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cs.LG2026
Active Learning on Adversarially Corrupted Graphs
Marco Bressan, Nicolò Cesa-Bianchi, Tommaso d`Orsi +2
Motivated by real-world scenarios where malicious entities tamper with existing networks, we define a model where an adversary seeks to hide a set of \emph{corrupted vertices} insi…
cs.LG2026
Learning Conditional Averages
Marco Bressan, Nataly Brukhim, Nicolo Cesa-Bianchi +4
We introduce the problem of learning conditional averages in the PAC framework. The learner receives a sample labeled by an unknown target concept from a known concept class, as in…