PAC-Bayes bounds for stable algorithms with instance-dependent priors. - Omar Rivasplata; Csaba Szepesvári; John Shawe-Taylor; Emilio Parrado-Hernández; Shiliang Sun (NeurIPS, 2018)

PAC-Bayes with Backprop. - Omar Rivasplata; Vikram M. Tankasali; Csaba Szepesvári (CoRR, 2019)

Logarithmic Pruning is All You Need. - Laurent Orseau; Marcus Hutter; Omar Rivasplata (NeurIPS, 2020)

PAC-Bayes Analysis Beyond the Usual Bounds. - Omar Rivasplata; Ilja Kuzborskij; Csaba Szepesvári; John Shawe-Taylor (NeurIPS, 2020)

Upper and Lower Bounds on the Performance of Kernel PCA. - Maxime Haddouche; Benjamin Guedj; Omar Rivasplata; John Shawe-Taylor (CoRR, 2020)

On the Role of Optimization in Double Descent: A Least Squares Study. - Ilja Kuzborskij; Csaba Szepesvári; Omar Rivasplata; Amal Rannen-Triki; Razvan Pascanu (NeurIPS, 2021)

Learning PAC-Bayes Priors for Probabilistic Neural Networks. - María Pérez-Ortiz 001; Omar Rivasplata; Benjamin Guedj; Matthew Gleeson; Jingyu Zhang; John Shawe-Taylor; Miroslaw Bober; Josef Kittler (CoRR, 2021)

Progress in Self-Certified Neural Networks. - María Pérez-Ortiz 001; Omar Rivasplata; Emilio Parrado-Hernández; Benjamin Guedj; John Shawe-Taylor (CoRR, 2021)

PAC-Bayes Unleashed: Generalisation Bounds with Unbounded Losses. - Maxime Haddouche; Benjamin Guedj; Omar Rivasplata; John Shawe-Taylor (Entropy, 2021)

Tighter Risk Certificates for Neural Networks. - María Pérez-Ortiz 001; Omar Rivasplata; John Shawe-Taylor; Csaba Szepesvári (J. Mach. Learn. Res., 2021)

Semi-Counterfactual Risk Minimization Via Neural Networks. - Gholamali Aminian; Roberto Vega; Omar Rivasplata; Laura Toni; Miguel R. D. Rodrigues (CoRR, 2022)

A Note on Generalization Bounds for Losses with Finite Moments. - Borja Rodríguez Gálvez; Omar Rivasplata; Ragnar Thobaben; Mikael Skoglund (ISIT, 2024)

A Note on the Convergence of Denoising Diffusion Probabilistic Models. - Sokhna Diarra Mbacke; Omar Rivasplata (Trans. Mach. Learn. Res., 2024)

Generalization and Distributed Learning of GFlowNets. - Tiago Silva; Amauri H. Souza; Omar Rivasplata; Vikas Garg 001; Samuel Kaski; Diego Mesquita (ICLR, 2025)

Regularized Gradient Clipping Provably Trains Wide and Deep Neural Networks. - Matteo Tucat; Anirbit Mukherjee; Procheta Sen; Mingfei Sun 001; Omar Rivasplata (Trans. Mach. Learn. Res., 2025)

Semi-pessimistic Reinforcement Learning. - Jin Zhu; Xin Zhou; Jiaang Yao; Gholamali Aminian; Omar Rivasplata; Simon Little; Lexin Li; Chengchun Shi (CoRR, 2025)