29 citations · 88 across the 10 of their papers we have counts for
6 papers · 1 filter
Spectral Top-Down Recovery of Latent Tree Models
Yariv Aizenbud, Ariel Jaffe, Meng Wang +5
Modeling the distribution of high dimensional data by a latent tree graphical model is a prevalent approach in multiple scientific domains. A common task is to infer the underlying…
Spectral neighbor joining for reconstruction of latent tree models
Ariel Jaffe, Noah Amsel, Yariv Aizenbud +3
A common assumption in multiple scientific applications is that the distribution of observed data can be modeled by a latent tree graphical model. An important example is phylogene…
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Ariel Jaffe, Roi Weiss, Shai Carmi +2
Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem…
Minimax-optimal semi-supervised regression on unknown manifolds
Amit Moscovich, Ariel Jaffe, Boaz Nadler
We consider semi-supervised regression when the predictor variables are drawn from an unknown manifold. A simple two step approach to this problem is to: (i) estimate the manifold…
A Deep Learning Approach to Unsupervised Ensemble Learning
Uri Shaham, Xiuyuan Cheng, Omer Dror +4
We show how deep learning methods can be applied in the context of crowdsourcing and unsupervised ensemble learning. First, we prove that the popular model of Dawid and Skene, whic…
Estimating the Accuracies of Multiple Classifiers Without Labeled Data
Ariel Jaffe, Boaz Nadler, Yuval Kluger
In various situations one is given only the predictions of multiple classifiers over a large unlabeled test data. This scenario raises the following questions: Without any labeled…