2959'
abstract:
- lang: eng
text: We study maximum likelihood estimation in Gaussian graphical models from a
geometric point of view. An algebraic elimination criterion allows us to find
exact lower bounds on the number of observations needed to ensure that the maximum
likelihood estimator (MLE) exists with probability one. This is applied to bipartite
graphs, grids and colored graphs. We also study the ML degree, and we present
the first instance of a graph for which the MLE exists with probability one, even
when the number of observations equals the treewidth.
acknowledgement: "I wish to thank Bernd Sturmfels for many helpful discus- sions and
Steffen Lauritzen for introducing me to the problem of the existence of the MLE
in Gaussian graphical models. I would also like to thank two referees who provided
helpful comments on the original version of this paper.\r\n"
author:
- first_name: Caroline
full_name: Uhler, Caroline
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
citation:
ama: Uhler C. Geometry of maximum likelihood estimation in Gaussian graphical models.
Annals of Statistics. 2012;40(1):238-261. doi:10.1214/11-AOS957
apa: Uhler, C. (2012). Geometry of maximum likelihood estimation in Gaussian graphical
models. Annals of Statistics. Institute of Mathematical Statistics. https://doi.org/10.1214/11-AOS957
chicago: Uhler, Caroline. “Geometry of Maximum Likelihood Estimation in Gaussian
Graphical Models.” Annals of Statistics. Institute of Mathematical Statistics,
2012. https://doi.org/10.1214/11-AOS957.
ieee: C. Uhler, “Geometry of maximum likelihood estimation in Gaussian graphical
models,” Annals of Statistics, vol. 40, no. 1. Institute of Mathematical
Statistics, pp. 238–261, 2012.
ista: Uhler C. 2012. Geometry of maximum likelihood estimation in Gaussian graphical
models. Annals of Statistics. 40(1), 238–261.
mla: Uhler, Caroline. “Geometry of Maximum Likelihood Estimation in Gaussian Graphical
Models.” Annals of Statistics, vol. 40, no. 1, Institute of Mathematical
Statistics, 2012, pp. 238–61, doi:10.1214/11-AOS957.
short: C. Uhler, Annals of Statistics 40 (2012) 238–261.
date_created: 2018-12-11T12:00:33Z
date_published: 2012-02-01T00:00:00Z
date_updated: 2021-01-12T07:40:04Z
day: '01'
department:
- _id: CaUh
doi: 10.1214/11-AOS957
intvolume: ' 40'
issue: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/1012.2643
month: '02'
oa: 1
oa_version: Preprint
page: 238 - 261
publication: Annals of Statistics
publication_status: published
publisher: Institute of Mathematical Statistics
publist_id: '3767'
quality_controlled: '1'
scopus_import: 1
status: public
title: Geometry of maximum likelihood estimation in Gaussian graphical models
type: journal_article
user_id: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87
volume: 40
year: '2012'
...
---
_id: '2960'
abstract:
- lang: eng
text: Traditional statistical methods for the confidentiality protection for statistical
databases do not scale well to deal with GWAS (genome-wide association studies)
databases and external information on them. The more recent concept of differential
privacy, introduced by the cryptographic community, is an approach which provides
a rigorous definition of privacy with meaningful privacy guarantees in the presence
of arbitrary external information. Building on such notions, we propose new methods
to release aggregate GWAS data without compromising an individual's privacy. We
present methods for releasing differentially private minor allele frequencies,
chi-square statistics and p-values. We compare these approaches on simulated data
and on a GWAS study of canine hair length involving 685 dogs. We also propose
a privacy-preserving method for finding genome-wide associations based on a differentially
private approach to penalized logistic regression.
author:
- first_name: Stephen
full_name: Fienberg, Stephen E
last_name: Fienberg
- first_name: Aleksandra
full_name: Slavkovic, Aleksandra
last_name: Slavkovic
- first_name: Caroline
full_name: Caroline Uhler
id: 49ADD78E-F248-11E8-B48F-1D18A9856A87
last_name: Uhler
orcid: 0000-0002-7008-0216
citation:
ama: 'Fienberg S, Slavkovic A, Uhler C. Privacy Preserving GWAS Data Sharing. In:
IEEE; 2011. doi:10.1109/ICDMW.2011.140'
apa: Fienberg, S., Slavkovic, A., & Uhler, C. (2011). Privacy Preserving GWAS
Data Sharing. Presented at the Proceedings of the 11th IEEE International Conference
on Data Mining, IEEE. https://doi.org/10.1109/ICDMW.2011.140
chicago: Fienberg, Stephen, Aleksandra Slavkovic, and Caroline Uhler. “Privacy Preserving
GWAS Data Sharing.” IEEE, 2011. https://doi.org/10.1109/ICDMW.2011.140.
ieee: S. Fienberg, A. Slavkovic, and C. Uhler, “Privacy Preserving GWAS Data Sharing,”
presented at the Proceedings of the 11th IEEE International Conference on Data
Mining, 2011.
ista: Fienberg S, Slavkovic A, Uhler C. 2011. Privacy Preserving GWAS Data Sharing.
Proceedings of the 11th IEEE International Conference on Data Mining.
mla: Fienberg, Stephen, et al. Privacy Preserving GWAS Data Sharing. IEEE,
2011, doi:10.1109/ICDMW.2011.140.
short: S. Fienberg, A. Slavkovic, C. Uhler, in:, IEEE, 2011.
conference:
name: Proceedings of the 11th IEEE International Conference on Data Mining
date_created: 2018-12-11T12:00:34Z
date_published: 2011-01-01T00:00:00Z
date_updated: 2021-01-12T07:40:05Z
day: '01'
doi: 10.1109/ICDMW.2011.140
extern: 1
month: '01'
publication_status: published
publisher: IEEE
publist_id: '3766'
quality_controlled: 0
status: public
title: Privacy Preserving GWAS Data Sharing
type: conference
year: '2011'
...