A Unified Framework for Robustness, Fairness, and Collaboration in Machine Learning by Dr. Nika Haghtalab at CRCS Social Impact Seminar Series
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150 Western Avenue, Allston, MA 02134
https://crcs.seas.harvard.edu/event/nika-haghtalab“Pervasive needs for robustness, multi-agent collaboration, and fairness have motivated the design of new methods in research and development. However, these methods remain largely stylized, lacking a foundational perspective and provable performance. In this talk, I will introduce and highlight the importance of multi-objective learning as a unifying paradigm for addressing these needs. This paradigm aims to optimize complex and unstructured objectives from only a small amount of sampled data. I will also discuss how the multi-objective learning paradigm relates to the classical and modern considerations in machine learning broadly, introduce technical tools with versatile provable guarantees, and empirical evidence for its performance on a range of important benchmarks.”
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