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Simple and near-optimal algorithms for hidden stratification and multi-group learning
Christopher Tosh · Daniel Hsu

Tue Jul 19 03:30 PM -- 05:30 PM (PDT) @ Hall E #1215

Multi-group agnostic learning is a formal learning criterion that is concerned with the conditional risks of predictors within subgroups of a population. The criterion addresses recent practical concerns such as subgroup fairness and hidden stratification. This paper studies the structure of solutions to the multi-group learningproblem, and provides simple and near-optimal algorithms for the learning problem.

Author Information

Christopher Tosh (Memorial Sloan Kettering)
Christopher Tosh

I am a postdoc in the Department of Epidemiology and Biostatistics at Memorial Sloan Kettering Cancer Center. My current interests are on problems arising in interactive learning, representation learning, and robust learning, particularly with applications to cancer research. Previously, I was at Columbia University, where I worked with Daniel Hsu; and before that, I received my PhD from UC San Diego, where I was advised by Sanjoy Dasgupta.

Daniel Hsu (Columbia University)

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