Borja de Balle Pigem

Research Scientist @ DeepMind

From April 2017 until May 2019 I was senior machine learning scientist in Neil Lawrence's team at Amazon (Cambridge, UK). Before that I was a lecturer (≈ assistant professor) at Lancaster University affiliated with the Department of Mathematics and Statistics and the Data Science Institute. From October 2013 until September 2015 I was a post-doctoral fellow in the Reasoning and Learning Laboratory at McGill University, where I worked with Prakash Panangaden, Joelle Pineau, and Doina Precup. I obtained my PhD in 2013 from UPC after working in the LARCA research group under the supervision of Jorge Castro and Ricard Gavaldà. During my PhD I spent several months visiting Mehryar Mohri at the Courant Institute (NYU).


borja /dot/ balle /at/ gmail /dot/ theusual

My research interests revolve around all aspects of Machine Learning: theory, algorithms, and applications.

Currently I focus on the foundations of privacy-preserving data analysis, including Differential Privacy and Private Multi-Party Machine Learning.

In the past I worked on scalable spectral algorithms for learning latent-variable models inspired by Language Theory and Dynamical Systems, and motivated by applications in Natural Language Processing and Reinforcement Learning.

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The Privacy Blanket of the Shuffle Model
Privacy and the Science of Data Analysis, Simons Institute, April 2019

Learning the Privacy-Utility Trade-off with Bayesian Optimization
Data Privacy: From Foundations to Applications, Simons Institute, March 2019

Automata Learning
Summer School on Foundations of Programming and Software Systems, July 2018

Singular Value Automata and Approximate Minimization
Weighted Automata: Theory and Applications, May 2018

A Short Tutorial on Differential Privacy
The Alan Turing Institute, January 2018

Learning Automata with Hankel Matrices
Logic and Learning Workshop, The Alan Turing Institute, January 2018

Theoretical Guarantees for Learning Weighted Automata
International Conference on Grammatical Inference (ICGI), October 2016

Tutorial on (Co-)Algebraic and Analytical Aspects of Weighted Automata Minimisation and Equivalence
Coalgebraic Methods in Computer Science (CMCS), April 2016
(Presented jointly with Alexandra Silva)

Tutorial on Spectral Learning Techniques for Weighted Automata, Transducers, and Grammars
Empirical Methods in Natural Language Processing (EMNLP), October 2014
(Presented jointly with Ariadna Quattoni and Xavier Carreras)

Area Chair / Senior PC   —   NeurIPS 2019, ICML 2019, AISTATS 2019, NeurIPS 2018, NIPS 2014

Workshops Chair (with Marco Cuturi)   —   NIPS 2015

Steering Committe   —   International Conference on Grammatical Inference (since 2016)

Workshop Organizer   —   Privacy-Preserving Machine Learning (PPML'19) @ CCS 2019

Workshop Organizer   —   Privacy in Machine Learning and Artificial Intelligence (PiMLAI) @ ICML 2018

Workshop Organizer   —   Learning and Automata (LearnAut) @ LICS 2017

Workshop Organizer   —   Fairness and Privacy in Machine Learning @ DALI 2017

Workshop Organizer   —   Private Multi-Party Machine Learning @ NIPS 2016

Competition Organizer   —   Sequence Prediction Challenge (SPICE) (2016)

Workshop Organizer   —   Methods of Moments and Spectral Learning @ ICML 2014

Workshop Organizer   —   Spectral Learning @ NIPS 2013

Workshop Organizer   —   Spectral Learning @ ICML 2013