65 citations · 143 across the 21 of their papers we have counts for
4 papers · 1 filter
On the Power of Differentiable Learning versus PAC and SQ Learning
Emmanuel Abbe, Pritish Kamath, Eran Malach +2
We study the power of learning via mini-batch stochastic gradient descent (SGD) on the population loss, and batch Gradient Descent (GD) on the empirical loss, of a differentiable m…
Spectral Recovery of Binary Censored Block Models
Souvik Dhara, Julia Gaudio, Elchanan Mossel +1
Community detection is the problem of identifying community structure in graphs. Often the graph is modeled as a sample from the Stochastic Block Model, in which each vertex belong…
Spoofing Generalization: When Can't You Trust Proprietary Models?
Ankur Moitra, Elchanan Mossel, Colin Sandon
In this work, we study the computational complexity of determining whether a machine learning model that perfectly fits the training data will generalizes to unseen data. In partic…
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…