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Fairness in Machine Learning

  • FDS/CADMY Research in Motion Colloquium: Aranyak Mehta (Google Research), “From Theory to Practice, and Back: Ad Auctions and Online Matching”

    Research in Motion Series is co-hosted by the Center for Algorithms, Data, and Market Design at Yale (CADMY) and the Yale Institute for Foundations of Data Science (FDS). Abstract: We will discuss topics in the area of market algorithms together with their impactful applications, including auctions, autobidding, online allocations, and more recent connections to AI. …

  • FDS Seminar: Kumar Kshitij Patel (TTIC)

    “Re-inventing Machine Learning for Multiple Distributions: Optimization, Privacy, and Incentives” Abstract: Federated Learning (FL) has emerged as a transformative framework for multi-distribution learning, driving breakthroughs in healthcare, research, finance, and consumer technologies. FL enables agents to train models on private data without sharing raw information, offering a basic yet crucial step toward safeguarding privacy while complying with…

  • FDS Colloquium: Bhramar Mukherjee (Yale), “Analysis of “Big” Real-World Health Care Data: Promises and Perils”

    Speaker: Bhramar Mukherjee, Ph.D.Senior Associate Dean of Public Health Data Science and Data EquityAnna M.R Lauder Professor of BiostatisticsProfessor of Epidemiology (Chronic Diseases) and of Statistics and Data ScienceYale University Optional Zoom link: https://yale.zoom.us/j/94323793445 Analysis of “Big” Real-World Health Care Data: Promises and Perils Abstract: Using administrative patient-care data such as Electronic Health Records and…