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Oríon: A framework for distributed hyperparameter optimisation

I am the lead developer of Oríon, an open-source framework developed at Mila for distributed black-box optimization. Its purpose is to serve as a meta-optimizer for machine learning models and training, as well as a flexible experimentation platform for large-scale asynchronous optimization procedures.

Track: Generic logger backend for machine learning

Track is a generic logger backend developed at Mila that can serve as a bridge between different logging libraries for machine learning. The goal is to provide a backend for Oríon so that users can use their own logger without any modification and simply configure Oríon to use the proper bridge with Track.

Kleiṓ: Experiment management for machine learning

Kleiṓ is an experiment manager, a logging journal of all data describing your experiments. Its purpose is to provide an automatic tool to log extensive environment information, including a script’s code version, system specification, and script configuration.


Accounting for Variance in Machine Learning Benchmarks

Xavier Bouthillier, Pierre Delaunay, Mirko Bronzi, Assya Trofimov, Brennan Nichyporuk, Justin Szeto, Nazanin Mohammadi Sepahvand, Edward Raff, Kanika Madan, Vikram Voleti, Samira Ebrahimi Kahou, Vincent Michalski, Tal Arbel, Chris Pal, Gael Varoquaux, Pascal Vincent
Machine Learning and Systems (MLSys 2021), 2021

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Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.