| Tighter risk certificates for neural networks M Pérez-Ortiz, O Rivasplata, J Shawe-Taylor, C Szepesvári Journal of Machine Learning Research 22 (227), 1-40, 2021 | 135 | 2021 |
| Logarithmic pruning is all you need L Orseau, M Hutter, O Rivasplata Advances in Neural Information Processing Systems 33, 2925-2934, 2020 | 116 | 2020 |
| Subgaussian random variables: An expository note O Rivasplata Internet publication, PDF 5, 2012 | 102 | 2012 |
| PAC-Bayes analysis beyond the usual bounds O Rivasplata, I Kuzborskij, C Szepesvári, J Shawe-Taylor Advances in Neural Information Processing Systems 33, 16833-16845, 2020 | 94 | 2020 |
| PAC-Bayes bounds for stable algorithms with instance-dependent priors O Rivasplata, E Parrado-Hernández, JS Shawe-Taylor, S Sun, ... Advances in Neural Information Processing Systems 31, 2018 | 62 | 2018 |
| PAC-Bayes with backprop O Rivasplata, VM Tankasali, C Szepesvari arXiv preprint arXiv:1908.07380, 2019 | 59 | 2019 |
| Smallest singular value of sparse random matrices A Litvak, O Rivasplata Studia Math 212, 195-218, 2012 | 29 | 2012 |
| Learning PAC-Bayes priors for probabilistic neural networks M Perez-Ortiz, O Rivasplata, B Guedj, M Gleeson, J Zhang, ... arXiv preprint arXiv:2109.10304, 2021 | 25 | 2021 |
| Progress in self-certified neural networks M Perez-Ortiz, O Rivasplata, E Parrado-Hernandez, B Guedj, ... arXiv preprint arXiv:2111.07737, 2021 | 19 | 2021 |
| On the role of optimization in double descent: A least squares study I Kuzborskij, C Szepesvári, O Rivasplata, A Rannen-Triki, R Pascanu Advances in Neural Information Processing Systems 34, 29567-29577, 2021 | 15 | 2021 |
| A note on the convergence of denoising diffusion probabilistic models SD Mbacke, O Rivasplata arXiv preprint arXiv:2312.05989, 2023 | 6 | 2023 |
| Meta-Analysis of Bayesian Analyses P Blomstedt, D Mesquita, O Rivasplata, J Lintusaari, T Sivula, J Corander, ... Bayesian Analysis, 2024 | 5* | 2024 |
| Towards Better Visual Explanations for Deep Image Classifiers A Grabska-Barwinska, A Rannen-Triki, O Rivasplata, A György NeurIPS 2020 Workshop eXplainable AI approaches for debugging and diagnosis., 2021 | 4 | 2021 |
| Statistical learning theory: A hitchhiker’s guide J Shawe-Taylor, O Rivasplata NeurIPS 2018, 2018 | 4 | 2018 |
| PAC-Bayesian Computation O Rivasplata University College London, 2022 | 3 | 2022 |
| Towards self-certified learning: Probabilistic neural networks trained by PAC-Bayes with backprop M Pérez-Ortiz, O Rivasplata, J Shawe-Taylor, C Szepesvári NeurIPS 2020 Workshop - Beyond Backpropagation, 2020 | 2 | 2020 |
| Semi-counterfactual risk minimization via neural networks G Aminian, R Vega, O Rivasplata, L Toni, M Rodrigues European Workshop on Reinforcement Learning, 2022 | 1 | 2022 |
| Reversibility for diffusions via quasi-invarience O Rivasplata, J Rychtář, B Schmuland Acta Universitatis Carolinae. Mathematica et Physica 48 (1), 3-10, 2007 | 1 | 2007 |
| A note on generalization bounds for losses with finite moments B Rodríguez-Gálvez, O Rivasplata, R Thobaben, M Skoglund 2024 IEEE International Symposium on Information Theory (ISIT), 2676-2681, 2024 | | 2024 |
| Semi-supervised Batch Learning From Logged Data G Aminian, A Behnamnia, R Vega, L Toni, C Shi, HR Rabiee, ... arXiv preprint arXiv:2209.07148, 2022 | | 2022 |
