12 citations · 12 across the 4 of their papers we have counts for
6 papers
DeGAS: Gradient-Based Optimization of Probabilistic Programs without Sampling
Francesca Randone, Romina Doz, Mirco Tribastone +1
We present DeGAS, a differentiable Gaussian approximate semantics for loopless probabilistic programs that enables sample-free, gradient-based optimization in models with both cont…
Foundations for Deductive Verification of Continuous Probabilistic Programs: From Lebesgue to Riemann and Back
Kevin Batz, Joost-Pieter Katoen, Francesca Randone +1
We lay out novel foundations for the computer-aided verification of guaranteed bounds on expected outcomes of imperative probabilistic programs featuring (i) general loops, (ii) co…
Symbolic Quantitative Information Flow for Probabilistic Programs
Philipp Schröer, Francesca Randone, Raúl Pardo +1
It is of utmost importance to ensure that modern data intensive systems do not leak sensitive information. In this paper, the authors, who met thanks to Joost-Pieter Katoen, discus…
Inference of Probabilistic Programs with Moment-Matching Gaussian Mixtures
Francesca Randone, Luca Bortolussi, Emilio Incerto +1
Computing the posterior distribution of a probabilistic program is a hard task for which no one-fit-for-all solution exists. We propose Gaussian Semantics, which approximates the e…
Model Learning with Personalized Interpretability Estimation (ML-PIE)
Marco Virgolin, Andrea De Lorenzo, Francesca Randone +2
High-stakes applications require AI-generated models to be interpretable. Current algorithms for the synthesis of potentially interpretable models rely on objectives or regularizat…
Learning a Formula of Interpretability to Learn Interpretable Formulas
Marco Virgolin, Andrea De Lorenzo, Eric Medvet +1
Many risk-sensitive applications require Machine Learning (ML) models to be interpretable. Attempts to obtain interpretable models typically rely on tuning, by trial-and-error, hyp…