Being Robust (in High Dimensions) Can Be Practical

ICML, pp. 999-1008, 2017.

Cited by: 72|Bibtex|Views30|Links
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Abstract:

Robust estimation is much more challenging in high dimensions than it is in one dimension: Most techniques either lead to intractable optimization problems or estimators that can tolerate only a tiny fraction of errors. Recent work in theoretical computer science has shown that, in appropriate distributional models, it is possible to robu...More

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