279 citations · 1.4k across the 46 of their papers we have counts for
9 papers · 1 filter
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…
Guarded Kleene Algebra with Tests: Verification of Uninterpreted Programs in Nearly Linear Time
Steffen Smolka, Nate Foster, Justin Hsu +3
Guarded Kleene Algebra with Tests (GKAT) is a variation on Kleene Algebra with Tests (KAT) that arises by restricting the union () and iteration () operations from KAT to pre…
Hypothesis Testing Interpretations and Renyi Differential Privacy
Borja Balle, Gilles Barthe, Marco Gaboardi +2
Differential privacy is a de facto standard in data privacy, with applications in the public and private sectors. A way to explain differential privacy, which is particularly appea…
Scalable Verification of Probabilistic Networks
Steffen Smolka, Praveen Kumar, David M Kahn +4
This paper presents McNetKAT, a scalable tool for verifying probabilistic network programs. McNetKAT is based on a new semantics for the guarded and history-free fragment of Probab…
Data Poisoning against Differentially-Private Learners: Attacks and Defenses
Yuzhe Ma, Xiaojin Zhu, Justin Hsu
Data poisoning attacks aim to manipulate the model produced by a learning algorithm by adversarially modifying the training set. We consider differential privacy as a defensive mea…
Fake News Detection via NLP is Vulnerable to Adversarial Attacks
Zhixuan Zhou, Huankang Guan, Meghana Moorthy Bhat +1
News plays a significant role in shaping people's beliefs and opinions. Fake news has always been a problem, which wasn't exposed to the mass public until the past election cycle f…
A Pre-Expectation Calculus for Probabilistic Sensitivity
Alejandro Aguirre, Gilles Barthe, Justin Hsu +3
Sensitivity properties describe how changes to the input of a program affect the output, typically by upper bounding the distance between the outputs of two runs by a monotone func…
Relational Proofs for Quantum Programs
Gilles Barthe, Justin Hsu, Mingsheng Ying +2
Relational verification of quantum programs has many potential applications in quantum and post-quantum security and other domains. We propose a relational program logic for quantu…