88 citations · 96 across the 13 of their papers we have counts for
Showing 2018Show all
3 papers · 1 filter
stat.ML2018★ 3 cited
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data
Victor Veitch, Morgane Austern, Wenda Zhou +2
Empirical risk minimization is the main tool for prediction problems, but its extension to relational data remains unsolved. We solve this problem using recent ideas from graph sam…
math.ST2018★ 3 cited
Limit theorems for invariant distributions
Morgane Austern, Peter Orbanz
A distributional symmetry is invariance of a distribution under a group of transformations. Exchangeability and stationarity are examples. We explain that a result of ergodic theor…
stat.ML2018★ 88 cited
Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach
Wenda Zhou, Victor Veitch, Morgane Austern +2
Modern neural networks are highly overparameterized, with capacity to substantially overfit to training data. Nevertheless, these networks often generalize well in practice. It has…