Near-Optimal Learning of Tree-Structured Distributions by Chow and Liu von Bhattacharyya, Arnab, Gayen, Sutanu, Price, Eric, Tan, Vincent Y. F., Vinodchandran, N. V.

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Perfect Zero Knowledge: New Upperbounds and Relativized Separations von Dixon, Peter, Gayen, Sutanu, Pavan, A., Vinodchandran, N. V.

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Efficient Sample-optimal Learning of Gaussian Tree Models via Sample-optimal Testing of Gaussian Mutual Information von Gayen, Sutanu, Kale, Sanket, Sen, Sayantan

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Algorithms for k-median Clustering over Distributed Streams von Gayen, Sutanu, Vinodchandran, N. V.

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Learnability of Parameter-Bounded Bayes Nets von Bhattacharyya, Arnab, Choo, Davin, Gayen, Sutanu, Myrisiotis, Dimitrios

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Distribution Learning Meets Graph Structure Sampling von Bhattacharyya, Arnab, Gayen, Sutanu, John, Philips George, Sen, Sayantan, Vinodchandran, N. V

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Total Variation Distance for Product Distributions is $\#\mathsf{P}$-Complete von Bhattacharyya, Arnab, Gayen, Sutanu, Meel, Kuldeep S, Myrisiotis, Dimitrios, Pavan, A, Vinodchandran, N. V

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Efficient Sample-optimal Learning of Gaussian Tree Models via Sample-optimal Testing of Gaussian Mutual Information von Gayen, Sutanu, Kale, Sanket, Sen, Sayantan

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Total Variation Distance Meets Probabilistic Inference von Bhattacharyya, Arnab, Gayen, Sutanu, Meel, Kuldeep S, Myrisiotis, Dimitrios, Pavan, A, Vinodchandran, N. V

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Learning Sparse Fixed-Structure Gaussian Bayesian Networks von Bhattacharyya, Arnab, Choo, Davin, Gajjala, Rishikesh, Gayen, Sutanu, Wang, Yuhao

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On Approximating Total Variation Distance von Bhattacharyya, Arnab, Gayen, Sutanu, Meel, Kuldeep S, Myrisiotis, Dimitrios, Pavan, A, Vinodchandran, N V

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Testing Product Distributions: A Closer Look von Bhattacharyya, Arnab, Gayen, Sutanu, Kandasamy, Saravanan, Vinodchandran, N. V

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Near-Optimal Learning of Tree-Structured Distributions by Chow-Liu von Bhattacharyya, Arnab, Gayen, Sutanu, Price, Eric, Vinodchandran, N. V

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Learnability of Parameter-Bounded Bayes Nets von Bhattacharyya, Arnab, Choo, Davin, Gayen, Sutanu, Myrisiotis, Dimitrios

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Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning von Bhattacharyya, Arnab, Gayen, Sutanu, Meel, Kuldeep S, Vinodchandran, N. V

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Efficient inference of interventional distributions von Bhattacharyya, Arnab, Gayen, Sutanu, Kandasamy, Saravanan, Raval, Vedant, Vinodchandran, N. V

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Learning Sparse Fixed-Structure Gaussian Bayesian Networks von Bhattacharyya, Arnab, Choo, Davin, Gajjala, Rishikesh, Gayen, Sutanu, Wang, Yuhao

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Total Variation Distance Meets Probabilistic Inference von Bhattacharyya, Arnab, Gayen, Sutanu, Meel, Kuldeep S, Myrisiotis, Dimitrios, Pavan, A, Vinodchandran, N V

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Total Variation Distance for Product Distributions is \(\#\mathsf{P}\)-Complete von Bhattacharyya, Arnab, Gayen, Sutanu, Meel, Kuldeep S, Myrisiotis, Dimitrios, Pavan, A, Vinodchandran, N V

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Distribution Learning Meets Graph Structure Sampling von Bhattacharyya, Arnab, Gayen, Sutanu, Philips, George John, Sen, Sayantan, Vinodchandran, N V

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