Researchers from the UK and abroad gathered at Bath between 12th and 13th September at the Statistical Learning and Differential Privacy Workshop, which is chaired by our MIRA Cangxiong Chen. The workshop brought together researchers and practitioners from statistical machine learning, deep learning, compressed sensing, dynamical systems and Bayesian machine learning to discuss recent development in statistical learning and differential privacy and provided a snapshot of this interdisciplinary research topic to students, mathematicians, computer scientists and the wider community. The workshop is sponsored by ART-AI and Maths4DL.
As the chair of the organising committee, Cangxiong was the main driving force in organising this workshop. In his words, “the idea of having a workshop started from a discussion between two members at the Center for Mathematics and Algorithms for Data (MAD) at the university. Privacy in machine learning is becoming a crucial issue in our society as machine learning becomes more widely adopted and more detailed personal data are being collected through edge devices such as smartphones to feed the models. To tackle the challenge would require lots of development from the mathematical side, especially novel methods for differential privacy guarantee. I hope this workshop can get more people in maths and computer science interested in privacy of machine learning.”
Please refer to the workshop website for more details: https://mathematics-and-algorithms-for-data.github.io/events/workshop_SLDP2022/