Gianluca Detommaso

16 papers A* 3Journal 11Unranked 2

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Cost-Effective Hallucination Detection for LLMs. CoRR Simon Valentin, Jinmiao Fu, Gianluca Detommaso, Shaoyuan Xu, Giovanni Zappella, Bryan Wang
2024	—	`conf`	Distance-aware Calibration for Pre-trained Language Models. EMNLP (Findings) Alberto Gasparin, Gianluca Detommaso
2024	J	`jnl`	Fortuna: A Library for Uncertainty Quantification in Deep Learning. J. Mach. Learn. Res. Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau
2024	A*	`conf`	Multicalibration for Confidence Scoring in LLMs. ICML Gianluca Detommaso, Martin Bertran Lopez, Riccardo Fogliato, Aaron Roth
2024	J	`jnl`	Multicalibration for Confidence Scoring in LLMs. CoRR Gianluca Detommaso, Martín Bertrán, Riccardo Fogliato, Aaron Roth
2023	—	`conf`	Explaining Multiclass Classifiers with Categorical Values: A Case Study in Radiography. TML4H Luca Franceschi, Cemre Zor, Muhammad Bilal Zafar, Gianluca Detommaso, Cédric Archambeau, Tamas Madl, Michele Donini, Matthias W. Seeger
2023	J	`jnl`	Fortuna: A Library for Uncertainty Quantification in Deep Learning. CoRR Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias W. Seeger, Andrew Gordon Wilson, Cédric Archambeau
2022	J	`jnl`	Uncertainty Calibration in Bayesian Neural Networks via Distance-Aware Priors. CoRR Gianluca Detommaso, Alberto Gasparin, Andrew Wilson, Cédric Archambeau
2021	J	`jnl`	Causal Bias Quantification for Continuous Treatment. CoRR Gianluca Detommaso, Michael Brückner, Philip Schulz, Victor Chernozhukov
2021	A*	`conf`	HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference. AAAI Jakob Kruse, Gianluca Detommaso, Ullrich Köthe, Robert Scheichl
2019	J	`jnl`	Continuous Level Monte Carlo and Sample-Adaptive Model Hierarchies. SIAM/ASA J. Uncertain. Quantification Gianluca Detommaso, Tim J. Dodwell, Robert Scheichl
2019	J	`jnl`	HINT: Hierarchical Invertible Neural Transport for General and Sequential Bayesian inference. CoRR Gianluca Detommaso, Jakob Kruse, Lynton Ardizzone, Carsten Rother, Ullrich Köthe, Robert Scheichl
2019	J	`jnl`	Multilevel Dimension-Independent Likelihood-Informed MCMC for Large-Scale Inverse Problems. CoRR Tiangang Cui, Gianluca Detommaso, Robert Scheichl
2019	J	`jnl`	Stein Variational Online Changepoint Detection with Applications to Hawkes Processes and Neural Networks. CoRR Gianluca Detommaso, Hanne Hoitzing, Tiangang Cui, Ardavan Alamir
2018	A*	`conf`	A Stein variational Newton method. NeurIPS Gianluca Detommaso, Tiangang Cui, Youssef M. Marzouk, Alessio Spantini, Robert Scheichl
2018	J	`jnl`	A Stein variational Newton method. CoRR Gianluca Detommaso, Tiangang Cui, Youssef M. Marzouk, Robert Scheichl, Alessio Spantini