The One World Seminar Series on the Mathematics of Machine Learning is an online platform for research seminars, workshops and seasonal schools in theoretical machine learning. The focus of the series lies on theoretical advances in machine learning and deep learning as a complement to the one world seminars on probability, on Information, Signals and Data (MINDS), on methods for arbitrary data sources (MADS), and on imaging and inverse problems (IMAGINE).
The series was started during the Covid-19 epidemic in 2020 to bring together researchers from all over the world for presentations and discussions in a virtual environment. It follows in the footsteps of other community projects under the One World Umbrella which originated around the same time.
We welcome suggestions for speakers concerning new and exciting developments and are committed to providing a platform also for junior researchers. We recognize the advantages that online seminars provide in terms of flexibility, and we are experimenting with different formats. Any feedback on different events is welcome.
Wed 5 March
How to beat a Bayesian adversary
Deep neural networks and other modern machine learning models are often susceptible to adversarial attacks. Indeed, an adversary may often be able to change a model's prediction through a small, directed perturbation of the model's input - an issue in safety-critical applications. Adversarially robust machine learning is usually based on a minmax optimisation problem that minimises the machine learning loss under maximisation-based adversarial attacks. In this work, we study adversaries that determine their attack using a Bayesian statistical approach rather than maximisation. The resulting Bayesian adversarial robustness problem is a relaxation of the usual minmax problem. To solve this problem, we propose Abram - a continuous-time particle system that shall approximate the gradient flow corresponding to the underlying learning problem. We show that Abram approximates a McKean--Vlasov process and justify the use of Abram by giving assumptions under which the McKean--Vlasov process finds the minimiser of the Bayesian adversarial robustness problem. We discuss two ways to discretise Abram and show its suitability in benchmark adversarial deep learning experiments.
Zoom link: https://uofglasgow.zoom.us/j/82620068482
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Seminars are held online on Zoom. The presentations are recorded and video is made available on our youtube channel. A list of past seminars can be found here. All seminars, unless otherwise stated, are held on Wednesdays at 12 noon ET. The invitation will be shared on this site before the talk and distributed via email.
Ricardo Baptista (University of Toronto)
Wuyang Chen (Simon Fraser University)
Bin Dong (Peking University)
Lyudmila Grigoryeva (University of St. Gallen)
Boumediene Hamzi (Caltech)
Yuka Hashimoto (NTT)
Qianxiao Li (National University of Singapore)
Lizao Li (Google)
Tiffany Vlaar (University of Glasgow)
Zhiqin Xu (Shanghai Jiao Tong University)
Simon Shaolei Du (University of Washington)
Franca Hoffmann (Caltech)
Surbhi Goel (Microsoft Research NY)
Issa Karambal (Quantum Leap Africa)
Chao Ma (Stanford University)
Song Mei (UC Berkeley)
Philipp Petersen (University of Vienna)
Matthew Thorpe (University of Warwick)
Stephan Wojtowytsch (University of Pittsburgh)