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Omar_Portrait


Omar Rivasplata

(No longer at the)
Department of Computing Science
University of Alberta


Email: try or

Here is my Curriculum Vitae
Last updated: not so long ago

And here is my genealogy

News

Look me up in Google Scholar
Also in ResearchGate





Research Interests
Machine Learning.   Artificial Intelligence.   Mathematics.   Probability and Statistics.  

Currently
I am affiliated with the Department of Mathematics and the Department of Computer Science, University College London, where I work on machine learning research at the Centre for Artificial Intelligence.

This field is fascinating! Besides statistical learning I am also interested in other learning frameworks such as online learning and reinforcement learning, and of course deep learning, which is quite popular these days.

It looks that optimization is one pervasive theme in machine learning, though it comes up in such a variety of flavours and colours that it isn't boring. It reminds of the least action principle of Maupertuis, saying that "everything happens as if some quantity was to be made as small as possible." (This principle has lead the optimists to believe that we live in the best possible world.) But just optimization doesn't quite do it for machine learning... to really be talking about learning one has to pay attention to generalization!

Before
I did research studies in machine learning at the Department of Computer Science, University College London, sponsored by DeepMind. In parallel, I was affiliated with DeepMind as a research scientist intern, for three years.

People with whom I have worked at UCL include John Shawe-Taylor, María Pérez-Ortiz, and Benjamin Guedj.

People with whom I have worked at DeepMind include Laurent Orseau, Marcus Hutter, Ilja Kuzborskij, Csaba Szepesvári, András György from my own team; and Amal Rannen-Triki, Agnieszka Grabska-Barwińska, Razvan Pascanu, Geoffrey Irving, Thore Graepel from friend teams.

Much Before
I spent a year at the Department of Computing Science, University of Alberta. During this time I started building my mental model of the machine learning field and fine-tuning my hyperparameters. In theory my host was Rich Sutton but in practice I was developing my research plans together with Csaba Szepesvári.

Much Much Before
I spent some time with Mauricio's group looking at problems related to seismic signal analysis. Before that I was working with Sasha and Nicole on the smallest singular value of a sparse random matrix, using methods from geometric functional analysis and probability. Even before that I worked with Byron on reversibility of a Brownian motion with drift. As an undergrad, with Loretta I worked on a fun project about repeated two-player games with incomplete information on one side.

Talks (sample)
  • Tighter risk certificates for (probabilistic) neural networks. UCL Centre for AI. Slides Video
  • Statistical Learning Theory: A Hitchhiker's Guide. NeurIPS 2018 Tutorial. (with J. Shawe-Taylor) Slides Video

Submitted Papers
  • M. Pérez-Ortiz, O. Rivasplata, B. Guedj, M. Gleeson, J. Zhang, J. Shawe-Taylor, M. Bober, and J. Kittler, Learning PAC-Bayes Priors for Probabilistic Neural Networks. arXiv PDF

Conference & Journal Papers
  • I. Kuzborskij, Cs. Szepesvári, O. Rivasplata, A. Rannen-Triki, R. Pascanu, On the Role of Optimization in Double Descent: A Least Squares Study. arXiv PDF to appear in NeurIPS 2021.
  • M. Haddouche, B. Guedj, O. Rivasplata, J. Shawe-Taylor, PAC-Bayes unleashed: generalisation bounds with unbounded losses. arXiv PDF to appear in Entropy (2021).
  • 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
  • O. Rivasplata, J. Rychtar, B. Schmuland, Reversibility for diffusions via quasi-invariance. Acta Univ. Carolin. Math. Phys., 48, 1 (2007), 3-10. PDF
  • O. Rivasplata, J. Rychtar, C. Sykes, Evolutionary games in finite populations. Pro Mathematica, 20, 39/40 (2006), 147-164. PDF
  • O. Rivasplata, B. Schmuland, Invariant and reversible measures for random walks on Z. Pro Mathematica, 19, 37/38 (2005), 117-124. PDF

Workshop Papers
  • A. Grabska-Barwińska, A. Rannen-Triki, O. Rivasplata, A. György, Towards better visual explanations for deep image classifiers. To appear in NeurIPS 2021 Workshop - eXplainable AI for debugging and diagnosis.
  • M. Pérez-Ortiz, O. Rivasplata, J. Shawe-Taylor, Cs. Szepesvári, Towards self-certified learning: Probabilistic neural networks trained by PAC-Bayes with Backprop. NeurIPS 2020 Workshop - Beyond BackPropagation. PDF
  • O. Rivasplata, I. Kuzborskij, Cs. Szepesvári, J. Shawe-Taylor, PAC-Bayes analysis with stochastic kernels. NeurIPS 2019 Workshop - Machine Learning with Guarantees. PDF

Expository Notes
  • O. Rivasplata, A note on a confidence bound of Kuzborskij and Szepesvári. (2021) PDF
  • O. Rivasplata, Subgaussian random variables: An expository note. (2012) PDF

Undergoing renovations
  • M. Haddouche, B. Guedj, O. Rivasplata, J. Shawe-Taylor, Upper and Lower Bounds on the Performance of Kernel PCA. PDF

Utterly arXiv'ed
  • O. Rivasplata, V. Tankasali, Cs. Szepesvári, PAC-Bayes with Backprop. (2019) PDF


Machine Learning Links
What is machine learning?
A solid reference for Understanding Machine Learning (by Shai Shalev-Shwartz and Shai Ben-David)
Deep Learning book (by Ian Goodfellow and Yoshua Bengio and Aaron Courville)
Rich Sutton's incomplete ideas and Reinforcement Learning book (with Andrew Barto)
Advice for Machine Learning students

Math Links
What's new
Math Seminars (beta).
The complex number operations neatly visualised.
Advice for Math students

Probability Links (accessible with high probability)
Almost Sure and Random
Research in Probability
The Gaussian Processes Website
Advice for Probability students

Writing aids
How to Write Mathematics, tips from the Mathematics Student Handbook at Trent University.
A Guide to Writing Mathematics by Kevin Lee.
Writing Mathematics by Berry & Lawson.
The Underground Grammarian by Richard Mitchell.

Peruvian Links
My birth town is Trujillo, the marinera dance town.
Sometimes people ask me about Machu Picchu, it's a great place to see.
They ask me less about Arequipa though it is also a great place to visit.
Last link, in case you care to know, is about Pisco.