33 citations · 35 across the 10 of their papers we have counts for
11 papers
Virgil: Navigating Explainability for Transformer-based Language Models
Martino Ciaperoni, Sezer Kutluk, Benedetta Muscato +2
Explainability for transformer-based language models is becoming crucial as these systems are deployed in high-stakes applications. As a result, the ecosystem of explainability too…
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…
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…
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…
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…
Workload-Aware Materialization of Junction Trees
Martino Ciaperoni, Cigdem Aslay, Aristides Gionis +1
Bayesian networks are popular probabilistic models that capture the conditional dependencies among a set of variables. Inference in Bayesian networks is a fundamental task for answ…
Relevance of temporal cores for epidemic spread in temporal networks
Martino Ciaperoni, Edoardo Galimberti, Francesco Bonchi +3
Temporal networks are widely used to represent a vast diversity of systems, including in particular social interactions, and the spreading processes unfolding on top of them. The i…
Span-core Decomposition for Temporal Networks: Algorithms and Applications
Edoardo Galimberti, Martino Ciaperoni, Alain Barrat +3
When analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their…