Venkat Chandrasekaran: List of Publications

Preprints

E. Levin and V. Chandrasekaran, Free Descriptions of Convex Sets.
M. Diaz and V. Chandrasekaran, Controlling the False Discovery Rate in Subspace Selection.
A. Taeb, P. Shah, and V. Chandrasekaran, Learning Exponential Family Graphical Models with Latent Variables using Regularized Conditional Likelihood.

Journal papers

O. Leong, E. O'Reilly, Y. Soh, and V. Chandrasekaran, Optimal Regularization for a Data Source, Foundations of Computational Mathematics, accepted.
A. Taeb, P. Bühlmann, and V. Chandrasekaran, Model Selection over Partially Ordered Sets, Proceedings of the National Academy of Sciences, 2024.
E. O'Reilly and V. Chandrasekaran, Spectrahedral Regression, SIAM Journal on Optimization, 2023.
J. Saunderson and V. Chandrasekaran, Terracini Convexity, Mathematical Programming, 2023.
U. Candogan and V. Chandrasekaran, Convex Graph Invariant Relaxations for Graph Edit Distance, Mathematical Programming, 2022.
R. Murray, V. Chandrasekaran, and A. Wierman, Newton Polytopes and Relative Entropy Optimization, Foundations of Computational Mathematics, 2021.
U. Candogan, Y. Soh, and V. Chandrasekaran, A Note on Convex Relaxations for the Inverse Eigenvalue Problem, Optimization Letters, 2021.
Y. Soh and V. Chandrasekaran, Fitting Tractable Convex Sets to Support Function Evaluations, Discrete and Computational Geometry, 2021.
R. Murray, V. Chandrasekaran, and A. Wierman, Signomial and Polynomial Optimization via Relative Entropy and Partial Dualization, Mathematical Programming Computation, 2021.
A. Taeb, P. Shah, and V. Chandrasekaran, False Discovery and Its Control in Low-Rank Estimation, Journal of the Royal Statistical Society (Series B), 2020.
Y. Soh and V. Chandrasekaran, Learning Semidefinite Regularizers, Foundations of Computational Mathematics, 2019.
U. Candogan and V. Chandrasekaran, Finding Planted Subgraphs with Few Eigenvalues using the Schur-Horn Relaxation, SIAM Journal on Optimization, 2018.
A. Taeb and V. Chandrasekaran, Interpreting Latent Variables in Factor Models via Convex Optimization, Mathematical Programming, 2018.
A. Taeb, J. Reager, M. Turmon, and V. Chandrasekaran, A Statistical Graphical Model of the California Reservoir System, Water Resources Research, 2017.
Y. Soh and V. Chandrasekaran, High-Dimensional Change-Point Estimation: Combining Filtering with Convex Optimization, Applied and Computational Harmonic Analysis, 2017.
V. Chandrasekaran and P. Shah, Relative Entropy Optimization and its Applications, Mathematical Programming, 2017.
N. Matni and V. Chandrasekaran, Regularization for Design, IEEE Transactions on Automatic Control, 2016.
V. Chandrasekaran and P. Shah, Relative Entropy Relaxations for Signomial Optimization, SIAM Journal on Optimization, 2016.
V. Chandrasekaran and M. Jordan, Computational and Statistical Tradeoffs via Convex Relaxations, Proceedings of the National Academy of Sciences, 2013.
J. Saunderson, V. Chandrasekaran, P. Parrilo, and A. Willsky, Diagonal and Low-Rank Matrix Decompositions, Correlation Matrices, and Ellipsoid Fitting, SIAM Journal on Matrix Analysis and Applications, 2012.
V. Chandrasekaran, B. Recht, P. Parrilo, and A. Willsky, The Convex Geometry of Linear Inverse Problems, Foundations of Computational Mathematics, 2012.
V. Chandrasekaran, P. Parrilo, and A. Willsky, Convex Graph Invariants, SIAM Review, 2012.
P. Shah and V. Chandrasekaran, Group Symmetry and Covariance Regularization, Electronic Journal of Statistics, 2012. (erratum correcting notational mistake)
Y. Liu, V. Chandrasekaran, A. Anandkumar, and A. Willsky, Feedback Message Passing for Inference in Gaussian Graphical Models, IEEE Transactions on Signal Processing, 2012.
V. Chandrasekaran, P. Parrilo, and A. Willsky, Latent Variable Graphical Model Selection via Convex Optimization, Annals of Statistics (with discussion), 2012.
V. Chandrasekaran, M. Chertkov, D. Gamarnik, D. Shah, and J. Shin, Counting Independent Sets using the Bethe Approximation, SIAM Journal on Discrete Mathematics, 2011.
V. Chandrasekaran, S. Sanghavi, P. Parrilo, and A. Willsky, Rank Sparsity Incoherence for Matrix Decomposition, SIAM Journal on Optimization, 2011.
M. Choi, V. Chandrasekaran, and A. Willsky, Gaussian Multiresolution Models: Exploiting Sparse Markov and Covariance Structure, IEEE Transactions on Signal Processing, 2010.
V. Chandrasekaran, M. Wakin, D. Baron, and R. Baraniuk, Representation and Compression of Multi-Dimensional Piecewise Functions using Surflets, IEEE Transactions on Information Theory, 2009.
M. Choi, V. Chandrasekaran, D. Malioutov, J. Johnson, and A. Willsky, Multiscale Stochastic Modeling for Tractable Inference and Data Assimilation, Computer Methods in Applied Mechanics and Engineering, 2008.
V. Chandrasekaran, J. Johnson, and A. Willsky, Estimation in Gaussian Graphical Models using Tractable Subgraphs: A Walk-Sum Analysis, IEEE Transactions on Signal Processing, 2008.

Ph.D. thesis

V. Chandrasekaran, Convex Optimization Methods for Graphs and Statistical Modeling, Ph.D. thesis, Massachusetts Institute of Technology, 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, 2012.
J. Saunderson, V. Chandrasekaran, P. Parrilo, and A. Willsky, Tree-Structured Statistical Modeling via Convex Optimization, IEEE Conference on Decision and Control, Orlando, Florida, 2011.
V. Chandrasekaran, S. Sanghavi, P. Parrilo, and A. Willsky, Sparse and Low-Rank Matrix Decompositions, IFAC Symposium on System Identification, Saint-Malo, France, 2009.
M. Choi, V. Chandrasekaran, and A. 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, 2008.
V. Chandrasekaran, J. Johnson, and A. Willsky, Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis, Neural Information Processing Systems Conference, Vancouver, Canada, 2007.
V. Chandrasekaran, J. Johnson, and A. Willsky, Maximum Entropy Relaxation for Graphical Model Selection given Inconsistent Statistics, IEEE Statistical Signal Processing Workshop, Madison, Wisconsin, 2007.
J. Johnson, V. Chandrasekaran, and A. Willsky, Learning Markov Structure by Maximum Entropy Relaxation, International Conference in Artificial Intelligence and Statistics, San Juan, Puerto Rico, 2007.
V. Chandrasekaran, M. Wakin, D. Baron, and R. Baraniuk, Surflets: A Sparse Representation for Multidimensional Functions Containing Smooth Discontinuities, IEEE International Symposium on Information Theory, Chicago, Illinois, 2004.

Last updated: November 2023