1 citations · 2 across the 9 of their papers we have counts for
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Efficient Distribution Learning with Error Bounds in Wasserstein Distance
Eduardo Figueiredo, Steven Adams, Luca Laurenti
The Wasserstein distance has emerged as a key metric to quantify distances between probability distributions, with applications in various fields, including machine learning, contr…
discretize_distributions: Efficient Quantization of Gaussian Mixtures with Guarantees in Wasserstein Distance
Steven Adams, Elize Alwash, Luca Laurenti
We present discretize_distributions, a Python package that efficiently constructs discrete approximations of Gaussian mixture distributions and provides guarantees on the approxima…
Finite Neural Networks as Mixtures of Gaussian Processes: From Provable Error Bounds to Prior Selection
Steven Adams, Andrea Patanè, Morteza Lahijanian +1
Infinitely wide or deep neural networks (NNs) with independent and identically distributed (i.i.d.) parameters have been shown to be equivalent to Gaussian processes. Because of th…
BNN-DP: Robustness Certification of Bayesian Neural Networks via Dynamic Programming
Steven Adams, Andrea Patane, Morteza Lahijanian +1
In this paper, we introduce BNN-DP, an efficient algorithmic framework for analysis of adversarial robustness of Bayesian Neural Networks (BNNs). Given a compact set of input point…