65 citations · 91 across the 4 of their papers we have counts for
7 papers
Learning to Sample from Censored Markov Random Fields
Ankur Moitra, Elchanan Mossel, Colin Sandon
We study learning Censor Markov Random Fields (abbreviated CMRFs). These are Markov Random Fields where some of the nodes are censored (not observed). We present an algorithm for l…
Poly-time universality and limitations of deep learning
Emmanuel Abbe, Colin Sandon
The goal of this paper is to characterize function distributions that deep learning can or cannot learn in poly-time. A universality result is proved for SGD-based deep learning an…
Parallels Between Phase Transitions and Circuit Complexity?
Ankur Moitra, Elchanan Mossel, Colin Sandon
In many natural average-case problems, there are or there are believed to be critical values in the parameter space where the structure of the space of solutions changes in a funda…
Provable limitations of deep learning
Emmanuel Abbe, Colin Sandon
As the success of deep learning reaches more grounds, one would like to also envision the potential limits of deep learning. This paper gives a first set of results proving that ce…
Graph powering and spectral robustness
Emmanuel Abbe, Enric Boix, Peter Ralli +1
Spectral algorithms, such as principal component analysis and spectral clustering, typically require careful data transformations to be effective: upon observing a matrix , one…
Recovering communities in the general stochastic block model without knowing the parameters
Emmanuel Abbe, Colin Sandon
Most recent developments on the stochastic block model (SBM) rely on the knowledge of the model parameters, or at least on the number of communities. This paper introduces efficien…