Gianluca Detommaso

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Robert ScheichlHeidelberg UniversityVerified email at uni-heidelberg.de
Tiangang CuiUniversity of SydneyVerified email at sydney.edu.au
Alessio SpantiniMassachusetts Institute of TechnologyVerified email at mit.edu
Youssef MarzoukProfessor, Massachusetts Institute of TechnologyVerified email at mit.edu
Ullrich KötheAdjunct Professor of Computer Science, University of HeidelbergVerified email at iwr.uni-heidelberg.de
Jakob KruseVisual Learning Lab, Heidelberg University (HCI/IWR)Verified email at iwr.uni-heidelberg.de
Cédric ArchambeauHelsing, BerlinVerified email at helsing.ai
Alberto GasparinApplied Scientist, AmazonVerified email at amazon.de
Andrew Gordon WilsonNew York UniversityVerified email at cims.nyu.edu
Hanne HoitzingVerified email at imperial.ac.uk
Aaron RothProfessor of Computer Science, University of PennsylvaniaVerified email at cis.upenn.edu
Riccardo FogliatoAmazon Web ServicesVerified email at amazon.com
Martin BertranAmazon AWSVerified email at amazon.com
Michael BrücknerPrincipal Applied Scientist, AmazonVerified email at amazon.de
Victor ChernozhukovProfessor, Department of Economics + Center for Statistics and Data Science, MIT;Verified email at mit.edu

Gianluca Detommaso

AI Research at Helsing | ex-AWS

Verified email at helsing.ai - Homepage

Uncertainty Quantification AI Assurance Causal Inference


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
A Stein variational Newton method G Detommaso, T Cui, A Spantini, Y Marzouk, R Scheichl NeurIPS 2018, 2018	134	2018
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference J Kruse, G Detommaso, R Scheichl, U Köthe AAAI-21, 2019	49*	2019
Continuous level Monte Carlo and sample-adaptive model hierarchies G Detommaso, T Dodwell, R Scheichl SIAM/ASA Journal on Uncertainty Quantification 7 (1), 93-116, 2019	29	2019
Fortuna: A Library for Uncertainty Quantification in Deep Learning G Detommaso, A Gasparin, M Donini, M Seeger, AG Wilson, ... JMLR OSS, 2023	15	2023
Multilevel dimension-independent likelihood-informed MCMC for large-scale inverse problems T Cui, G Detommaso, R Scheichl Inverse Problems 40 (3), 035005, 2024	14*	2024
A data-centric approach to generative modelling for 3D-printed steel TJ Dodwell, LR Fleming, C Buchanan, P Kyvelou, G Detommaso, ... Proceedings of the Royal Society A 477 (2255), 20210444, 2021	14	2021
Stein variational online changepoint detection with applications to Hawkes processes and neural networks G Detommaso, H Hoitzing, T Cui, A Alamir ICML 2019 (Workshop), 2019	9	2019
Multicalibration for confidence scoring in LLMs G Detommaso, M Bertran, R Fogliato, A Roth arXiv preprint arXiv:2404.04689, 2024	5	2024
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors G Detommaso, A Gasparin, A Wilson, C Archambeau arXiv preprint arXiv:2207.08200, 2022	2	2022
Causal Bias Quantification for Continuous Treatments G Detommaso, M Brückner, P Schulz, V Chernozhukov arXiv preprint arXiv:2106.09762, 2021	2	2021
Cost-Effective Hallucination Detection for LLMs S Valentin, J Fu, G Detommaso, S Xu, G Zappella, B Wang arXiv preprint arXiv:2407.21424, 2024	1	2024
Explaining multiclass classifiers with categorical values: A case study in radiography L Franceschi, C Zor, MB Zafar, G Detommaso, C Archambeau, T Madl, ... International Workshop on Trustworthy Machine Learning for Healthcare, 11-24, 2023	1	2023

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