14 citations · 26 across the 11 of their papers we have counts for
11 papers
Structural Adversarial Attacks on Relational Deep Learning under Integrity Constraints
Alan Gany, Bogdan Cautis, Silviu Maniu
Relational Deep Learning (RDL) has become a standard methodology for machine learning on relational databases: the database is encoded as a heterogeneous temporal graph in which tu…
Influence Maximization with Fairness at Scale (Extended Version)
Yuting Feng, Ankitkumar Patel, Bogdan Cautis +1
In this paper, we revisit the problem of influence maximization with fairness, which aims to select k influential nodes to maximise the spread of information in a network, while en…
IGNiteR: News Recommendation in Microblogging Applications (Extended Version)
Yuting Feng, Bogdan Cautis
News recommendation is one of the most challenging tasks in recommender systems, mainly due to the ephemeral relevance of news to users. As social media, and particularly microblog…
Contextual Bandits for Advertising Campaigns: A Diffusion-Model Independent Approach (Extended Version)
Alexandra Iacob, Bogdan Cautis, Silviu Maniu
Motivated by scenarios of information diffusion and advertising in social media, we study an influence maximization problem in which little is assumed to be known about the diffusi…
HADAD: A Lightweight Approach for Optimizing Hybrid Complex Analytics Queries (Extended Version)
Rana Alotaibi, Bogdan Cautis, Alin Deutsch +1
Hybrid complex analytics workloads typically include (i) data management tasks (joins, selections, etc. ), easily expressed using relational algebra (RA)-based languages, and (ii)…
Bandits Under The Influence (Extended Version)
Silviu Maniu, Stratis Ioannidis, Bogdan Cautis
Recommender systems should adapt to user interests as the latter evolve. A prevalent cause for the evolution of user interests is the influence of their social circle. In general,…