Riccardo Barbano

28 papers A* 3B 1Journal 23Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	CMAD: Cooperative Multi-Agent Diffusion via Stochastic Optimal Control. CoRR Riccardo Barbano, Alexander Denker, Zeljko Kereta, Runchang Li, Francisco Vargas
2026	J	`jnl`	Solving Inverse Problems with Flow-based Models via Model Predictive Control. CoRR George Webber, Alexander Denker, Riccardo Barbano, Andrew J. Reader
2025	J	`jnl`	Best practices for multi-fidelity Bayesian optimization in materials and molecular research. Nat. Comput. Sci. Victor Sabanza Gil, Riccardo Barbano, Daniel Pacheco Gutiérrez, Jeremy S. Luterbacher, José Miguel Hernández-Lobato, Philippe Schwaller, Loïc Roch
2025	J	`jnl`	Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction. IEEE Trans. Medical Imaging Riccardo Barbano, Alexander Denker, Hyungjin Chung, Tae-Hoon Roh, Simon R. Arridge, Peter Maass, Bangti Jin, Jong Chul Ye
2024	J	`jnl`	Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research. CoRR Victor Sabanza Gil, Riccardo Barbano, Daniel Pacheco Gutiérrez, Jeremy S. Luterbacher, José Miguel Hernández-Lobato, Philippe Schwaller, Loïc Roch
2024	A*	`conf`	DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform. NeurIPS Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Riccardo Barbano, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lió
2024	J	`jnl`	DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform. CoRR Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio
2024	J	`jnl`	Image Reconstruction via Deep Image Prior Subspaces. Trans. Mach. Learn. Res. Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin, Zeljko Kereta
2023	J	`jnl`	Fast and Painless Image Reconstruction in Deep Image Prior Subspaces. CoRR Riccardo Barbano, Javier Antorán, Johannes Leuschner, José Miguel Hernández-Lobato, Zeljko Kereta, Bangti Jin
2023	—	`conf`	SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction. MIDL Marco Nittscher, Michael Falk Lameter, Riccardo Barbano, Johannes Leuschner, Bangti Jin, Peter Maass
2023	J	`jnl`	SVD-DIP: Overcoming the Overfitting Problem in DIP-based CT Reconstruction. CoRR Marco Nittscher, Michael Lameter, Riccardo Barbano, Johannes Leuschner, Bangti Jin, Peter Maass
2023	A*	`conf`	Sampling-based inference for large linear models, with application to linearised Laplace. ICLR Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
2023	J	`jnl`	Score-Based Generative Models for PET Image Reconstruction. CoRR Imraj RD Singh, Alexander Denker, Riccardo Barbano, Zeljko Kereta, Bangti Jin, Kris Thielemans, Peter Maass, Simon R. Arridge
2023	J	`jnl`	Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Imaging Inverse Problems. CoRR Riccardo Barbano, Alexander Denker, Hyungjin Chung, Tae-Hoon Roh, Simon Arrdige, Peter Maass, Bangti Jin, Jong Chul Ye
2023	J	`jnl`	Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior. Trans. Mach. Learn. Res. Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin
2022	J	`jnl`	A Probabilistic Deep Image Prior for Computational Tomography. CoRR Javier Antorán, Riccardo Barbano, Johannes Leuschner, José Miguel Hernández-Lobato, Bangti Jin
2022	A*	`conf`	Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. ICML Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
2022	J	`jnl`	Adapting the Linearised Laplace Model Evidence for Modern Deep Learning. CoRR Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato
2022	J	`jnl`	An Educated Warm Start for Deep Image Prior-Based Micro CT Reconstruction. IEEE Trans. Computational Imaging Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt, Alexander Denker, Andreas Hauptmann, Peter Maass, Bangti Jin
2022	J	`jnl`	Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior. CoRR Riccardo Barbano, Johannes Leuschner, Javier Antorán, Bangti Jin, José Miguel Hernández-Lobato
2022	J	`jnl`	Sampling-based inference for large linear models, with application to linearised Laplace. CoRR Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
2021	J	`jnl`	Conditional Variational Autoencoder for Learned Image Reconstruction. Comput. Chen Zhang, Riccardo Barbano, Bangti Jin
2021	J	`jnl`	Conditional Variational Autoencoder for Learned Image Reconstruction. CoRR Chen Zhang, Riccardo Barbano, Bangti Jin
2021	J	`jnl`	Is Deep Image Prior in Need of a Good Education? CoRR Riccardo Barbano, Johannes Leuschner, Maximilian Schmidt, Alexander Denker, Andreas Hauptmann, Peter Maaß, Bangti Jin
2021	J	`jnl`	Unsupervised Knowledge-Transfer for Learned Image Reconstruction. CoRR Riccardo Barbano, Zeljko Kereta, Andreas Hauptmann, Simon R. Arridge, Bangti Jin
2020	B	`conf`	Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent. ICPR Riccardo Barbano, Chen Zhang, Simon R. Arridge, Bangti Jin
2020	J	`jnl`	Quantifying Model Uncertainty in Inverse Problems via Bayesian Deep Gradient Descent. CoRR Riccardo Barbano, Chen Zhang, Simon R. Arridge, Bangti Jin
2020	J	`jnl`	Quantifying Sources of Uncertainty in Deep Learning-Based Image Reconstruction. CoRR Riccardo Barbano, Zeljko Kereta, Chen Zhang, Andreas Hauptmann, Simon R. Arridge, Bangti Jin