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Federico Di Gennaro

2 accepted papers

2026

Hedging on the frontier: Learning new tasks with few samples

ICML 2026spotlight

When a learner is faced with a new task, but is given very few samples, it must leverage any available side-information. In practice, this often comes in the form of benchmarks, where there is abundant data to evaluate model performance on related tasks. Though task relatedness is difficult to forma…

Cited by 0SourceScholar
2025

Instance-Dependent Regret Bounds for Nonstochastic Linear Partial Monitoring

NeurIPS 2025poster

In contrast to the classic formulation of partial monitoring, linear partial monitoring can model infinite outcome spaces, while imposing a linear structure on both the losses and the observations. This setting can be viewed as a generalization of linear bandits where loss and feedback are decoupled…

Cited by 0SourceScholar