6 papers
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…
Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models
Martino Ciaperoni, Marzio Di Vece, Roberto Pellungrini +3
Large-scale foundation models exhibit behavioral shifts when subjected to interventions such as scaling, fine-tuning, reinforcement learning with human feedback, or in-context lear…
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…
Sample and Expand: Discovering Low-rank Submatrices With Quality Guarantees
Martino Ciaperoni, Aristides Gionis, Heikki Mannila
The problem of approximating a matrix by a low-rank one has been extensively studied. This problem assumes, however, that the whole matrix has a low-rank structure. This assumption…
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…