Pontil, Massimiliano

Quantum machine learning: a classical perspective

Ciliberto, C., Herbster, M., Ialongo, A. D., Pontil, M., Rocchetto, A., Severini, S., & Wossnig, L. (2018) · Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 518

Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

Lise, S., Archambeau, C., Pontil, M., & Jones, D. T. (2009) · BMC Bioinformatics

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 107

Taking Advantage of Multitask Learning for Fair Classification

Oneto, L., Doninini, M., Elders, A., & Pontil, M. (2019) · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society

Ethics and Social Impacts of AIPrivacy-Preserving Technologies in Data

Cited by 64

Fair Regression with Wasserstein Barycenters

Chzhen, E., Denis, C., Hebiri, M., Oneto, L., & Pontil, M. (2020) · arXiv

Ethics and Social Impacts of AIPrivacy-Preserving Technologies in Data

Cited by 46

Combining Heterogeneous Data Sources for Neuroimaging Based Diagnosis: Re-Weighting and Selecting What Is Important

Donini, M., Monteiro, J. M., Pontil, M., Hahn, T., Fallgatter, A. J., Shawe-Taylor, J., & Mourão-Miranda, J. (2019) · NeuroImage

Dementia and Cognitive Impairment ResearchFunctional Brain Connectivity Studies

Cited by 19

Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues

Grazzi, R., Siems, J., Zela, A., Franke, J. K. H., Hutter, F., & Pontil, M. (2024) · arXiv

Neural Networks and ApplicationsTopic Modeling

Cited by 1