The Secure, Reliable, and Intelligent Systems Lab (SRI) is a research
group in the Department of Computer Science at ETH Zurich.
Our current research focus is on the areas of reliable, secure,
robust and fair machine learning, probabilistic and quantum
programming, and machine learning for code.
Our work led to three ETH spin-offs: DeepCode.ai (AI for Code), ChainSecurity (security verification),
and LatticeFlow (robust machine learning).
Please see Research and
Publications to learn
more.
Latest Blog Posts
Latest News
Latest News & Blog Posts
SRI Lab at ICLR 2022: SRI Lab will present five works at ICLR 2022! In this meta post we aggregate all content related to our ICLR papers, including links to the conference portal and individual blogposts where you can learn more about the topics we currently focus on.
Generating provably robust adversarial examples: We introduce the concept of provably robust adversarial examples. These are adversarial examples that are generated together with a region around them that can be proven robust to perturbations. We also show a method for generating large such regions in a scalable manner.
Multi-neuron relaxation guided branch-and-bound: Learn more about how multi-neuron constraints can be used in a Branch-and-Bound framework to build a state-of-the-art complete neural network verifier.
Encoding sensitive data with guarantees: Fair Normalizing Flows (FNF) are a new approach for encoding data into a new representation in order to ensure fairness and utility in downstream tasks.
