Learning from untrusted data

The vast majority of theoretical results in machine learning and statistics assume that the training data is a reliable reflection of the phenomena to be learned. Similarly, most learning techniques used in practice are brittle to the presence of large amounts of biased or malicious data. Motivated by this, we consider two frameworks for studying estimation, learning, and optimization in the presence of significant fractions of arbitrary data.

Authors

• Moses Charikar
• Jacob Steinhardt
• Gregory Valiant

Keywords

• robust learning
• high-dimensional statistics
• outlier removal