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ORCIDVenkat Chandrasekaran
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2020 – today
- 2023
[j24]James Saunderson
, Venkat Chandrasekaran:
Terracini convexity. Math. Program. 198(1): 399-441 (2023)- [j23]Eliza O'Reilly, Venkat Chandrasekaran:
Spectrahedral Regression. SIAM J. Optim. 33(2): 553-588 (2023) - [j22]Mareike Dressler, André Uschmajew, Venkat Chandrasekaran:
Kronecker Product Approximation of Operators in Spectral Norm via Alternating SDP. SIAM J. Matrix Anal. Appl. 44(4): 1693-1708 (2023) - [i15]Anshuman Pradhan, Kyra H. Adams, Venkat Chandrasekaran, Zhen Liu, John T. Reager, Andrew M. Stuart, Michael J. Turmon:
Modeling groundwater levels in California's Central Valley by hierarchical Gaussian process and neural network regression. CoRR abs/2310.14555 (2023) - 2022
- [j21]Utkan Onur Candogan, Venkat Chandrasekaran:
Convex graph invariant relaxations for graph edit distance. Math. Program. 191(2): 595-629 (2022) - [i14]Mareike Dressler, André Uschmajew, Venkat Chandrasekaran:
Kronecker Product Approximation of Operators in Spectral Norm via Alternating SDP. CoRR abs/2207.03186 (2022) - 2021
- [j20]Yong Sheng Soh, Venkat Chandrasekaran:
Fitting Tractable Convex Sets to Support Function Evaluations. Discret. Comput. Geom. 66(2): 510-551 (2021) - [j19]Riley Murray, Venkat Chandrasekaran, Adam Wierman:
Newton Polytopes and Relative Entropy Optimization. Found. Comput. Math. 21(6): 1703-1737 (2021) - [j18]Riley Murray, Venkat Chandrasekaran, Adam Wierman:
Signomial and polynomial optimization via relative entropy and partial dualization. Math. Program. Comput. 13(2): 257-295 (2021) - [j17]Riley Murray, Venkat Chandrasekaran, Adam Wierman:
Publisher Correction to: Signomial and polynomial optimization via relative entropy and partial dualization. Math. Program. Comput. 13(2): 297-299 (2021) - 2020
- [i13]Armeen Taeb, Parikshit Shah, Venkat Chandrasekaran:
Learning Exponential Family Graphical Models with Latent Variables using Regularized Conditional Likelihood. CoRR abs/2010.09386 (2020)
2010 – 2019
- 2019
- [j16]Yong Sheng Soh, Venkat Chandrasekaran:
Learning Semidefinite Regularizers. Found. Comput. Math. 19(2): 375-434 (2019) - [i12]Yong Sheng Soh, Venkat Chandrasekaran:
Fitting Tractable Convex Sets to Support Function Evaluations. CoRR abs/1903.04194 (2019) - [i11]Utkan Onur Candogan, Venkat Chandrasekaran:
Convex Graph Invariant Relaxations For Graph Edit Distance. CoRR abs/1904.08934 (2019) - 2018
- [j15]Armeen Taeb, Venkat Chandrasekaran:
Interpreting latent variables in factor models via convex optimization. Math. Program. 167(1): 129-154 (2018) - [j14]Utkan Onur Candogan, Venkat Chandrasekaran:
Finding Planted Subgraphs with Few Eigenvalues using the Schur-Horn Relaxation. SIAM J. Optim. 28(1): 735-759 (2018) - 2017
- [j13]Venkat Chandrasekaran, Parikshit Shah:
Relative entropy optimization and its applications. Math. Program. 161(1-2): 1-32 (2017) - [i10]Yong Sheng Soh, Venkat Chandrasekaran:
A Matrix Factorization Approach for Learning Semidefinite-Representable Regularizers. CoRR abs/1701.01207 (2017) - 2016
- [j12]Quentin Berthet, Venkat Chandrasekaran:
Resource Allocation for Statistical Estimation. Proc. IEEE 104(1): 111-125 (2016) - [j11]Venkat Chandrasekaran, Parikshit Shah:
Relative Entropy Relaxations for Signomial Optimization. SIAM J. Optim. 26(2): 1147-1173 (2016) - [j10]Nikolai Matni, Venkat Chandrasekaran:
Regularization for Design. IEEE Trans. Autom. Control. 61(12): 3991-4006 (2016) - [i9]Juba Ziani, Venkat Chandrasekaran, Katrina Ligett:
Recovering Games from Perturbed Equilibrium Observations Using Convex Optimization. CoRR abs/1603.01318 (2016) - [i8]Utkan Onur Candogan, Venkat Chandrasekaran:
Finding Planted Subgraphs with Few Eigenvalues using the Schur-Horn Relaxation. CoRR abs/1605.04008 (2016) - 2015
- [c18]Yong Sheng Soh, Venkat Chandrasekaran:
High-dimensional change-point estimation: Combining filtering with convex optimization. ISIT 2015: 151-155 - 2014
- [c17]Nikolai Matni, Venkat Chandrasekaran:
Regularization for design. CDC 2014: 1111-1118 - [c16]Venkat Chandrasekaran, Parikshit Shah:
Conic geometric programming. CISS 2014: 1-4 - [i7]Nikolai Matni, Venkat Chandrasekaran:
Regularization for Design. CoRR abs/1404.1972 (2014) - [i6]Yong Sheng Soh, Venkat Chandrasekaran:
High-Dimensional Change-Point Estimation: Combining Filtering with Convex Optimization. CoRR abs/1412.3731 (2014) - 2012
- [j9]Venkat Chandrasekaran, Benjamin Recht, Pablo A. Parrilo, Alan S. Willsky:
The Convex Geometry of Linear Inverse Problems. Found. Comput. Math. 12(6): 805-849 (2012) - [j8]James Saunderson, Venkat Chandrasekaran, Pablo A. Parrilo, Alan S. Willsky:
Diagonal and Low-Rank Matrix Decompositions, Correlation Matrices, and Ellipsoid Fitting. SIAM J. Matrix Anal. Appl. 33(4): 1395-1416 (2012) - [j7]Venkat Chandrasekaran, Pablo A. Parrilo, Alan S. Willsky:
Convex Graph Invariants. SIAM Rev. 54(3): 513-541 (2012) - [j6]Ying Liu, Venkat Chandrasekaran, Animashree Anandkumar, Alan S. Willsky:
Feedback Message Passing for Inference in Gaussian Graphical Models. IEEE Trans. Signal Process. 60(8): 4135-4150 (2012) - [c15]Venkat Chandrasekaran, Pablo A. Parrilo, Alan S. Willsky:
Convex graph invariants. CISS 2012: 1-6 - [c14]Parikshit Shah, Venkat Chandrasekaran:
Group symmetry and covariance regularization. CISS 2012: 1-6 - [c13]Mert Pilanci, Laurent El Ghaoui, Venkat Chandrasekaran:
Recovery of Sparse Probability Measures via Convex Programming. NIPS 2012: 2429-2437 - [i5]Venkat Chandrasekaran, Nathan Srebro, Prahladh Harsha:
Complexity of Inference in Graphical Models. CoRR abs/1206.3240 (2012) - [i4]Venkat Chandrasekaran, Pablo A. Parrilo, Alan S. Willsky:
Rejoinder: Latent variable graphical model selection via convex optimization. CoRR abs/1211.0835 (2012) - [i3]Venkat Chandrasekaran, Michael I. Jordan:
Computational and Statistical Tradeoffs via Convex Relaxation. CoRR abs/1211.1073 (2012) - 2011
- [b1]Venkat Chandrasekaran:
Convex optimization methods for graphs and statistical modeling. Massachusetts Institute of Technology, Cambridge, MA, USA, 2011 - [j5]Venkat Chandrasekaran, Misha Chertkov, David Gamarnik, Devavrat Shah, Jinwoo Shin:
Counting Independent Sets Using the Bethe Approximation. SIAM J. Discret. Math. 25(2): 1012-1034 (2011) - [j4]Venkat Chandrasekaran, Sujay Sanghavi, Pablo A. Parrilo, Alan S. Willsky:
Rank-Sparsity Incoherence for Matrix Decomposition. SIAM J. Optim. 21(2): 572-596 (2011) - [c12]Parikshit Shah, Venkat Chandrasekaran:
Iterative projections for signal identification on manifolds: Global recovery guarantees. Allerton 2011: 760-767 - [c11]James Saunderson, Venkat Chandrasekaran, Pablo A. Parrilo, Alan S. Willsky:
Tree-structured statistical modeling via convex optimization. CDC/ECC 2011: 2883-2888 - [i2]Ying Liu, Venkat Chandrasekaran, Animashree Anandkumar, Alan S. Willsky:
Feedback Message Passing for Inference in Gaussian Graphical Models. CoRR abs/1105.1853 (2011) - 2010
- [j3]Myung Jin Choi, Venkat Chandrasekaran, Alan S. Willsky:
Gaussian multiresolution models: exploiting sparse Markov and covariance structure. IEEE Trans. Signal Process. 58(3): 1012-1024 (2010) - [c10]Venkat Chandrasekaran, Benjamin Recht, Pablo A. Parrilo, Alan S. Willsky:
The Convex algebraic geometry of linear inverse problems. Allerton 2010: 699-703 - [c9]Venkat Chandrasekaran, Pablo A. Parrilo, Alan S. Willsky:
Latent variable graphical model selection via convex optimization. Allerton 2010: 1610-1613 - [c8]Ying Liu, Venkat Chandrasekaran, Animashree Anandkumar, Alan S. Willsky:
Feedback message passing for inference in gaussian graphical models. ISIT 2010: 1683-1687 - [i1]Venkat Chandrasekaran, Pablo A. Parrilo, Alan S. Willsky:
Convex Graph Invariants. CoRR abs/1012.0623 (2010)
2000 – 2009
- 2009
- [j2]Venkat Chandrasekaran, Michael B. Wakin, Dror Baron, Richard G. Baraniuk:
Representation and Compression of Multidimensional Piecewise Functions Using Surflets. IEEE Trans. Inf. Theory 55(1): 374-400 (2009) - [c7]Venkat Chandrasekaran, Sujay Sanghavi, Pablo A. Parrilo, Alan S. Willsky:
Sparse and low-rank matrix decompositions. Allerton 2009: 962-967 - [c6]Myung Jin Choi, Venkat Chandrasekaran, Alan S. Willsky:
Exploiting sparse Markov and covariance structure in multiresolution models. ICML 2009: 177-184 - 2008
- [j1]Venkat Chandrasekaran, Jason K. Johnson, Alan S. Willsky:
Estimation in Gaussian Graphical Models Using Tractable Subgraphs: A Walk-Sum Analysis. IEEE Trans. Signal Process. 56(5): 1916-1930 (2008) - [c5]Myung Jin Choi, Venkat Chandrasekaran, Alan S. Willsky:
Maximum entropy relaxation for multiscale graphical model selection. ICASSP 2008: 1889-1892 - [c4]Venkat Chandrasekaran, Nathan Srebro, Prahladh Harsha:
Complexity of Inference in Graphical Models. UAI 2008: 70-78 - 2007
- [c3]Venkat Chandrasekaran, Jason K. Johnson, Alan S. Willsky:
Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis. NIPS 2007: 249-256 - [c2]Jason K. Johnson, Venkat Chandrasekaran, Alan S. Willsky:
Learning Markov Structure by Maximum Entropy Relaxation. AISTATS 2007: 203-210 - 2004
- [c1]Venkat Chandrasekaran, Michael B. Wakin, Dror Baron, Richard G. Baraniuk:
Surflets: a sparse representation for multidimensional functions containing smooth discontinuities. ISIT 2004: 563
Coauthor Index
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