Good Initializations of Variational Bayes for Deep Models. - Simone Rossi 001; Pietro Michiardi; Maurizio Filippone (ICML, 2019)

Efficient Approximate Inference with Walsh-Hadamard Variational Inference. - Simone Rossi 001; Sébastien Marmin; Maurizio Filippone (CoRR, 2019)

Walsh-Hadamard Variational Inference for Bayesian Deep Learning. - Simone Rossi 001; Sébastien Marmin; Maurizio Filippone (NeurIPS, 2020)

Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations. - Simone Rossi 001; Markus Heinonen; Edwin V. Bonilla; Zheyang Shen; Maurizio Filippone (CoRR, 2020)

Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations. - Simone Rossi 001; Markus Heinonen; Edwin V. Bonilla; Zheyang Shen; Maurizio Filippone (AISTATS, 2021)

Model Selection for Bayesian Autoencoders. - Ba-Hien Tran; Simone Rossi 001; Dimitrios Milios; Pietro Michiardi; Edwin V. Bonilla; Maurizio Filippone (NeurIPS, 2021)

All You Need is a Good Functional Prior for Bayesian Deep Learning. - Ba-Hien Tran; Simone Rossi 001; Dimitrios Milios; Maurizio Filippone (J. Mach. Learn. Res., 2022)

Improving Scalability and Inference in Probabilistic Deep Models. (Avancements dans la scalabilité et l'inférence des modèles profonds probabilistes). - Simone Rossi 001 (2022)

Continuous-Time Functional Diffusion Processes. - Giulio Franzese; Giulio Corallo; Simone Rossi 001; Markus Heinonen; Maurizio Filippone; Pietro Michiardi (NeurIPS, 2023)

On permutation symmetries in Bayesian neural network posteriors: a variational perspective. - Simone Rossi 001; Ankit Singh; Thomas Hannagan (NeurIPS, 2023)

Continuous-Time Functional Diffusion Processes. - Giulio Franzese; Simone Rossi 001; Dario Rossi 001; Markus Heinonen; Maurizio Filippone; Pietro Michiardi (CoRR, 2023)

Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning. - Marc Schachtsiek; Simone Rossi 001; Thomas Hannagan (CoRR, 2023)

How Much Is Enough? A Study on Diffusion Times in Score-Based Generative Models. - Giulio Franzese; Simone Rossi 001; Lixuan Yang; Alessandro Finamore; Dario Rossi 001; Maurizio Filippone; Pietro Michiardi (Entropy, 2023)

Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation. - Yixiao Wang; Chen Tang 001; Lingfeng Sun; Simone Rossi 001; Yichen Xie 002; Chensheng Peng; Thomas Hannagan; Stefano Sabatini; Nicola Poerio; Masayoshi Tomizuka; Wei Zhan (ECCV (29), 2024)

From predictions to confidence intervals: an empirical study of conformal prediction methods for in-context learning. - Zhe Huang; Simone Rossi 001; Rui Yuan; Thomas Hannagan (AABI, 2025)

Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL. - Simone Papicchio; Simone Rossi 001; Luca Cagliero; Paolo Papotti (CoRR, 2025)

Scaling Laws for Uncertainty in Deep Learning. - Mattia Rosso; Simone Rossi 001; Giulio Franzese; Markus Heinonen; Maurizio Filippone (CoRR, 2025)