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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…

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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…

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