robust risk minimization
Robust Risk Minimization for Statistical Learning
Osama, Muhammad, Zachariah, Dave, Stoica, Peter
We consider a general statistical learning problem where an unknown fraction of the training data is corrupted. We develop a robust learning method that only requires specifying an upper bound on the corrupted data fraction. The method is formulated as a risk minimization problem that can be solved using a blockwise coordinate descent algorithm. We demonstrate the wide range applicability of the method, including regression, classification, unsupervised learning and classic parameter estimation, with state-of-the-art performance.
1910.01544
Country:
- North America > United States > Wisconsin (0.05)
- North America > United States > New York > New York County > New York City (0.04)
- North America > United States > California > Los Angeles County > Long Beach (0.04)
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Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)