79 citations · 241 across the 9 of their papers we have counts for
21 papers
Symbolic Execution for Randomized Programs
Zachary Susag, Sumit Lahiri, Justin Hsu +1
We propose a symbolic execution method for programs that can draw random samples. In contrast to existing work, our method can verify randomized programs with unknown inputs and ca…
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…
Analyzing Accuracy Loss in Randomized Smoothing Defenses
Yue Gao, Harrison Rosenberg, Kassem Fawaz +2
Recent advances in machine learning (ML) algorithms, especially deep neural networks (DNNs), have demonstrated remarkable success (sometimes exceeding human-level performance) on s…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
A Probabilistic Separation Logic
Gilles Barthe, Justin Hsu, Kevin Liao
Probabilistic independence is a useful concept for describing the result of random sampling---a basic operation in all probabilistic languages---and for reasoning about groups of r…