[Statlist] Reminder: FDS Seminar talk with Sewoong Oh - 3 June 2021, 17:00-18:00 CEST

[Maurer Letizia]

We are pleased to announce the following online talk in our ETH Foundations of Data Science Seminar series

"Robust and Differentially Private Estimation“
by Sewoong Oh, Allen School of Computer Science & Engineering University of Washington

Date and Time: Thursday, 03 June 2021, 17:00-18:00 CEST
Place: Zoom at https://ethz.zoom.us/j/65961194418

Abstract: "Differential privacy has emerged as a standard requirement in a variety of applications ranging from the U.S. Census to data collected in commercial devices, initiating an extensive line of research in accurately and privately releasing statistics of a database. An increasing number of such databases consist of data from multiple sources, not all of which can be trusted. This leaves existing private analyses vulnerable to attacks by an adversary who injects corrupted data. We are in a dire need for algorithms that guarantee privacy and robustness (to a fraction of data being corrupted) simultaneously. However, even the simplest questions remain open. For the canonical problem of estimating the mean (and the covariance) from i.i.d. samples under both privacy and robustness, I will present a minimax optimal algorithm, but requires an exponential run-​time. This is followed by an efficient algorithm that requires a factor of $d^{1/2}$ more samples when the samples are in $d$-​dimensions. It remains an open question if this computational gap can be closed, either with a more sample efficient algorithm or a tighter lower bound."

Organisers: A. Bandeira, H. Bölcskei, P. Bühlmann, J. Buhmann, N. He, T. Hofmann, A. Krause, R. Kyng, A. Lapidoth, H.-A. Loeliger, M. Maathuis, N. Meinshausen, S. Mishra, G. Rätsch, Ch. Schwab, D. Steurer, S. van de Geer, F. Yang, R. Zenklusen

Seminar website: https://math.ethz.ch/sfs/news-and-events/data-science-seminar.html