| 8:25 – 8:30 AM | Opening remarks | Cheng Soon Ong, Sergey Lisitsyn | |
| 8:30 – 9:00 AM | Invited talk (pdf) | Gina Helfrich | NumFOCUS |
| 9:00 – 9:30 AM | Invited talk (pdf) | Christoph Hertzberg | Eigen3 |
| 9:30 – 10:00 AM | Invited talk (pdf) | Joaquin Vanschoren | OpenML |
| 10:00 – 10:05 AM | Poster spotlight (pdf) | Peter Sadowski | Sherpa: Hyperparameter Optimization for Machine Learning Models |
| 10:05 – 10:10 AM | Poster spotlight (pdf) | Maximilian Alber | How to iNNvestigate neural network’s predictions! |
| 10:10 – 10:15 AM | Poster spotlight (pdf) | Marcus Edel | mlpack open-source machine learning library and community |
| 10:15 – 10:20 AM | Poster spotlight (pdf) | Hiroyuki Kasai | Stochastic optimization library: SGDLibrary |
| 10:20 – 10:25 AM | Poster spotlight (pdf) | Brian Lester | Baseline: Strong, Extensible, Reproducible, Deep Learning Baselines for NLP |
| 10:25 AM – 11:20 AM | Poster | | McTorch, a manifold optimization library for deep learning |
| 10:25 AM – 11:20 AM | Poster | | Tensorflex: Tensorflow bindings for the Elixir programming language |
| 10:25 AM – 11:20 AM | Poster | | Open Source Machine Learning Software Development in CERN(High-Energy Physics): lessons and exchange of experience |
| 10:25 AM – 11:20 AM | Poster | | Accelerating Machine Learning Research with MI-Prometheus |
| 10:25 AM – 11:20 AM | Poster | | Gravity: A Mathematical Modeling Language for Optimization and Machine Learning |
| 10:25 AM – 11:20 AM | Poster | | skpro: A domain-agnostic modelling framework for probabilistic supervised learning |
| 10:25 AM – 11:20 AM | Poster | | xpandas - python data containers for structured types and structured machine learning tasks |
| 10:25 AM – 11:20 AM | Poster | | Machine Learning at Microsoft with ML.NET |
| 10:25 AM – 11:20 AM | Poster | | Open Fabric for Deep Learning Models |
| 10:25 AM – 11:20 AM | Poster | | Towards Reproducible and Reusable Deep Learning Systems Research Artifacts |
| 10:25 AM – 11:20 AM | Poster | | PyLissom: A tool for modeling computational maps of the visual cortex in PyTorch |
| 10:25 AM – 11:20 AM | Poster | | Salad: A Toolbox for Semi-supervised Adaptive Learning Across Domains |
| 10:25 AM – 11:20 AM | Poster | | Why every GBM speed benchmark is wrong |
| 11:20 AM – 11:40 PM | Contributed talk (pdf) | Martin Andrews | Building, growing and sustaining ML communities |
| 11:40 AM – 12:00 PM | Contributed talk (pdf) | Christopher Fonnesbeck | PyMC’s Big Adventure: Lessons Learned from the Development of Open-source Software for Probabilistic Programming |
| 12:00 PM – 2:00 PM | Lunch (on your own) | | |
| 2:00 – 2:30 PM | Invited talk (pdf) | James Hensman | GPFlow |
| 2:30 – 3:00 PM | Invited talk (pdf) | Mara Averick | tidyverse |
| 3:00 – 3:30 PM | Afternoon coffee break | | |
| 3:30 – 3:50 PM | Demo | Yuri Kuratov | DeepPavlov: An Open Source Library for Conversational AI |
| 3:50 – 4:10 PM | Demo | Eric Meissner | MXFusion: A Modular Deep Probabilistic Programming Library |
| 4:10 – 4:30 PM | Demo (pdf) | Nishant Kheterpal | Flow: Open Source Reinforcement Learning for Traffic Control |
| 4:30 – 4:50 PM | Demo | Jessica Forde | Reproducing Machine Learning Research on Binder |
| 4:50 – 5:30 PM | Panel discussion | Gina Helfrich, Christoph Hertzberg, Cheng Soon Ong, Mara Averick, Ryan Curtin (moderator) | |
| 5:30 – 5:35 PM | Closing remarks | Ryan Curtin | |