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Poster
Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering
Ilias Diakonikolas · Daniel Kane · Sushrut Karmalkar · Eric Price · Alistair Stewart

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #228
in Theory -- Learning Theory »

We study high-dimensional sparse estimation tasks in a robust setting where a constant fraction of the dataset is adversarially corrupted. Specifically, we focus on the fundamental problems of robust sparse mean estimation and robust sparse PCA. We give the first practically viable robust estimators for these problems. In more detail, our algorithms are sample and computationally efficient and achieve near-optimal robustness guarantees. In contrast to prior provable algorithms which relied on the ellipsoid method, our algorithms use spectral techniques to iteratively remove outliers from the dataset. Our experimental evaluation on synthetic data shows that our algorithms are scalable and significantly outperform a range of previous approaches, nearly matching the best error rate without corruptions.

Keywords: Learning Theory · Theory
[ Paper]  [ Poster] 

Author Information

Ilias Diakonikolas (UW Madison)
Daniel Kane (UCSD)
Sushrut Karmalkar (The University of Texas at Austin)
Eric Price (University of Texas at Austin)
Alistair Stewart (University of Southern California)

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