Convexity, Optimization and Data Science
Head:
Venkat Chandrasekaran
Phone:
+49 (0) 341 - 9959 - 50
Fax:
+49 (0) 341 - 9959 - 658
Address:
Inselstr. 22
04103 Leipzig
Publications Group Venkat Chandrasekaran
Preprints
Eliza O'Reilly and Venkat Chandrasekaran:
Spectrahedral regression
Repository Open AccessYong Sheng Soh and Venkat Chandrasekaran:
A matrix factorization approach for learning semidefinite-representable regularizers
Repository Open AccessJames Francis Saunderson and Venkat Chandrasekaran:
Terracini convexity
Repository Open AccessArmeen Taeb ; Parikshit Shah and Venkat Chandrasekaran:
Learning exponential family graphical models with latent variables using regularized
conditional likelihood
Repository Open AccessJuba Ziani ; Venkat Chandrasekaran and Katrina Ligett:
Efficiently characterizing games consistent with perturbed equilibrium observations
Repository Open AccessArmeen Taeb and Venkat Chandrasekaran:
Sufficient dimension reduction and modeling responses conditioned on covariates :
an integrated approach via convex optimization
Repository Open AccessVenkat Chandrasekaran ; Michael B. Wakin ; Dror Baron and Richard G. Baraniuk:
Compressing piecewise smooth multidimensional functions using surflets : rate-distortion
analysis
Repository Open AccessPublications
Utkan Onur Candogan ; Yong Sheng Soh and Venkat Chandrasekaran:
A note on convex relaxations for the inverse eigenvalue problem
In: Optimization letters,
15 (2021), p. 2757-2772
Bibtex DOI: 10.1007/s11590-021-01708-1 ARXIV: https://arxiv.org/abs/1911.02225Riley Murray ; Venkat Chandrasekaran and Adam Wierman:
Signomial and polynomial optimization via relative entropy and partial dualization
In: Mathematical programming computation,
13 (2021) 2, p. 257-295
Bibtex DOI: 10.1007/s12532-020-00193-4 ARXIV: https://arxiv.org/abs/1907.00814Riley Murray ; Venkat Chandrasekaran and Adam Wierman:
Newton polytopes and relative entropy optimization
In: Foundations of computational mathematics,
21 (2021) 6, p. 1703-1737
Bibtex DOI: 10.1007/s10208-021-09497-w ARXIV: https://arxiv.org/abs/1810.01614Riley Murray ; Venkat Chandrasekaran and Adam Wierman:
Publisher correction to: 'Signomial and polynomial optimization via relative entropy
and partial dualization' [In: Mathematical programming computation 13(2021)2, 257-295]
In: Mathematical programming computation,
13 (2021) 2, p. 297-299
Bibtex DOI: 10.1007/s12532-021-00201-1 ARXIV: https://arxiv.org/abs/1907.00814Yong Sheng Soh and Venkat Chandrasekaran:
Fitting tractable convex sets to support function evaluations
In: Discrete and computational geometry,
66 (2021) 2, p. 510-551
Bibtex DOI: 10.1007/s00454-020-00258-0 ARXIV: https://arxiv.org/abs/1903.04194Utkan Onur Candogan and Venkat Chandrasekaran:
Convex graph invariant relaxations for graph edit distance
In: Mathematical programming,
Vol. not yet known, pp. not yet known
Bibtex DOI: 10.1007/s10107-020-01564-4 ARXIV: https://arxiv.org/abs/1904.08934Armeen Taeb ; Parikshit Shah and Venkat Chandrasekaran:
False discovery and its control in low rank estimation
In: Journal of the Royal Statistical Society / B,
82 (2020) 4, p. 997-1027
Bibtex DOI: 10.1111/rssb.12387 ARXIV: https://arxiv.org/abs/1810.08595Yong Sheng Soh and Venkat Chandrasekaran:
Learning semidefinite regularizers
In: Foundations of computational mathematics,
19 (2019) 2, p. 375-434
Bibtex DOI: 10.1007/s10208-018-9386-z ARXIV: https://arxiv.org/abs/1701.01207Utkan Onur Candogan and Venkat Chandrasekaran:
Finding planted subgraphs with few eigenvalues using the Schur-Horn relaxation
In: SIAM journal on optimization,
28 (2018) 1, p. 735-759
Bibtex DOI: 10.1137/16M1075144 ARXIV: https://arxiv.org/abs/1605.04008Armeen Taeb and Venkat Chandrasekaran:
Interpreting latent variables in factor models via convex optimization
In: Mathematical programming,
167 (2018) 1, p. 129-154
Bibtex DOI: 10.1007/s10107-017-1187-7 ARXIV: https://arxiv.org/abs/1601.00389Venkat Chandrasekaran and Parikshit Shah:
Relative entropy optimization and its applications
In: Mathematical programming,
161 (2017) 1/2, p. 1-32
Bibtex DOI: 10.1007/s10107-016-0998-2Yong Sheng Soh and Venkat Chandrasekaran:
High-dimensional change-point estimation : combining filtering with convex optimization
In: Applied and computational harmonic analysis,
43 (2017) 1, p. 122-147
Bibtex DOI: 10.1016/j.acha.2015.11.003 ARXIV: https://arxiv.org/abs/1412.3731Armeen Taeb ; John T. Reager ; Michael Turmon and Venkat Chandrasekaran:
A statistical graphical model of the California reservoir system
In: Water resources research,
53 (2017) 11, p. 9721-9739
Bibtex DOI: 10.1002/2017WR020412 ARXIV: https://arxiv.org/abs/1606.08098Quentin Berthet and Venkat Chandrasekaran:
Resource allocation for statistical estimation
In: Proceedings of the IEEE,
104 (2016) 1, p. 111-125
Bibtex DOI: 10.1109/JPROC.2015.2494098 ARXIV: https://arxiv.org/abs/1412.6613Venkat Chandrasekaran and Parikshit Shah:
Relative entropy relaxations for signomial optimization
In: SIAM journal on optimization,
26 (2016) 2, p. 1147-1173
Bibtex DOI: 10.1137/140988978 ARXIV: https://arxiv.org/abs/1409.7640Nikolai Matni and Venkat Chandrasekaran:
Regularization for design
In: IEEE transactions on automatic control,
61 (2016) 12, p. 3991-4006
Bibtex DOI: 10.1109/TAC.2016.2517570 ARXIV: https://arxiv.org/abs/1404.1972Yong Sheng Soh and Venkat Chandrasekaran:
High-dimensional change-point estimation : combining filtering with convex optimization
In: IEEE international symposium on information theory proceedings (ISIT) 2015 : Hong
Kong Convention and Exhibition Centre, Hong Kong, 14-19 June 2015
Bibtex DOI: 10.1109/ISIT.2015.7282435 ARXIV: https://arxiv.org/abs/1412.3731Piscataway, NY : IEEE, 2015. - P. 151-155
Venkat Chandrasekaran and Parikshit Shah:
Conic geometric programming
In: 48th annual conference on information sciences and systems (CISS) 19-21 March 2014,
Princeton, NJ
Bibtex DOI: 10.1109/CISS.2014.6814151 ARXIV: https://arxiv.org/abs/1310.0899Piscataway, NJ : IEEE, 2014. - P. 1-4
Nikolai Matni and Venkat Chandrasekaran:
Regularization for design
In: 53rd IEEE annual conference on decision and control (CDC), 2014 15-17 Dec. 2014, Los
Angeles, California, USA
Bibtex DOI: 10.1109/CDC.2014.7039530 ARXIV: https://arxiv.org/abs/1404.1972Piscataway, NY : IEEE, 2014. - P. 1111-1118
Venkat Chandrasekaran and Michael I. Jordan:
Computational and statistical tradeoffs via convex relaxation
In: Proceedings of the National Academy of Sciences of the United States of America,
110 (2013) 13, p. E1181-E1190
Bibtex DOI: 10.1073/pnas.1302293110 ARXIV: https://arxiv.org/abs/1211.1073Parikshit Shah and Venkat Chandrasekaran:
Erratum: 'Group symmetry and covariance regularization' [In: Electron. J. Statist.
6(2012),1600-1640]
In: Electronic journal of statistics,
7 (2013), p. 3057-3058
Bibtex DOI: 10.1214/13-EJS871Venkat Chandrasekaran ; Pablo A. Parrilo and Alan S. Willsky:
Latent variable graphical model selection via convex optimization
In: The annals of statistics,
40 (2012) 4, p. 1935-1967
Bibtex DOI: 10.1214/11-AOS949 ARXIV: https://arxiv.org/abs/1008.1290Venkat Chandrasekaran ; Pablo A. Parrilo and Alan S. Willsky:
Convex graph invariants
In: SIAM review,
54 (2012) 3, p. 513-541
Bibtex DOI: 10.1137/100816900 ARXIV: https://arxiv.org/abs/1012.0623Venkat Chandrasekaran ; Pablo A. Parrilo and Alan S. Willsky:
Convex graph invariants
In: 46th annual conference on information sciences and systems (CISS) : 21-23 March 2012,
Princeton University, Princeton, NJ, USA
Bibtex DOI: 10.1109/CISS.2012.6310764 ARXIV: https://arxiv.org/abs/1012.0623Piscataway, NJ : IEEE, 2012. - P. 1-6
Venkat Chandrasekaran ; Benjamin Recht ; Pablo A. Parrilo and Alan S. Willsky:
The convex geometry of linear inverse problems
In: Foundations of computational mathematics,
12 (2012) 6, p. 805-849
Bibtex DOI: 10.1007/s10208-012-9135-7 ARXIV: https://arxiv.org/abs/1012.0621Ying Liu ; Venkat Chandrasekaran ; Animashree Anandkumar and Alan S. Willsky:
Feedback message passing for inference in Gaussian graphical models
In: IEEE transactions on signal processing,
60 (2012) 8, p. 4135-4150
Bibtex DOI: 10.1109/TSP.2012.2195656 ARXIV: https://arxiv.org/abs/1105.1853Pablo A. Parrilo ; Alan S. Willsky and Venkat Chandrasekaran:
Rejoinder: Latent variable graphical model selection via convex optimization
In: The annals of statistics,
40 (2012) 4, p. 2005-2013
Bibtex DOI: 10.1214/12-AOS1020 ARXIV: https://arxiv.org/abs/1211.0835Mert Pilanci ; Laurent el Ghaoui and Venkat Chandrasekaran:
Recovery of sparse probability measures via convex programming
In: Advances in neural information processing systems 25 : NIPS 2012 ; 26th annual conference
on neural information processing systems 2012, held December 3-6, 2012, Lake Tahoe,
Nevada, United States / Peter L. Bartlett (ed.)
Bibtex LINK: https://openreview.net/forum?id=HJNTNwWO-SLa Jolla, CA : Neural Information Processing Systems, 2012. - P. 2429-2437
James Francis Saunderson ; Venkat Chandrasekaran ; Pablo A. Parrilo and Alan S. Willsky:
Diagonal and low-rank matrix decompositions, correlation matrices, and ellipsoid fitting
In: SIAM journal on matrix analysis and applications,
33 (2012) 4, p. 1395-1416
Bibtex DOI: 10.1137/120872516 ARXIV: https://arxiv.org/abs/1204.1220James Francis Saunderson ; Venkat Chandrasekaran ; Pablo A. Parrilo and Alan S. Willsky:
Tree-structured statistical modeling via convex optimization
In: 50th IEEE annual conference on decision and control (CDC), 2011
Bibtex DOI: 10.1109/CDC.2011.6161011Piscataway, NY : IEEE, 2012. - P. 2883-2888
Parikshit Shah and Venkat Chandrasekaran:
Group symmetry and covariance regularization
In: Electronic journal of statistics,
6 (2012), p. 1600-1640
Bibtex DOI: 10.1214/12-EJS723 ARXIV: https://arxiv.org/abs/1111.7061Parikshit Shah and Venkat Chandrasekaran:
Group symmetry and covariance regularization
In: 46th annual conference on information sciences and systems (CISS) : 21-23 March 2012,
Princeton University, Princeton, NJ, USA
Bibtex DOI: 10.1109/CISS.2012.6310765 ARXIV: https://arxiv.org/abs/1111.7061Piscataway, NJ : IEEE, 2012. - P. 1-6
Venkat Chandrasekaran ; Misha Chertkov ; David Gamarnik ; Devavrat Shah and Jinwoo Shin:
Counting independent sets using the Bethe approximation
In: SIAM journal on discrete mathematics,
25 (2011) 2, p. 1012-1034
Bibtex DOI: 10.1137/090767145 LINK: https://www.researchgate.net/publication/220533068Venkat Chandrasekaran ; Sujay Sanghavi ; Pablo A. Parrilo and Alan S. Willsky:
Rank-sparsity incoherence for matrix decomposition
In: SIAM journal on optimization,
21 (2011) 2, p. 572-596
Bibtex DOI: 10.1137/090761793 ARXIV: https://arxiv.org/abs/0906.2220Parikshit Shah and Venkat Chandrasekaran:
Iterative projections for signal identification on manifolds : Global recovery guarantees
In: 2011 49th Annual Allerton conference on communication, control, and computing : Monticello,
Illinois, USA, 28-30 September 2011
Bibtex DOI: 10.1109/Allerton.2011.6120244Piscataway, N.J. : IEEE, 2011. - P. 760-767
Myung Jin Choi ; Venkat Chandrasekaran and Alan S. Willsky:
Gaussian multiresolution models: exploiting sparse Markov and covariance structure
In: IEEE transactions on signal processing,
58 (2010) 3, p. 1012-1024
Bibtex DOI: 10.1109/TSP.2009.2036042 LINK: https://resolver.caltech.edu/CaltechAUTHORS:20121008-094406124Ying Liu ; Venkat Chandrasekaran ; Animashree Anandkumar and Alan S. Willsky:
Feedback message passing for inference in Gaussian graphical models
In: IEEE international symposium on information theory proceedings (ISIT) : 2010 Austin,
Texas, USA, 13-18 June 2010
Bibtex DOI: 10.1109/ISIT.2010.5513321 ARXIV: https://arxiv.org/abs/1105.1853Piscataway, NY : IEEE, 2010. - P. 1683-1687
Venkat Chandrasekaran ; Michael B. Wakin ; Dror Baron and Richard G. Baraniuk:
Representation and compression of multidimensional piecewise functions using surflets
In: IEEE transactions on information theory,
55 (2009) 1, p. 374-400
Bibtex DOI: 10.1109/TIT.2008.2008153Myung Jin Choi ; Venkat Chandrasekaran and Alan S. Willsky:
Exploiting sparse Markov and covariance structure in multiresolution models
In: ICML 2009 : Proceedings of the 26th international conference on machine learning ;
14-18 June 2009
Bibtex DOI: 10.1145/1553374.1553397New York : Association for Computing Machinery, 2009. - P. 177-184
Venkat Chandrasekaran ; Jason K. Johnson and Alan S. Willsky:
Estimation in Gaussian graphical models using tractable subgraphs : a walk-sum analysis
In: IEEE transactions on signal processing,
56 (2008) 5, p. 1916-1930
Bibtex DOI: 10.1109/TSP.2007.912280 LINK: https://resolver.caltech.edu/CaltechAUTHORS:20121005-083046659Venkat Chandrasekaran ; Jason K. Johnson and Alan S. Willsky:
Adaptive embedded subgraph algorithms using walk-sum analysis
In: Advances in neural information processing systems 20 : NIPS 2007 ; 21st annual conference
on neural information processing systems 2007, Vancouver, British Columbia, Canada,
December 3-6, 2007
Bibtex LINK: https://openreview.net/forum?id=rJVSoObuWSLa Jolla, CA : Neural Information Processing Systems, 2008. - P. 249-256
Venkat Chandrasekaran ; Nathan Srebro and Prahladh Harsha:
Complexity of inference in graphical models
In: UAI 2008 : proceedings of the 24th conference in uncertainty in artificial intelligence,
Helsinki, Finland, July 9-12, 2008 / David McAllester (ed.)
Bibtex ARXIV: https://arxiv.org/abs/1206.3240Corvallis, Or. : AUAI Press, 2008. - P. 70-78
Myung Jin Choi ; Venkat Chandrasekaran ; Dimitry M. Malioutov ; Jason K. Johnson and Alan S. Willsky:
Multiscale stochastic modeling for tractable inference and data assimilation
In: Computer methods in applied mechanics and engineering,
197 (2008) 43-44, p. 3492-3515
Bibtex DOI: 10.1016/j.cma.2007.12.021Myung Jin Choi ; Venkat Chandrasekaran and Alan S. Willsky:
Maximum entropy relaxation for multiscale graphical model selection
In: 2008 IEEE international conference on acoustics, speech, and signal processing (ICASSP)
: March 30 - April 4, 2008
Bibtex DOI: 10.1109/ICASSP.2008.4518003Piscataway, NY : IEEE, 2008. - P. 1889-1892
Jason K. Johnson ; Venkat Chandrasekaran and Alan S. Willsky:
Learning Markov structure by maximum entropy relaxation
In: Proceedings of the eleventh international conference on artificial intelligence and
statistics : 21-24 March 2007, San Juan, Puerto Rico
Long Beach, California : PMLR, 2007. - P. 203-210
(Proceedings of machine learning research ; 2)
Bibtex LINK: http://proceedings.mlr.press/v2/johnson07a.htmlVenkat Chandrasekaran ; Michael B. Wakin ; Dror Baron and Richard G. Baraniuk:
Surflets : a sparse representation for multidimensional functions containing smooth
discontinuities
In: IEEE international symposium on information theory proceedings (ISIT) : 2004 Chicago,
Illinois, USA, June 27-July 2, 2004
Bibtex DOI: 10.1109/ISIT.2004.1365602 LINK: https://resolver.caltech.edu/CaltechAUTHORS:20121011-131626671Piscataway, NY : IEEE, 2004. - P. 563-563
Thesis
Venkat Chandrasekaran:
Convex optimization methods for graphs and statistical Modeling
Dissertation, Massachusetts Institute of Technology, 2011
Bibtex LINK: https://resolver.caltech.edu/CaltechAUTHORS:20121008-130644748