Gianluca Detommaso

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Robert ScheichlHeidelberg University在 uni-heidelberg.de 的電子郵件地址已通過驗證
Tiangang CuiUniversity of Sydney在 sydney.edu.au 的電子郵件地址已通過驗證
Alessio SpantiniMassachusetts Institute of Technology在 mit.edu 的電子郵件地址已通過驗證
Youssef MarzoukProfessor, Massachusetts Institute of Technology在 mit.edu 的電子郵件地址已通過驗證
Ullrich KötheAdjunct Professor of Computer Science, University of Heidelberg在 iwr.uni-heidelberg.de 的電子郵件地址已通過驗證
Jakob KruseVisual Learning Lab, Heidelberg University (HCI/IWR)在 iwr.uni-heidelberg.de 的電子郵件地址已通過驗證
Cédric ArchambeauDirector of AI at Helsing. Ex Principal Applied Scientist at AWS.在 helsing.ai 的電子郵件地址已通過驗證
Alberto GasparinApplied Scientist, Amazon在 amazon.de 的電子郵件地址已通過驗證
Andrew Gordon WilsonNew York University在 cims.nyu.edu 的電子郵件地址已通過驗證
Hanne Hoitzing在 imperial.ac.uk 的電子郵件地址已通過驗證
Aaron RothProfessor of Computer Science, University of Pennsylvania在 cis.upenn.edu 的電子郵件地址已通過驗證
Riccardo FogliatoAmazon Web Services在 amazon.com 的電子郵件地址已通過驗證
Martin BertranAmazon AWS在 amazon.com 的電子郵件地址已通過驗證
Michael BrücknerPrincipal Applied Scientist, Amazon在 amazon.de 的電子郵件地址已通過驗證
Victor ChernozhukovProfessor, Department of Economics + Center for Statistics and Data Science, MIT;在 mit.edu 的電子郵件地址已通過驗證

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Gianluca Detommaso

AI Research at Helsing | ex-AWS

在 helsing.ai 的電子郵件地址已通過驗證 - 首頁

Uncertainty Quantification AI Assurance Causal Inference


標題按引用次數排序按年份排序按標題排序	引用次數引用次數	年份
A Stein variational Newton method G Detommaso, T Cui, A Spantini, Y Marzouk, R Scheichl NeurIPS 2018, 2018	139	2018
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference J Kruse, G Detommaso, R Scheichl, U Köthe AAAI-21, 2019	51*	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	18	2023
Multilevel dimension-independent likelihood-informed MCMC for large-scale inverse problems T Cui, G Detommaso, R Scheichl Inverse Problems 40 (3), 035005, 2024	15*	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	15	2021
Multicalibration for confidence scoring in LLMs G Detommaso, M Bertran, R Fogliato, A Roth arXiv preprint arXiv:2404.04689, 2024	9	2024
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
Cost-effective hallucination detection for llms S Valentin, J Fu, G Detommaso, S Xu, G Zappella, B Wang arXiv preprint arXiv:2407.21424, 2024	4	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	3	2022
Causal Bias Quantification for Continuous Treatments G Detommaso, M Brückner, P Schulz, V Chernozhukov arXiv preprint arXiv:2106.09762, 2021	2	2021
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
Distance-aware Calibration for Pre-trained Language Models A Gasparin, G Detommaso Findings of the Association for Computational Linguistics: EMNLP 2024, 12434 …, 2024		2024
Model Cautiousness: Towards Safer Deployment in Critical Domains G Detommaso

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