Emilio Dorigatti

According to our database, Emilio Dorigatti authored at least 14 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

How Inverse Conditional Flows Can Serve as a Substitute for Distributional Regression.

Lucas Kook, Chris Kolb, Philipp Schiele, Daniel Dold, Marcel Arpogaus, Cornelius Fritz, Philipp F. M. Baumann, Philipp Kopper, Tobias Pielok, Emilio Dorigatti, David Rügamer

CoRR, 2024

2023

Approximate Bayes Optimal Pseudo-Label Selection.

Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin

CoRR, 2023

Approximately Bayes-optimal pseudo-label selection.

Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Neural Architecture Search for Genomic Sequence Data.

Amadeu Scheppach, Hüseyin Anil Gündüz, Emilio Dorigatti, Philipp C. Münch, Alice C. McHardy, Bernd Bischl, Mina Rezaei, Martin Binder

Proceedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2023

Frequentist Uncertainty Quantification in Semi-Structured Neural Networks.

Emilio Dorigatti, Benjamin Schubert, Bernd Bischl, David Rügamer

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Improved proteasomal cleavage prediction with positive-unlabeled learning.

Emilio Dorigatti, Bernd Bischl, Benjamin Schubert

CoRR, 2022

Joint Debiased Representation and Image Clustering Learning with Self-Supervision.

Shunjie-Fabian Zheng, JaeEun Nam, Emilio Dorigatti, Bernd Bischl, Shekoofeh Azizi, Mina Rezaei

CoRR, 2022

Robust and Efficient Imbalanced Positive-Unlabeled Learning with Self-supervision.

Emilio Dorigatti, Jonas Schweisthal, Bernd Bischl, Mina Rezaei

CoRR, 2022

Positive-Unlabeled Learning with Uncertainty-aware Pseudo-label Selection.

Emilio Dorigatti, Jann Goschenhofer, Benjamin Schubert, Mina Rezaei, Bernd Bischl

CoRR, 2022

Joint Debiased Representation Learning and Imbalanced Data Clustering.

Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl

Proceedings of the IEEE International Conference on Data Mining Workshops, 2022

2021

Learning Statistical Representation with Joint Deep Embedded Clustering.

Mina Rezaei, Emilio Dorigatti, David Rügamer, Bernd Bischl

CoRR, 2021

Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany.

Cornelius Fritz, Emilio Dorigatti, David Rügamer

CoRR, 2021

2020

Graph-theoretical formulation of the generalized epitope-based vaccine design problem.

Emilio Dorigatti, Benjamin Schubert

PLoS Comput. Biol., 2020

2018

N-ary relation extraction for simultaneous T-Box and A-Box knowledge base augmentation.

Marco Fossati, Emilio Dorigatti, Claudio Giuliano

Semantic Web, 2018