33 citations · 35 across the 10 of their papers we have counts for
5 papers · 1 filter
Explainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature Attributions
Martino Ciaperoni, Margherita Lalli, Simone Piaggesi +6
Predicting cancer drug response from transcriptomic profiles is a cornerstone of precision oncology, yet the scientific value of machine learning models hinges not solely on predic…
Khatri-Rao Clustering for Data Summarization
Martino Ciaperoni, Collin Leiber, Aristides Gionis +1
As datasets continue to grow in size and complexity, finding succinct yet accurate data summaries poses a key challenge. Centroid-based clustering, a widely adopted approach to add…
Fair PCA, One Component at a Time
Antonis Matakos, Martino Ciaperoni, Heikki Mannila
The Min-Max Fair PCA problem seeks a low-rank representation of multi-group data such that the the approximation error is as balanced as possible across groups. Existing approaches…
Efficient Exploration of the Rashomon Set of Rule Set Models
Martino Ciaperoni, Han Xiao, Aristides Gionis
Today, as increasingly complex predictive models are developed, simple rule sets remain a crucial tool to obtain interpretable predictions and drive high-stakes decision making. Ho…
Concise and interpretable multi-label rule sets
Martino Ciaperoni, Han Xiao, Aristides Gionis
Multi-label classification is becoming increasingly ubiquitous, but not much attention has been paid to interpretability. In this paper, we develop a multi-label classifier that ca…