Seminar • Algorithms and Complexity • Foundations of Multi-objective Machine Learning

Please note: This seminar will take place in DC 1302 and online.

Nika Haghtalab, Assistant Professor
EECS Department, UC Berkeley

In this talk, I will introduce the framework of multi-objective learning as a modern generalization of statistical learning. This framework that acts as a unifying paradigm for addressing needs such as robustness, collaboration, and fairness aims to optimize a set of 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, such as generalization, introducing technical tools with versatile provable guarantees, and empirical evidence for its performance on existing benchmarks.

To attend this seminar in person, please go to DC 1302. You can also attend virtually on Zoom.