TALK    [MERL Seminar Series 2023] Prof. Flavio Calmon presents talk titled Multiplicity in Machine Learning

Date released: November 7, 2023

  •  TALK    [MERL Seminar Series 2023] Prof. Flavio Calmon presents talk titled Multiplicity in Machine Learning
    (Learn more about the MERL Seminar Series.)
  • Date & Time:

    Tuesday, November 7, 2023; 12:00 PM

  • Abstract:

    This talk reviews the concept of predictive multiplicity in machine learning. Predictive multiplicity arises when different classifiers achieve similar average performance for a specific learning task yet produce conflicting predictions for individual samples. We discuss a metric called “Rashomon Capacity” for quantifying predictive multiplicity in multi-class classification. We also present recent findings on the multiplicity cost of differentially private training methods and group fairness interventions in machine learning.

    This talk is based on work published at ICML'20, NeurIPS'22, ACM FAccT'23, and NeurIPS'23.

  • Speaker:

    Flavio Calmon
    Harvard University

    Flavio P. Calmon is an Assistant Professor of Electrical Engineering at the Harvard John A. Paulson School of Engineering and Applied Sciences. Before joining Harvard, he was the inaugural Data Science for Social Good Post-Doctoral Fellow at IBM Research in Yorktown Heights, New York. He received his Ph.D. in Electrical Engineering and Computer Science at MIT. His research develops information-theoretic tools for responsible and reliable machine learning. Prof. Calmon has received the NSF CAREER award, faculty awards from Google, IBM, and Amazon, the NSF-Amazon Fairness in AI award, the Harvard Data Science Initiative Bias2 award, and the Harvard Dean of Undergraduate Studies Commendation for “Extraordinary Teaching during Extraordinary Times.” He also received the inaugural Título de Honra ao Mérito (Honor to the Merit Title) given to alumni from the Universidade de Brasília (Brazil).

  • MERL Host:

    Ye Wang

  • Research Areas:

    Artificial Intelligence, Machine Learning