216 citations · 454 across the 55 of their papers we have counts for
4 papers · 1 filter
Fault Tolerance in Iterative-Convergent Machine Learning
Aurick Qiao, Bryon Aragam, Bingjing Zhang +1
Machine learning (ML) training algorithms often possess an inherent self-correcting behavior due to their iterative-convergent nature. Recent systems exploit this property to achie…
Sample Complexity of Nonparametric Semi-Supervised Learning
Chen Dan, Liu Leqi, Bryon Aragam +2
We study the sample complexity of semi-supervised learning (SSL) and introduce new assumptions based on the mismatch between a mixture model learned from unlabeled data and the tru…
DAGs with NO TEARS: Continuous Optimization for Structure Learning
Xun Zheng, Bryon Aragam, Pradeep Ravikumar +1
Estimating the structure of directed acyclic graphs (DAGs, also known as Bayesian networks) is a challenging problem since the search space of DAGs is combinatorial and scales supe…
Identifiability of Nonparametric Mixture Models and Bayes Optimal Clustering
Bryon Aragam, Chen Dan, Eric P. Xing +1
Motivated by problems in data clustering, we establish general conditions under which families of nonparametric mixture models are identifiable, by introducing a novel framework in…