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Identifiability of deep generative models under mixture priors without auxiliary information



Bohdan Kivva , Goutham Rajendran , Pradeep Ravikumar , Bryon Aragam

* 31 pages, 9 figures 

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A non-graphical representation of conditional independence via the neighbourhood lattice



Arash A. Amini , Bryon Aragam , Qing Zhou

* 30 pages, 3 figures 

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A super-polynomial lower bound for learning nonparametric mixtures



Bryon Aragam , Wai Ming Tai


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Optimal estimation of Gaussian DAG models



Ming Gao , Wai Ming Tai , Bryon Aragam

* 19 pages, 2 figures, to appear in AISTATS 2022 

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Tradeoffs of Linear Mixed Models in Genome-wide Association Studies



Haohan Wang , Bryon Aragam , Eric Xing

* in final revision of Journal of Computational Biology 

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NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters



Ben Lengerich , Caleb Ellington , Bryon Aragam , Eric P. Xing , Manolis Kellis


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Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families



Goutham Rajendran , Bohdan Kivva , Ming Gao , Bryon Aragam

* Accepted to NeurIPS 2021; 27 pages, 9 figures 

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Efficient Bayesian network structure learning via local Markov boundary search



Ming Gao , Bryon Aragam

* 30 pages, 3 figures, to appear in NeurIPS 2021 

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Uniform Consistency in Nonparametric Mixture Models



Bryon Aragam , Ruiyi Yang


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