Dimensionality-adaptive k-center in sliding windows. - Paolo Pellizzoni; Andrea Pietracaprina; Geppino Pucci (DSAA, 2020)

k-Center Clustering with Outliers in Sliding Windows. - Paolo Pellizzoni; Andrea Pietracaprina; Geppino Pucci (Algorithms, 2022)

Dimensionality reduction on complex vector spaces for dynamic weighted Euclidean distance. - Paolo Pellizzoni; Francesco Silvestri 001 (CoRR, 2022)

Adaptive k-center and diameter estimation in sliding windows. - Paolo Pellizzoni; Andrea Pietracaprina; Geppino Pucci (Int. J. Data Sci. Anal., 2022)

VC-dimension and Rademacher Averages of Subgraphs, with Applications to Graph Mining. - Paolo Pellizzoni; Fabio Vandin (ICDE, 2023)

FASM and FAST-YB: Significant Pattern Mining with False Discovery Rate Control. - Paolo Pellizzoni; Karsten M. Borgwardt (ICDM, 2023)

Fisher Information Embedding for Node and Graph Learning. - Dexiong Chen; Paolo Pellizzoni; Karsten M. Borgwardt (ICML, 2023)

Higher-order genetic interaction discovery with network-based biological priors. - Paolo Pellizzoni; Giulia Muzio; Karsten M. Borgwardt (Bioinform., 2023)

On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks. - Paolo Pellizzoni; Till Hendrik Schulz; Dexiong Chen; Karsten M. Borgwardt (NeurIPS, 2024)

Structure- and Function-Aware Substitution Matrices via Learnable Graph Matching. - Paolo Pellizzoni; Carlos G. Oliver; Karsten M. Borgwardt (RECOMB, 2024)

Endowing Protein Language Models with Structural Knowledge. - Dexiong Chen; Philip Hartout; Paolo Pellizzoni; Carlos G. Oliver; Karsten M. Borgwardt (CoRR, 2024)

Graph Neural Networks Can (Often) Count Substructures. - Paolo Pellizzoni; Till Hendrik Schulz; Karsten M. Borgwardt (ICLR, 2025)

Fully Dynamic Clustering and Diversity Maximization in Doubling Metrics. - Paolo Pellizzoni; Andrea Pietracaprina; Geppino Pucci (ACM Trans. Knowl. Discov. Data, 2025)