Machine Learning Open Source Software

To support the open source software movement, JMLR MLOSS publishes contributions related to implementations of non-trivial machine learning algorithms, toolboxes or even languages for scientific computing. Submission instructions are available here.

• solo-learn: A Library of Self-supervised Methods for Visual Representation Learning

  Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, Elisa Ricci (56):1−6, 2022 codePDF BibTeX

• SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

  Marius Lindauer, Katharina Eggensperger, Matthias Feurer, André Biedenkapp, Difan Deng, Carolin Benjamins, Tim Ruhkopf, René Sass, Frank Hutter (54):1−9, 2022 codePDF BibTeX

• DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python

  Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler (53):1−6, 2022 codePDF BibTeX

• Toolbox for Multimodal Learn (scikit-multimodallearn)

  Dominique Benielli, Baptiste Bauvin, Sokol Koço, Riikka Huusari, Cécile Capponi, Hachem Kadri, François Laviolette (51):1−7, 2022 codePDF BibTeX

• Stable-Baselines3: Reliable Reinforcement Learning Implementations

  Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann (268):1−8, 2021 codePDF BibTeX

• DIG: A Turnkey Library for Diving into Graph Deep Learning Research

  Meng Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Hao Yuan, Shurui Gui, Haiyang Yu, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora M Oztekin, Xuan Zhang, Shuiwang Ji (240):1−9, 2021 codePDF BibTeX

• sklvq: Scikit Learning Vector Quantization

  Rick van Veen, Michael Biehl, Gert-Jan de Vries (231):1−6, 2021 codePDF BibTeX

• FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection

  Yang Liu, Tao Fan, Tianjian Chen, Qian Xu, Qiang Yang (226):1−6, 2021 codePDF BibTeX

• TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads

  Paweł Rościszewski, Michał Martyniak, Filip Schodowski (215):1−5, 2021 codePDF BibTeX

• dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python

  Hubert Baniecki, Wojciech Kretowicz, Piotr Piątyszek, Jakub Wiśniewski, Przemysław Biecek (214):1−7, 2021 codePDF BibTeX

• mlr3pipelines - Flexible Machine Learning Pipelines in R

  Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl (184):1−7, 2021 codePDF BibTeX

• Alibi Explain: Algorithms for Explaining Machine Learning Models

  Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, Alexandru Coca (181):1−7, 2021 codePDF BibTeX

• The ensmallen library for flexible numerical optimization

  Ryan R. Curtin, Marcus Edel, Rahul Ganesh Prabhu, Suryoday Basak, Zhihao Lou, Conrad Sanderson (166):1−6, 2021 codePDF BibTeX

• MushroomRL: Simplifying Reinforcement Learning Research

  Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters (131):1−5, 2021 codePDF BibTeX

• River: machine learning for streaming data in Python

  Jacob Montiel, Max Halford, Saulo Martiello Mastelini, Geoffrey Bolmier, Raphael Sourty, Robin Vaysse, Adil Zouitine, Heitor Murilo Gomes, Jesse Read, Talel Abdessalem, Albert Bifet (110):1−8, 2021 codePDF BibTeX

• mvlearn: Multiview Machine Learning in Python

  Ronan Perry, Gavin Mischler, Richard Guo, Theodore Lee, Alexander Chang, Arman Koul, Cameron Franz, Hugo Richard, Iain Carmichael, Pierre Ablin, Alexandre Gramfort, Joshua T. Vogelstein (109):1−7, 2021 codePDF BibTeX

• OpenML-Python: an extensible Python API for OpenML

  Matthias Feurer, Jan N. van Rijn, Arlind Kadra, Pieter Gijsbers, Neeratyoy Mallik, Sahithya Ravi, Andreas Müller, Joaquin Vanschoren, Frank Hutter (100):1−5, 2021 codePDF BibTeX

• POT: Python Optimal Transport

  Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, Titouan Vayer (78):1−8, 2021 codePDF BibTeX

• ChainerRL: A Deep Reinforcement Learning Library

  Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa (77):1−14, 2021 codePDF BibTeX

• Kernel Operations on the GPU, with Autodiff, without Memory Overflows

  Benjamin Charlier, Jean Feydy, Joan Alexis Glaunès, François-David Collin, Ghislain Durif (74):1−6, 2021 codePDF BibTeX

• giotto-tda: : A Topological Data Analysis Toolkit for Machine Learning and Data Exploration

  Guillaume Tauzin, Umberto Lupo, Lewis Tunstall, Julian Burella Pérez, Matteo Caorsi, Anibal M. Medina-Mardones, Alberto Dassatti, Kathryn Hess (39):1−6, 2021 codePDF BibTeX

• Pykg2vec: A Python Library for Knowledge Graph Embedding

  Shih-Yuan Yu, Sujit Rokka Chhetri, Arquimedes Canedo, Palash Goyal, Mohammad Abdullah Al Faruque (16):1−6, 2021 codePDF BibTeX

• algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD

  Joseph D. Ramsey, Daniel Malinsky, Kevin V. Bui (238):1−6, 2020 codePDF BibTeX

• 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 (223):1−9, 2020 codePDF BibTeX

• scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn

  Sebastian Pölsterl (212):1−6, 2020 codePDF BibTeX

• Scikit-network: Graph Analysis in Python

  Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier (185):1−6, 2020 codePDF BibTeX

• apricot: Submodular selection for data summarization in Python

  Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble (161):1−6, 2020 codePDF BibTeX

• metric-learn: Metric Learning Algorithms in Python

  William de Vazelhes, CJ Carey, Yuan Tang, Nathalie Vauquier, Aurélien Bellet (138):1−6, 2020 codePDF BibTeX

• Probabilistic Learning on Graphs via Contextual Architectures

  Davide Bacciu, Federico Errica, Alessio Micheli (134):1−39, 2020 codePDF BibTeX

• 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 (130):1−6, 2020 codePDF BibTeX

• 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 (127):1−6, 2020 codePDF BibTeX

• 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 (118):1−6, 2020 codePDF BibTeX

• 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 (116):1−6, 2020 codePDF BibTeX

• 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 (111):1−5, 2020 codePDF BibTeX

• ThunderGBM: Fast GBDTs and Random Forests on GPUs

  Zeyi Wen, Hanfeng Liu, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen (108):1−5, 2020 codePDF BibTeX

• AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings)

  Eugenio Bargiacchi, Diederik M. Roijers, Ann Nowé (102):1−12, 2020 codePDF BibTeX

• pyDML: A Python Library for Distance Metric Learning

  Juan Luis Suárez, Salvador García, Francisco Herrera (96):1−7, 2020 codePDF BibTeX

• Cornac: A Comparative Framework for Multimodal Recommender Systems

  Aghiles Salah, Quoc-Tuan Truong, Hady W. Lauw (95):1−5, 2020 codePDF BibTeX

• 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 (60):1−6, 2020 codePDF BibTeX

• GraKeL: A Graph Kernel Library in Python

  Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgiannis (54):1−5, 2020 codePDF BibTeX

• pyts: A Python Package for Time Series Classification

  Johann Faouzi, Hicham Janati (46):1−6, 2020 codePDF BibTeX

• Tensor Train Decomposition on TensorFlow (T3F)

  Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan Oseledets (30):1−7, 2020 codePDF BibTeX

• ORCA: A Matlab/Octave Toolbox for Ordinal Regression

  Javier Sánchez-Monedero, Pedro A. Gutiérrez, María Pérez-Ortiz (125):1−5, 2019 codePDF BibTeX

• PyOD: A Python Toolbox for Scalable Outlier Detection

  Yue Zhao, Zain Nasrullah, Zheng Li (96):1−7, 2019 codePDF BibTeX

• iNNvestigate Neural Networks!

  Maximilian Alber, Sebastian Lapuschkin, Philipp Seegerer, Miriam Hägele, Kristof T. Schütt, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller, Sven Dähne, Pieter-Jan Kindermans (93):1−8, 2019 codePDF BibTeX

• AffectiveTweets: a Weka Package for Analyzing Affect in Tweets

  Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, Saif M. Mohammad (92):1−6, 2019 codePDF BibTeX

• SMART: An Open Source Data Labeling Platform for Supervised Learning

  Rob Chew, Michael Wenger, Caroline Kery, Jason Nance, Keith Richards, Emily Hadley, Peter Baumgartner (82):1−5, 2019 codePDF BibTeX

• Picasso: A Sparse Learning Library for High Dimensional Data Analysis in R and Python

  Jason Ge, Xingguo Li, Haoming Jiang, Han Liu, Tong Zhang, Mengdi Wang, Tuo Zhao (44):1−5, 2019 codewebpagePDF BibTeX

• Pyro: Deep Universal Probabilistic Programming

  Eli Bingham, Jonathan P. Chen, Martin Jankowiak, Fritz Obermeyer, Neeraj Pradhan, Theofanis Karaletsos, Rohit Singh, Paul Szerlip, Paul Horsfall, Noah D. Goodman (28):1−6, 2019 codePDF BibTeX

• TensorLy: Tensor Learning in Python

  Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic (26):1−6, 2019 codePDF BibTeX

• spark-crowd: A Spark Package for Learning from Crowdsourced Big Data

  Enrique G. Rodrigo, Juan A. Aledo, José A. Gámez (19):1−5, 2019 codePDF BibTeX

• scikit-multilearn: A Python library for Multi-Label Classification

  Piotr Szymański, Tomasz Kajdanowicz (6):1−22, 2019 codePDF BibTeX

• Seglearn: A Python Package for Learning Sequences and Time Series

  David M. Burns, Cari M. Whyne (83):1−7, 2018 codewebpagePDF BibTeX

• Scikit-Multiflow: A Multi-output Streaming Framework

  Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem (72):1−5, 2018 codePDF BibTeX

• OpenEnsembles: A Python Resource for Ensemble Clustering

  Tom Ronan, Shawn Anastasio, Zhijie Qi, Pedro Henrique S. Vieira Tavares, Roman Sloutsky, Kristen M. Naegle (26):1−6, 2018 webpagecodePDF BibTeX

• ThunderSVM: A Fast SVM Library on GPUs and CPUs

  Zeyi Wen, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen (21):1−5, 2018 webpagecodePDF BibTeX

• ELFI: Engine for Likelihood-Free Inference

  Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski (16):1−7, 2018 webpagecodePDF BibTeX

• SGDLibrary: A MATLAB library for stochastic optimization algorithms

  Hiroyuki Kasai (215):1−5, 2018 codePDF BibTeX

• tick: a Python Library for Statistical Learning, with an emphasis on Hawkes Processes and Time-Dependent Models

  Emmanuel Bacry, Martin Bompaire, Philip Deegan, Stéphane Gaïffas, Søren V. Poulsen (214):1−5, 2018 codewebpagePDF BibTeX

• KELP: a Kernel-based Learning Platform

  Simone Filice, Giuseppe Castellucci, Giovanni Da San Martino, Aless, ro Moschitti, Danilo Croce, Roberto Basili (191):1−5, 2018 codewebpagePDF BibTeX

• Pycobra: A Python Toolbox for Ensemble Learning and Visualisation

  Benjamin Guedj, Bhargav Srinivasa Desikan (190):1−5, 2018 codewebpagePDF BibTeX

• HyperTools: a Python Toolbox for Gaining Geometric Insights into High-Dimensional Data

  Andrew C. Heusser, Kirsten Ziman, Lucy L. W. Owen, Jeremy R. Manning (152):1−6, 2018 codewebpagePDF BibTeX

• openXBOW -- Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit

  Maximilian Schmitt, Björn Schuller (96):1−5, 2017 codePDF BibTeX

• The MADP Toolbox: An Open Source Library for Planning and Learning in (Multi-)Agent Systems

  Frans A. Oliehoek, Matthijs T. J. Spaan, Bas Terwijn, Philipp Robbel, Jo\~{a}o V. Messias (89):1−5, 2017 codePDF BibTeX

• GPflow: A Gaussian Process Library using TensorFlow

  Alexander G. de G. Matthews, Mark van der Wilk, Tom Nickson, Keisuke Fujii, Alexis Boukouvalas, Pablo Le{\'o}n-Villagr{\'a}, Zoubin Ghahramani, James Hensman (40):1−6, 2017 codewebpagePDF BibTeX

• GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis

  Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski (39):1−5, 2017 coder-project.orgPDF BibTeX

• POMDPs.jl: A Framework for Sequential Decision Making under Uncertainty

  Maxim Egorov, Zachary N. Sunberg, Edward Balaban, Tim A. Wheeler, Jayesh K. Gupta, Mykel J. Kochenderfer (26):1−5, 2017 codewebpagePDF BibTeX

• Auto-WEKA 2.0: Automatic model selection and hyperparameter optimization in WEKA

  Lars Kotthoff, Chris Thornton, Holger H. Hoos, Frank Hutter, Kevin Leyton-Brown (25):1−5, 2017 codewebpagePDF BibTeX

• JSAT: Java Statistical Analysis Tool, a Library for Machine Learning

  Edward Raff (23):1−5, 2017 codewebpagePDF BibTeX

• Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning

  Guillaume Lemaître, Fernando Nogueira, Christos K. Aridas (17):1−5, 2017 codewebpagePDF BibTeX

• Refinery: An Open Source Topic Modeling Web Platform

  Daeil Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth (12):1−5, 2017 codewebpagePDF BibTeX

• SnapVX: A Network-Based Convex Optimization Solver

  David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosič, Stephen Boyd, Jure Leskovec (4):1−5, 2017 codestanford.eduPDF BibTeX

• fastFM: A Library for Factorization Machines

  Immanuel Bayer (184):1−5, 2016 codewebpagePDF BibTeX

• Megaman: Scalable Manifold Learning in Python

  James McQueen, Marina Meilă, Jacob VanderPlas, Zhongyue Zhang (148):1−5, 2016 codewebpagePDF BibTeX

• JCLAL: A Java Framework for Active Learning

  Oscar Reyes, Eduardo Pérez, María del Carmen Rodríguez-Hernández, Habib M. Fardoun, Sebastián Ventura (95):1−5, 2016 codePDF BibTeX

• LIBMF: A Library for Parallel Matrix Factorization in Shared-memory Systems

  Wei-Sheng Chin, Bo-Wen Yuan, Meng-Yuan Yang, Yong Zhuang, Yu-Chin Juan, Chih-Jen Lin (86):1−5, 2016 codePDF BibTeX

• CVXPY: A Python-Embedded Modeling Language for Convex Optimization

  Steven Diamond, Stephen Boyd (83):1−5, 2016 codewebpagePDF BibTeX

• Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches

  Jure Žbontar, Yann LeCun (65):1−32, 2016 codePDF BibTeX

• MLlib: Machine Learning in Apache Spark

  Xiangrui Meng, Joseph Bradley, Burak Yavuz, Evan Sparks, Shivaram Venkataraman, Davies Liu, Jeremy Freeman, DB Tsai, Manish Amde, Sean Owen, Doris Xin, Reynold Xin, Michael J. Franklin, Reza Zadeh, Matei Zaharia, Ameet Talwalkar (34):1−7, 2016 codewebpagePDF BibTeX

• MEKA: A Multi-label/Multi-target Extension to WEKA

  Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes (21):1−5, 2016 codewebpagePDF BibTeX

• Harry: A Tool for Measuring String Similarity

  Konrad Rieck, Christian Wressnegger (9):1−5, 2016 codewebpagePDF BibTeX

• partykit: A Modular Toolkit for Recursive Partytioning in R

  Torsten Hothorn, Achim Zeileis (118):3905−3909, 2015 codePDF BibTeX

• CEKA: A Tool for Mining the Wisdom of Crowds

  Jing Zhang, Victor S. Sheng, Bryce A. Nicholson, Xindong Wu (88):2853−2858, 2015 codePDF BibTeX

• pyGPs -- A Python Library for Gaussian Process Regression and Classification

  Marion Neumann, Shan Huang, Daniel E. Marthaler, Kristian Kersting (80):2611−2616, 2015 codePDF BibTeX

• The Libra Toolkit for Probabilistic Models

  Daniel Lowd, Amirmohammad Rooshenas (75):2459−2463, 2015 codePDF BibTeX

• RLPy: A Value-Function-Based Reinforcement Learning Framework for Education and Research

  Alborz Geramifard, Christoph Dann, Robert H. Klein, William Dabney, Jonathan P. How (46):1573−1578, 2015 codePDF BibTeX

• Encog: Library of Interchangeable Machine Learning Models for Java and C#

  Jeff Heaton (36):1243−1247, 2015 codewebpagePDF BibTeX

• The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R

  Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu (18):553−557, 2015 codewebpagePDF BibTeX

• Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit

  Felix Weninger (17):547−551, 2015 codePDF BibTeX

• A Classification Module for Genetic Programming Algorithms in JCLEC

  Alberto Cano, José María Luna, Amelia Zafra, Sebastián Ventura (15):491−494, 2015 codePDF BibTeX

• SAMOA: Scalable Advanced Massive Online Analysis

  Gianmarco De Francisci Morales, Albert Bifet (5):149−153, 2015 codePDF BibTeX

• BudgetedSVM: A Toolbox for Scalable SVM Approximations

  Nemanja Djuric, Liang Lan, Slobodan Vucetic, Zhuang Wang (84):3813−3817, 2013 codePDF BibTeX

• MULTIBOOST: A Multi-purpose Boosting Package

  Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl (18):549−553, 2012 codePDF BibTeX

• Waffles: A Machine Learning Toolkit

  Michael Gashler (69):2383−2387, 2011 codePDF BibTeX

• CARP: Software for Fishing Out Good Clustering Algorithms

  Volodymyr Melnykov, Ranjan Maitra (3):69−73, 2011 codePDF BibTeX

• LIBLINEAR: A Library for Large Linear Classification

  Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (61):1871−1874, 2008 codePDF BibTeX