Search Results for author: Giovanni Zappella

Found 10 papers, 0 papers with code

A Linear Bandit for Seasonal Environments

no code implementations28 Apr 2020 Giuseppe Di Benedetto, Vito Bellini, Giovanni Zappella

Here we present a contextual bandit algorithm which detects and adapts to abrupt changes of the reward function and leverages previous estimations whenever the environment falls back to a previously observed state.

Recommendation Systems

Linear Bandits with Stochastic Delayed Feedback

no code implementations ICML 2020 Claire Vernade, Alexandra Carpentier, Tor Lattimore, Giovanni Zappella, Beyza Ermis, Michael Brueckner

Stochastic linear bandits are a natural and well-studied model for structured exploration/exploitation problems and are widely used in applications such as online marketing and recommendation.

Multi-Armed Bandits

On Context-Dependent Clustering of Bandits

no code implementations ICML 2017 Claudio Gentile, Shuai Li, Purushottam Kar, Alexandros Karatzoglou, Evans Etrue, Giovanni Zappella

We investigate a novel cluster-of-bandit algorithm CAB for collaborative recommendation tasks that implements the underlying feedback sharing mechanism by estimating the neighborhood of users in a context-dependent manner.

Online Clustering of Bandits

no code implementations31 Jan 2014 Claudio Gentile, Shuai Li, Giovanni Zappella

We introduce a novel algorithmic approach to content recommendation based on adaptive clustering of exploration-exploitation ("bandit") strategies.

Online Clustering

A Gang of Bandits

no code implementations NeurIPS 2013 Nicolò Cesa-Bianchi, Claudio Gentile, Giovanni Zappella

Multi-armed bandit problems are receiving a great deal of attention because they adequately formalize the exploration-exploitation trade-offs arising in several industrially relevant applications, such as online advertisement and, more generally, recommendation systems.

Multi-Armed Bandits Recommendation Systems

A Linear Time Active Learning Algorithm for Link Classification

no code implementations NeurIPS 2012 Nicolò Cesa-Bianchi, Claudio Gentile, Fabio Vitale, Giovanni Zappella

We provide a theoretical analysis within this model, showing that we can achieve an optimal (to whithin a constant factor) number of mistakes on any graph $G = (V, E)$ such that $|E|$ is at least order of $|V|^{3/2}$ by querying at most order of $|V|^{3/2}$ edge labels.

Active Learning Classification +1

See the Tree Through the Lines: The Shazoo Algorithm

no code implementations NeurIPS 2011 Fabio Vitale, Nicolò Cesa-Bianchi, Claudio Gentile, Giovanni Zappella

Although it is known how to predict the nodes of an unweighted tree in a nearly optimal way, in the weighted case a fully satisfactory algorithm is not available yet.

Cannot find the paper you are looking for? You can Submit a new open access paper.