Statistical Learning and Differential Privacy Workshop

A Statistical Learning and Differential Privacy Workshop is taking place at the University of Bath on the 12th-13th September 2022.

12-13 September 2022, University of Bath

Statistical learning and deep learning techniques have been deployed in many parts of our lives, for example in search engines, online recommendation systems, and AI-assisted healthcare. An important question is how can we perform statistical learning to find general patterns from datasets without revealing data of individual participants? This question has become the key challenge that hinders further applications of statistical learning and deep learning in privacy-sensitive applications. Differential Privacy (DP) is a mathematical framework that can provide theoretical guarantees of privacy, while allowing us to achieve model utility and accuracy for specific applications. Mathematics has been the key for breakthroughs in developing statistical learning with DP. Recently, we have seen exciting developments in compressive learning and dynamical systems for designing and proving statistical learning algorithms with DP guarantees.

This workshop will bring together researchers and practitioners from statistical machine learning, deep learning, compressive sensing, dynamical systems and Bayesian neural networks to discuss this recent development and provide a snapshot of this interdisciplinary research topic to students, mathematicians, computer scientists and the wider community.

This workshop is organised by the Center for Mathematics and Algorithms for Data (MAD) at the University of Bath. It is sponsored by ART-AI and Maths4DL.

Programme: TBC

Monday 12 – Tuesday 13 September 2022

Confirmed Speakers

Clarice Poon (University of Bath)

Abstracts of the talks

TBA

Organisers

Cangxiong Chen, Tony Shardlow, Neill Campbell, Clarice Poon, Matt Nunes, Sandipan Roy, Teo Deveney

Event Coordinator

Christina Squire


Event Info

Start Date 12.09.2022
End Date 13.09.2022
Start Time 9:00am
End Time 5:00pm

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