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  • A Unified Framework for Robustness, Fairness, and Collaboration in Machine Learning by Dr. Nika Haghtalab at CRCS Social Impact Seminar Series

A Unified Framework for Robustness, Fairness, and Collaboration in Machine Learning by Dr. Nika Haghtalab at CRCS Social Impact Seminar Series

Monday, September 11, 2023 11am to 12pm

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About this Event

Science and Engineering Complex (SEC), 2.122 View map Free Event

150 Western Avenue, Allston, MA 02134

https://crcs.seas.harvard.edu/event/nika-haghtalab
View map Free Event

“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.”

 

Event Details

Event Type

Colloquia / Seminar / Lecture

Academics

Computer Science

Series

CRCS Seminars

Audience

Faculty General Public Graduate Students Researchers / Postdocs Staff Undergraduate Students Harvard Community SEAS Community

Research Centers & Initiatives

Center for Research on Computation and Society (CRCS)

Website

https://crcs.seas.harvard.edu/event/n...

Cost

Free

Speaker 1

Nika Haghtalab

Speaker Bio

Nika Haghtalab is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. She works broadly on the theoretical aspects of machine learning and algorithmic economics. Prof. Haghtalab's work builds theoretical foundations for ensuring both the performance of learning algorithms in presence of everyday economic forces and the integrity of social and economic forces that are born out of the use of machine learning systems. She received her Ph.D. from the Computer Science Department of Carnegie Mellon University, where her thesis won the CMU School of Computer Science Dissertation Award (ACM nomination) and the SIGecom Dissertation Honorable Mention. She is a co-founder of Learning Theory Alliance (LeT-All), a large-scale mentoring and community building initiative for the theory of machine learning community. Among her honors are an NSF CAREER award, NeurIPS and ICAPS best paper awards, an EC exemplary in AI track award, and several industry awards and fellowships.”

Host

Mark Peterson

Contact

Mark Peterson

Email

crcs@seas.harvard.edu

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