Multi-objective Learning: An Algorithmic Toolbox for Optimal Predictions Anytime Anywhere!

In this talk, I will introduce multiobjective learning as a unifying paradigm for learning models with performance guarantees across arbitrary downstream tasks and losses. I will present an algorithmic toolbox for learning such multiobjective models from a small number of samples and with modest computation. I will also highlight how this toolbox provides a useful lens for designing algorithms and obtaining improved or optimal guarantees for several general frameworks in ML theory, including multi-distribution learning, group distributionally robust learning, fairness in ML, calibration, and omniprediction.