B. Nadler

F. Parisi

F. Strino

Strino

Walter

Wright

Y. Kluger

Significance
          A key challenge in a broad range of decision-making and classification problems is how to rank and combine the possibly conflicting suggestions of several advisers of unknown reliability. We provide mathematical insights of striking conceptual simplicity that explain mutual relationships between independent advisers. These insights enable the design of efficient, robust, and reliable methods to rank the advisers’ performances and construct improved predictions in the absence of ground truth. Furthermore, these methods are robust to the presence of small subgroups of malicious advisers (cartels) attempting to veer the combined decisions to their interest.</jats:p

Fabio Parisi

Francesco Strino

Boaz Nadler

Yuval Kluger

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Proceedings of the National Academy of Sciences

Ranking and combining multiple predictors without labeled data

Ranking and combining multiple predictors without labeled data

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