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Gianluca Detommaso
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2020 – today
- 2024
- [j2]Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau:
Fortuna: A Library for Uncertainty Quantification in Deep Learning. J. Mach. Learn. Res. 25: 238:1-238:7 (2024) - [c4]Gianluca Detommaso, Martin Bertran Lopez, Riccardo Fogliato, Aaron Roth:
Multicalibration for Confidence Scoring in LLMs. ICML 2024 - [i9]Gianluca Detommaso, Martín Bertrán, Riccardo Fogliato, Aaron Roth:
Multicalibration for Confidence Scoring in LLMs. CoRR abs/2404.04689 (2024) - [i8]Simon Valentin, Jinmiao Fu, Gianluca Detommaso, Shaoyuan Xu, Giovanni Zappella, Bryan Wang:
Cost-Effective Hallucination Detection for LLMs. CoRR abs/2407.21424 (2024) - 2023
- [c3]Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cédric Archambeau, Tamas Madl, Michele Donini, Matthias W. Seeger:
Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography. TML4H 2023: 11-24 - [i7]Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau:
Fortuna: A Library for Uncertainty Quantification in Deep Learning. CoRR abs/2302.04019 (2023) - 2022
- [i6]Gianluca Detommaso, Alberto Gasparin, Andrew Wilson, Cédric Archambeau:
Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors. CoRR abs/2207.08200 (2022) - 2021
- [c2]Jakob Kruse, Gianluca Detommaso, Ullrich Köthe, Robert Scheichl:
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference. AAAI 2021: 8191-8199 - [i5]Gianluca Detommaso, Michael Brückner, Philip Schulz, Victor Chernozhukov:
Causal Bias Quantification for Continuous Treatment. CoRR abs/2106.09762 (2021)
2010 – 2019
- 2019
- [j1]Gianluca Detommaso, Tim J. Dodwell, Robert Scheichl:
Continuous Level Monte Carlo and Sample-Adaptive Model Hierarchies. SIAM/ASA J. Uncertain. Quantification 7(1): 93-116 (2019) - [i4]Gianluca Detommaso, Hanne Hoitzing, Tiangang Cui, Ardavan Alamir:
Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks. CoRR abs/1901.07987 (2019) - [i3]Gianluca Detommaso, Jakob Kruse, Lynton Ardizzone, Carsten Rother, Ullrich Köthe, Robert Scheichl:
HINT: Hierarchical Invertible Neural Transport for General and Sequential Bayesian inference. CoRR abs/1905.10687 (2019) - [i2]Tiangang Cui, Gianluca Detommaso, Robert Scheichl:
Multilevel Dimension-Independent Likelihood-Informed MCMC for Large-Scale Inverse Problems. CoRR abs/1910.12431 (2019) - 2018
- [c1]Gianluca Detommaso, Tiangang Cui, Youssef M. Marzouk, Alessio Spantini, Robert Scheichl:
A Stein variational Newton method. NeurIPS 2018: 9187-9197 - [i1]Gianluca Detommaso, Tiangang Cui, Youssef M. Marzouk, Robert Scheichl, Alessio Spantini:
A Stein variational Newton method. CoRR abs/1806.03085 (2018)
Coauthor Index
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last updated on 2024-09-18 01:08 CEST by the dblp team
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