Abstract Multi-distribution learning (MDL), which seeks to learn a shared model that
minimizes the worst-case risk across $ k $ distinct data distributions, has emerged as a …

Multi-distribution learning generalizes the classic PAC learning to handle data coming from
multiple distributions. Given a set of $ k $ data distributions and a hypothesis class of VC …

Collaboration is crucial for reaching collective goals. However, its potential for effectiveness
is often undermined by the strategic behavior of individual agents—a fact that is captured by …

Multi-distribution or collaborative learning involves learning a single predictor that works
well across multiple data distributions, using samples from each during training. Recent …

Multi-objective optimization (MOO) in deep learning aims to simultaneously optimize
multiple conflicting objectives, a challenge frequently encountered in areas like multi-task …

To test scientific theories and develop individualized treatment rules, researchers often wish
to learn heterogeneous treatment effects that can be consistently found across diverse …

The estimation of cumulative distribution functions (CDFs) is an important learning task with
a great variety of downstream applications, such as risk assessments in predictions and …

To address the needs of modeling uncertainty in sensitive machine learning applications,
the setup of distributionally robust optimization (DRO) seeks good performance uniformly …

The minimax sample complexity of group distributionally robust optimization (GDRO) has
been determined up to a $\log (K) $ factor, for $ K $ the number of groups. In this work, we …