activity
20092022
most citedInterpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking

122 citations · 160 across the 8 of their papers we have counts for

collaborators

9 papers

stat.ML20223 cited

Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders

Olivier Jeunen, Ciarán M. Gilligan-Lee, Rishabh Mehrotra +1

The ability to answer causal questions is crucial in many domains, as causal inference allows one to understand the impact of interventions. In many applications, only a single int…

cs.IR20228 cited

Mostra: A Flexible Balancing Framework to Trade-off User, Artist and Platform Objectives for Music Sequencing

Emanuele Bugliarello, Rishabh Mehrotra, James Kirk +1

We consider the task of sequencing tracks on music streaming platforms where the goal is to maximise not only user satisfaction, but also artist- and platform-centric objectives, n…

stat.ML20211 cited

Model Selection for Production System via Automated Online Experiments

Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia +2

A challenge that machine learning practitioners in the industry face is the task of selecting the best model to deploy in production. As a model is often an intermediate component…

cs.SI202018 cited

Every Colour You Are: Stance Prediction and Turnaround in Controversial Issues

Eduardo Graells-Garrido, Ricardo Baeza-Yates, Mounia Lalmas

Web platforms have allowed political manifestation and debate for decades. Technology changes have brought new opportunities for expression, and the availability of longitudinal da…

cs.SI2018

Quantifying Biases in Online Information Exposure

Dimitar Nikolov, Mounia Lalmas, Alessandro Flammini +1

Our consumption of online information is mediated by filtering, ranking, and recommendation algorithms that introduce unintentional biases as they attempt to deliver relevant and e…

stat.ML2017122 cited

Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking

Gabriele Tolomei, Fabrizio Silvestri, Andrew Haines +1

Machine-learned models are often described as "black boxes". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretabil…