Paolo Pellizzoni | Max Planck Institute of Biochemistry

About Me

I am a Ph.D. student in computer science at the Max Planck Institute of Biochemistry (previously at ETH Zürich as Scientific Assistant). I am supervised by Prof. K. Borgwardt.

Research Interests

Theoretical Computer Science: approximation algorithms, sampling theory
Machine Learning and Data Mining: statistical learning theory, pattern mining, clustering, graph learning
Bioinformatics: genetic marker discovery

Publications

Paolo Pellizzoni, T. Schulz, D. Chen and K. Borgwardt. On the Expressivity and Sample Complexity of Node-Individualized Graph Neural Networks, in NeurIPS, 2024.
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Paolo Pellizzoni, C. Oliver and K. Borgwardt. “Structure- and function-aware substitution matrices via learnable graph matching”, in RECOMB, 2024.
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Paolo Pellizzoni and K. Borgwardt. “FASM and Fast-YB: Significant Pattern Mining with False Discovery Rate Control”, in IEEE ICDM, 2023.
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D. Chen^*, Paolo Pellizzoni^*, and K. Borgwardt. “Fisher Information Embedding for Node and Graph Learning”, in ICML, 2023.
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Paolo Pellizzoni, G. Muzio, and K. Borgwardt. “Higher-order genetic interaction discovery with network-based biological priors”, in ISMB, 2023.
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Paolo Pellizzoni, A. Pietracaprina, and G. Pucci. “Fully Dynamic Clustering and Diversity Maximization in Doubling Metrics”, in WADS, 2023.
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Paolo Pellizzoni and F. Vandin. “VC-dimension and Rademacher Averages of Subgraphs, with Applications to Graph Mining”, in IEEE ICDE, 2023.
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Paolo Pellizzoni, A. Pietracaprina and G. Pucci. “Adaptive k-center and diameter estimation in sliding windows”, International Journal of Data Science and Analytics, 2022.
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Paolo Pellizzoni, A. Pietracaprina, and G. Pucci. “k-Center Clustering with Outliers in Sliding Windows”, Algorithms, vol. 15, no. 2, 2022.
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Paolo Pellizzoni, A. Pietracaprina, and G. Pucci. “Dimensionality-adaptive k-center in sliding windows”, in IEEE DSAA, 2020.
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