1 citations · 5 across the 13 of their papers we have counts for
4 papers · 1 filter
Robust Mixture Learning when Outliers Overwhelm Small Groups
Daniil Dmitriev, Rares-Darius Buhai, Stefan Tiegel +5
We study the problem of estimating the means of well-separated mixtures when an adversary may add arbitrary outliers. While strong guarantees are available when the outlier fractio…
Testably Learning Polynomial Threshold Functions
Lucas Slot, Stefan Tiegel, Manuel Wiedmer
Rubinfeld & Vasilyan recently introduced the framework of testable learning as an extension of the classical agnostic model. It relaxes distributional assumptions which are difficu…
Improved Hardness Results for Learning Intersections of Halfspaces
Stefan Tiegel
We show strong (and surprisingly simple) lower bounds for weakly learning intersections of halfspaces in the improper setting. Strikingly little is known about this problem. For in…
Computational-Statistical Gaps for Improper Learning in Sparse Linear Regression
Rares-Darius Buhai, Jingqiu Ding, Stefan Tiegel
We study computational-statistical gaps for improper learning in sparse linear regression. More specifically, given samples from a -sparse linear model in dimension , we…