activity
20112026
most citedBatch Active Learning at Scale

47 citations · 199 across the 25 of their papers we have counts for

collaborators
Showing 2023Show all

6 papers · 1 filter

cs.CR2023★ 1 cited

A New Mathematical Optimization-Based Method for the m-invariance Problem

Adrian Tobar, Jordi Castro, Claudio Gentile

The issue of ensuring privacy for users who share their personal information has been a growing priority in a business and scientific environment where the use of different types o…

cs.LG2023★ 4 cited

Fast and Effective GNN Training through Sequences of Random Path Graphs

Francesco Bonchi, Claudio Gentile, Francesco Paolo Nerini +2

We present GERN, a novel scalable framework for training GNNs in node classification tasks, based on effective resistance, a standard tool in spectral graph theory. Our method prog…

cs.LG2023

Data-Driven Online Model Selection With Regret Guarantees

Aldo Pacchiano, Christoph Dann, Claudio Gentile

We consider model selection for sequential decision making in stochastic environments with bandit feedback, where a meta-learner has at its disposal a pool of base learners, and de…

cs.LG2023★ 1 cited

Easy Learning from Label Proportions

Robert Istvan Busa-Fekete, Heejin Choi, Travis Dick +2

We consider the problem of Learning from Label Proportions (LLP), a weakly supervised classification setup where instances are grouped into "bags", and only the frequency of class…

cs.LG2023

Leveraging User-Triggered Supervision in Contextual Bandits

Alekh Agarwal, Claudio Gentile, Teodor V. Marinov

We study contextual bandit (CB) problems, where the user can sometimes respond with the best action in a given context. Such an interaction arises, for example, in text prediction…

cs.LG2023

Adversarial Online Collaborative Filtering

Stephen Pasteris, Fabio Vitale, Mark Herbster +2

We investigate the problem of online collaborative filtering under no-repetition constraints, whereby users need to be served content in an online fashion and a given user cannot b…