Venkat Chandrasekaran: List of Publications
Venkat Chandrasekaran: List of Publications
Preprints
Journal papers
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A. Taeb, P. Bühlmann, and V. Chandrasekaran, Model Selection over Partially Ordered Sets, Proceedings of the National Academy of Sciences, 2024.
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E. O'Reilly and V. Chandrasekaran, Spectrahedral Regression, SIAM Journal on Optimization, 2023.
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J. Saunderson and V. Chandrasekaran, Terracini Convexity, Mathematical Programming, 2023.
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U. Candogan and V. Chandrasekaran, Convex Graph Invariant Relaxations for Graph Edit Distance, Mathematical Programming, 2022.
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R. Murray, V. Chandrasekaran, and A. Wierman, Newton Polytopes and Relative Entropy Optimization, Foundations of Computational Mathematics, 2021.
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U. Candogan, Y. Soh, and V. Chandrasekaran, A Note on Convex Relaxations for the Inverse Eigenvalue Problem, Optimization Letters, 2021.
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Y. Soh and V. Chandrasekaran, Fitting Tractable Convex Sets to Support Function Evaluations, Discrete and Computational Geometry, 2021.
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R. Murray, V. Chandrasekaran, and A. Wierman, Signomial and Polynomial Optimization via Relative Entropy and Partial Dualization, Mathematical Programming Computation, 2021.
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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.
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Y. Soh and V. Chandrasekaran, Learning Semidefinite Regularizers, Foundations of Computational Mathematics, 2019.
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U. Candogan and V. Chandrasekaran, Finding Planted Subgraphs with Few Eigenvalues using the Schur-Horn Relaxation, SIAM Journal on Optimization, 2018.
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A. Taeb and V. Chandrasekaran, Interpreting Latent Variables in Factor Models via Convex Optimization, Mathematical Programming, 2018.
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A. Taeb, J. Reager, M. Turmon, and V. Chandrasekaran, A Statistical Graphical Model of the California Reservoir System, Water Resources Research, 2017.
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Y. Soh and V. Chandrasekaran, High-Dimensional Change-Point Estimation: Combining Filtering with Convex Optimization, Applied and Computational Harmonic Analysis, 2017.
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V. Chandrasekaran and P. Shah, Relative Entropy Optimization and its Applications, Mathematical Programming, 2017.
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N. Matni and V. Chandrasekaran, Regularization for Design, IEEE Transactions on Automatic Control, 2016.
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V. Chandrasekaran and P. Shah, Relative Entropy Relaxations for Signomial Optimization, SIAM Journal on Optimization, 2016.
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V. Chandrasekaran and M. Jordan, Computational and Statistical Tradeoffs via Convex Relaxations, Proceedings of the National Academy of Sciences, 2013.
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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.
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V. Chandrasekaran, B. Recht, P. Parrilo, and A. Willsky, The Convex Geometry of Linear Inverse Problems, Foundations of Computational Mathematics, 2012.
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V. Chandrasekaran, P. Parrilo, and A. Willsky, Convex Graph Invariants, SIAM Review, 2012.
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P. Shah and V. Chandrasekaran, Group Symmetry and Covariance Regularization, Electronic Journal of Statistics, 2012. (erratum correcting notational mistake)
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Y. Liu, V. Chandrasekaran, A. Anandkumar, and A. Willsky, Feedback Message Passing for Inference in Gaussian Graphical Models, IEEE Transactions on Signal Processing, 2012.
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V. Chandrasekaran, P. Parrilo, and A. Willsky, Latent Variable Graphical Model Selection via Convex Optimization, Annals of Statistics (with discussion), 2012.
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V. Chandrasekaran, M. Chertkov, D. Gamarnik, D. Shah, and J. Shin, Counting Independent Sets using the Bethe Approximation, SIAM Journal on Discrete Mathematics, 2011.
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V. Chandrasekaran, S. Sanghavi, P. Parrilo, and A. Willsky, Rank Sparsity Incoherence for Matrix Decomposition, SIAM Journal on Optimization, 2011.
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M. Choi, V. Chandrasekaran, and A. Willsky, Gaussian Multiresolution Models: Exploiting Sparse Markov and Covariance Structure, IEEE Transactions on Signal Processing, 2010.
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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.
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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.
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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
Selected conference papers
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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.
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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.
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V. Chandrasekaran, S. Sanghavi, P. Parrilo, and A. Willsky, Sparse and Low-Rank Matrix Decompositions, IFAC Symposium on System Identification, Saint-Malo, France, 2009.
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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.
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V. Chandrasekaran, N. Srebro, and P. Harsha, Complexity of Inference in Graphical Models, Conference on Uncertainty in Artificial Intelligence and Statistics, Helsinki, Finland, 2008.
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V. Chandrasekaran, J. Johnson, and A. Willsky, Adaptive Embedded Subgraph Algorithms using Walk-Sum Analysis, Neural Information Processing Systems Conference, Vancouver, Canada, 2007.
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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.
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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.
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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