|Position||Senior Research Fellow|
|Themes||General Theory and Methodology, Multivariate and High Dimensional Data.|
Before my current post I was at UCL Mathematics for a few months, and previously I was at UCL Computer Science for a few years, where I did research studies (machine learning) sponsored by DeepMind and in parallel I was a research scientist intern at DeepMind for three years.
Back in the day I studied undergraduate maths (BSc 2000, Pontificia Universidad Católica del Perú) and graduate maths (MSc 2005, PhD 2012, University of Alberta).
I've lived in Peru, in Canada, and now I'm based in the UK.
Machine Learning, Statistical Learning Theory, PAC-Bayes bounds, Probability & Statistics.
- I. Kuzborskij, Cs. Szepesvári, O. Rivasplata, A. Rannen-Triki, R. Pascanu, On the Role of Optimization in Double Descent: A Least Squares Study. NeurIPS 2021. arXiv PDF
- M. Pérez-Ortiz, O. Rivasplata, J. Shawe-Taylor, Cs. Szepesvári, Tighter risk certificates for neural networks. JMLR, 22, 227 (2021), 1-40. PDF / revised PDF / published PDF
- M. Haddouche, B. Guedj, O. Rivasplata, J. Shawe-Taylor, PAC-Bayes unleashed: generalisation bounds with unbounded losses. Entropy, 23, 10 (2021). arXiv PDF published PDF
- L. Orseau, M. Hutter, O. Rivasplata, Logarithmic pruning is all you need. NeurIPS 2020 . PDF
- O. Rivasplata, I. Kuzborskij, Cs. Szepesvári, J. Shawe-Taylor, PAC-Bayes analysis beyond the usual bounds. NeurIPS 2020. PDF
- O. Rivasplata, E. Parrado-Hernández, J. Shawe-Taylor, S. Sun, Cs. Szepesvári, PAC-Bayes bounds for stable algorithms with instance-dependent priors. NeurIPS 2018. PDF
- A.E. Litvak, O. Rivasplata, Smallest singular value of sparse random matrices. Studia Math., 212, 3 (2012), 195-218. PDF
Complete list of publication can be found on the personal webpage.