Mathematical Conversations
Robustness, Verifiability and Privacy in ML
Cryptography and Machine Learning have shared a curious history: a scientific success for one often provided an example of an impossible task for the other. Today, the goals of the two fields are aligned. Cryptographic models and tools can and should play a role in ensuring the trustworthiness of AI and machine learning and address problems such as privacy of training input, model verification and robustness of models to adversarial inputs. I will discuss three results along these lines.
Date & Time
October 07, 2020 | 5:30pm – 7:00pm
Location
Remote AccessSpeakers
Shafi Goldwasser
Affiliation
Simons Institute and University of California, Berkeley