The greatest challenge to any thinker is stating the problem in a way that will allow a solution.

Bertrand Russell [England, 1902].
My name is Carlos Riquelme. This is my personal website.
About Me
I'm currently a research scientist at Google Brain working on Artificial Intelligence.
Previously, I completed my PhD in statistical machine learning at Stanford.

Reinforcement Learning, Machine Learning, Unsupervised Learning.
Algorithms, Probability, Statistics, Optimization, Information Theory.

I was really lucky to have Ramesh Johari as my advisor at Stanford.
At Oxford, I did work on Probabilistic Combinatorics supervised by Oliver Riordan.
I really enjoyed long discussions with Dragan Vukotic about Functional Analysis.
Thanks! Thanks! Thanks!

Machine Learning + Data Science @ Google, Facebook, Twitter, Quora, Adobe.

Sven Schmit, Mohammad Ghavamzadeh, Alessandro Lazaric, Austin Benson, George Tucker, Matt Johnson, Matt Hoffman, Jasper Snoek, Baosen Zhang, Sid Banerjee, David Walsh, Ilya Tolstikhin, Josip Djolonga.

If you can dream it, you can do it.

Walt Disney.

please, feel free to contact me at     rikelhood AT gmail DOT com.


  1. Practical and Consistent Estimation of f-Divergences.
    Paul Rubenstein, Olivier Bousquet, Josip Djolonga, Carlos Riquelme, Ilya Tolstikhin.
    Under review.
  2. Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates.
    Hugo Penedones, Carlos Riquelme, Damien Vincent, Hartmut Maennel, Timothy Mann, Andre Barreto, Sylvain Gelly, Gergely Neu.
    Under review.
  3. Google Research Football: A Novel Reinforcement Learning Environment.
    Work in progress; with Karol Kurach, Anton Raichuk, Piotr Stanczyk, Michal Zajac, Olivier Bachem, Lasse Espeholt, Damien Vincent, Marcin Michalski, Olivier Bousquet, Sylvain Gelly.
    Under review. Paper pre-print
  4. Exploration for Deep Reinforcement Learning.
    Work in progress; with Gergely Neu and Nikita Zhivotovskii.
  5. Failure Modes of Variational Inference for Decision Making.
    Work in progress; with Matthew Hoffman and Matthew Johnson.
    ICML 2018, Workshop on Prediction and Generative Modeling in Reinforcement Learning.
  6. Deep Bayesian Bandits Showdown.
    Riquelme, Tucker, Snoek | ICLR 2018.
    Code: tensorflow/models/research/deep_contextual_bandits
  7. The Beta-VAE's Implicit Prior.
    Work in progress; with Matthew Hoffman and Matthew Johnson.
    NIPS 2017, Bayesian Deep Learning Workshop.
    Link to the paper.
  8. Active Learning for Accurate Estimation of Linear Models.
    Riquelme, Ghavamzadeh, Lazaric | ICML 2017.
  9. Human Interaction with Recommendation Systems: On Bias and Exploration.
    Schmit, Riquelme | AISTATS 2018.
  10. Online Active Linear Regression via Thresholding.
    Riquelme, Johari, Zhang | AAAI 2017.
  11. Experimentation under Non-Stationarity by Multitask Bayesian Optimization.
    Work in progress; with Eytan Bakshy and Ben Letham.
  12. Pricing Ride-Share Platforms: A Queueing-Theoretic Approach.
    Banerjee, Johari, Riquelme | EC 2015.
  13. Learning Multifractal Structure in Large Networks.
    Benson, Riquelme, Schmit | KDD 2014.
  14. On the Chromatic Number of Random Graphs.
    Riquelme | Masters Thesis at the University of Oxford, 2012.

What I like most about Madrid is that it always smiles at you, and smiling is contagious.