279 citations · 1.4k across the 46 of their papers we have counts for
4 papers · 1 filter
A Separation Logic for Negative Dependence
Jialu Bao, Marco Gaboardi, Justin Hsu +1
Formal reasoning about hashing-based probabilistic data structures often requires reasoning about random variables where when one variable gets larger (such as the number of elemen…
Data-Driven Invariant Learning for Probabilistic Programs
Jialu Bao, Nitesh Trivedi, Drashti Pathak +2
Morgan and McIver's weakest pre-expectation framework is one of the most well-established methods for deductive verification of probabilistic programs. Roughly, the idea is to gene…
A Quantum Interpretation of Bunched Logic for Quantum Separation Logic
Li Zhou, Gilles Barthe, Justin Hsu +2
We propose a model of the substructural logic of Bunched Implications (BI) that is suitable for reasoning about quantum states. In our model, the separating conjunction of BI descr…
Learning Differentially Private Mechanisms
Subhajit Roy, Justin Hsu, Aws Albarghouthi
Differential privacy is a formal, mathematical definition of data privacy that has gained traction in academia, industry, and government. The task of correctly constructing differe…