Venkat Chandrasekaran – Papers

Preprints

R. Murray, V. Chandrasekaran, and A. Wierman, Signomial and Polynomial Optimization via Relative Entropy and Partial Dualization, preprint. (software)

U. Candogan and V. Chandrasekaran, Convex Graph Invariant Relaxations for Graph Edit Distance, preprint.

Y. S. Soh and V. Chandrasekaran, Fitting Tractable Convex Sets to Support Function Evaluations, preprint.

A. Taeb, P. Shah, and V. Chandrasekaran, False Discovery and Its Control in Low-Rank Estimation, preprint.

R. Murray, V. Chandrasekaran, and A. Wierman, Newton Polytopes and Relative Entropy Optimization, preprint.

Journal Papers

Y. S. Soh and V. Chandrasekaran, Learning Semidefinite Regularizers, Foundations of Computational Mathematics, Vol. 19, No. 2, April 2019.

U. Candogan and V. Chandrasekaran, Finding Planted Subgraphs with Few Eigenvalues using the Schur-Horn Relaxation, SIAM Journal on Optimization, Vol. 28, No. 1, March 2018.

A. Taeb and V. Chandrasekaran, Interpreting Latent Variables in Factor Models via Convex Optimization, Mathematical Programming, Vol. 167, No. 1, January 2018.

A. Taeb, J. T. Reager, M. Turmon, and V. Chandrasekaran, A Statistical Graphical Model of the California Reservoir System, Water Resources Research, Vol. 53, No. 11, November 2017.

Y. S. Soh and V. Chandrasekaran, High-Dimensional Change-Point Estimation: Combining Filtering with Convex Optimization, Applied and Computational Harmonic Analysis, Vol. 43, No.1, July 2017.

V. Chandrasekaran and P. Shah, Relative Entropy Optimization and its Applications, Mathematical Programming, Vol. 161, No. 1, January 2017.

N. Matni and V. Chandrasekaran, Regularization for Design, IEEE Transactions on Automatic Control, Vol. 61, No. 12, December 2016.

V. Chandrasekaran and P. Shah, Relative Entropy Relaxations for Signomial Optimization, SIAM Journal on Optimization, Vol. 26, No. 2, May 2016. (software)

Q. Berthet and V. Chandrasekaran, Resource Allocation for Statistical Estimation, Proceedings of the IEEE, Vol. 104, No. 1, January 2016.

V. Chandrasekaran and M. I. Jordan, Computational and Statistical Tradeoffs via Convex Relaxation, Proceedings of the National Academy of Sciences, Vol. 110, No. 13, March 2013.

J. Saunderson, V. Chandrasekaran, P. A. Parrilo, and A. S. Willsky, Diagonal and Low-Rank Matrix Decompositions, Correlation Matrices, and Ellipsoid Fitting, SIAM Journal on Matrix Analysis and Applications, Vol. 33, No. 4, December 2012.

V. Chandrasekaran, B. Recht, P. A. Parrilo, and A. S. Willsky, The Convex Geometry of Linear Inverse Problems, Foundations of Computational Mathematics, Vol. 12, No. 6, December 2012.

V. Chandrasekaran, P. A. Parrilo, and A. S. Willsky, Convex Graph Invariants, SIAM Review, Vol. 54, No. 3, September 2012.

P. Shah and V. Chandrasekaran, Group Symmetry and Covariance Regularization, Electronic Journal of Statistics, Vol. 6, 2012. (erratum correcting notational mistake)

Y. Liu, V. Chandrasekaran, A. Anandkumar, and A. S. Willsky, Feedback Message Passing for Inference in Gaussian Graphical Models, IEEE Transactions on Signal Processing, Vol. 60, No. 8, August 2012.

V. Chandrasekaran, P. A. Parrilo, and A. S. Willsky, Latent Variable Graphical Model Selection via Convex Optimization, Annals of Statistics (with discussion), Vol. 40, No. 4, August 2012. (software)

V. Chandrasekaran, M. Chertkov, D. Gamarnik, D. Shah, and J. Shin, Counting Independent Sets using the Bethe Approximation, SIAM Journal on Discrete Mathematics, Vol. 25, No. 2, July 2011.

V. Chandrasekaran, S. Sanghavi, P. A. Parrilo, and A. S. Willsky, Rank-Sparsity Incoherence for Matrix Decomposition, SIAM Journal on Optimization, Vol. 21, No. 2, June 2011.

M. J. Choi, V. Chandrasekaran, and A. S. Willsky, Gaussian Multiresolution Models: Exploiting Sparse Markov and Covariance Structure, IEEE Transactions on Signal Processing, Vol. 58, No. 5, March 2010.

V. Chandrasekaran, M. B. Wakin, D. Baron, and R. G. Baraniuk, Representation and Compression of Multi-Dimensional Piecewise Functions Using Surflets, IEEE Transactions on Information Theory, Vol. 55, No. 1, January 2009.

M. J. Choi, V. Chandrasekaran, D. M. Malioutov, J. K. Johnson, and A. S. Willsky, Multiscale Stochastic Modeling for Tractable Inference and Data Assimilation, Computer Methods in Applied Mechanics and Engineering, Vol. 297, August 2008.

V. Chandrasekaran, J. K. Johnson, and A. S. Willsky, Estimation in Gaussian Graphical Models Using Tractable Subgraphs: A Walk-Sum Analysis, IEEE Transactions on Signal Processing, Vol. 56, No. 5, May 2008.

Ph.D. Thesis

V. Chandrasekaran, Convex Optimization Methods for Graphs and Statistical Modeling, Ph.D. thesis, Massachusetts Institute of Technology, April 2011.

Selected Conference Papers

M. Pilanci, L. El Ghaoui, and V. Chandrasekaran, Recovery of Sparse Probability Measures via Convex Programming, Neural Information Processing Systems Conference, Lake Tahoe, Nevada, December 2012.

J. Saunderson, V. Chandrasekaran, P. A. Parrilo, and A. S. Willsky, Tree-structured Statistical Modeling via Convex Optimization, IEEE Conference on Decision and Control, Orlando, Florida, December 2011.

V. Chandrasekaran, S. Sanghavi, P. A. Parrilo, and A. S. Willsky, Sparse and Low-Rank Matrix Decompositions, IFAC Symposium on System Identification, Saint-Malo, France, July 2009.

M. J. Choi, V. Chandrasekaran, and A. S. Willsky, Exploiting Sparse Markov and Covariance Structure in Multiresolution Models, International Conference on Machine Learning, Montreal, Canada, June 2009.

V. Chandrasekaran, N. Srebro, and P. Harsha, Complexity of Inference in Graphical Models, Conference on Uncertainty in Artificial Intelligence and Statistics, Helsinki, Finland, July 2008. (see extended tech report)

V. Chandrasekaran, J. K. Johnson, and A. S. Willsky, Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis, Neural Information Processing Systems Conference, Vancouver, Canada, December 2007.

V. Chandrasekaran, J. K. Johnson, and A. S. Willsky, Maximum Entropy Relaxation for Graphical Model Selection given Inconsistent Statistics, IEEE Statistical Signal Processing Workshop, Madison, Wisconsin, August 2007.

J. K. Johnson, V. Chandrasekaran, and A. S. Willsky, Learning Markov Structure by Maximum Entropy Relaxation, International Conference in Artificial Intelligence and Statistics, San Juan, Puerto Rico, March 2007.

V. Chandrasekaran, M. B. Wakin, D. Baron, and R. G. Baraniuk, Surflets: A Sparse Representation for Multidimensional Functions Containing Smooth Discontinuities, IEEE International Symposium on Information Theory, Chicago, Illinois, June 2004. (see extended tech report)

Technical Reports

J. Ziani, V. Chandrasekaran, and K. Ligett, Recovering Games from Perturbed Equilibrium Observations using Convex Optimization, 2016.

V. Chandrasekaran, N. Srebro, and P. Harsha, Complexity of Inference in Graphical Models, 2010.

V. Chandrasekaran, M. B. Wakin, D. Baron, and R. G. Baraniuk, Compressing Piecewise Smooth Multidimensional Functions Using Surflets: Rate-Distortion Analysis, 2004.