MLOSS 2020

20 papers

AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang

AI-Toolbox: A C++ Library for Reinforcement Learning and Planning (with Python Bindings) Eugenio Bargiacchi, Diederik M. Roijers, Ann Nowé

Algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD Joseph D. Ramsey, Daniel Malinsky, Kevin V. Bui

Apache Mahout: Machine Learning on Distributed Dataflow Systems Robin Anil, Gokhan Capan, Isabel Drost-Fromm, Ted Dunning, Ellen Friedman, Trevor Grant, Shannon Quinn, Paritosh Ranjan, Sebastian Schelter, Özgür Yılmazel

Apricot: Submodular Selection for Data Summarization in Python Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble

Cornac: A Comparative Framework for Multimodal Recommender Systems Aghiles Salah, Quoc-Tuan Truong, Hady W. Lauw

Geomstats: A Python Package for Riemannian Geometry in Machine Learning Nina Miolane, Nicolas Guigui, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Daniel Brooks, Bernhard Kainz, Claire Donnat, Susan Holmes, Xavier Pennec

GluonTS: Probabilistic and Neural Time Series Modeling in Python Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang

GraKeL: A Graph Kernel Library in Python Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgiannis

Kymatio: Scattering Transforms in Python Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine Cella, Michael Eickenberg

Metric-Learn: Metric Learning Algorithms in Python William de Vazelhes, Cj Carey, Yuan Tang, Nathalie Vauquier, Aurélien Bellet

MFE: Towards Reproducible Meta-Feature Extraction Edesio Alcobaça, Felipe Siqueira, Adriano Rivolli, Luís P. F. Garcia, Jefferson T. Oliva, André C. P. L. F. de Carvalho

Probabilistic Learning on Graphs via Contextual Architectures Davide Bacciu, Federico Errica, Alessio Micheli

pyDML: A Python Library for Distance Metric Learning Juan Luis Suárez, Salvador García, Francisco Herrera

Pyts: A Python Package for Time Series Classification Johann Faouzi, Hicham Janati

Scikit-Network: Graph Analysis in Python Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier

Scikit-Survival: A Library for Time-to-Event Analysis Built on Top of Scikit-Learn Sebastian Pölsterl

Tensor Train Decomposition on TensorFlow (T3F) Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan Oseledets

ThunderGBM: Fast GBDTs and Random Forests on GPUs Zeyi Wen, Hanfeng Liu, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen

Tslearn, a Machine Learning Toolkit for Time Series Data Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz, Chester Holtz, Marie Payne, Roman Yurchak, Marc Rußwurm, Kushal Kolar, Eli Woods