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.

Tensor Train Decomposition on TensorFlow (T3F)
Alexander Novikov, Pavel Izmailov, Valentin Khrulkov, Michael Figurnov, Ivan Oseledets; (30):1−7, 2020.
[abs][pdf][bib]      [code]

pyts: A Python Package for Time Series Classification
Johann Faouzi, Hicham Janati; (46):1−6, 2020.
[abs][pdf][bib]      [code]

GraKeL: A Graph Kernel Library in Python
Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgiannis; (54):1−5, 2020.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

Cornac: A Comparative Framework for Multimodal Recommender Systems
Aghiles Salah, Quoc-Tuan Truong, Hady W. Lauw; (95):1−5, 2020.
[abs][pdf][bib]      [code]

pyDML: A Python Library for Distance Metric Learning
Juan Luis Suárez, Salvador García, Francisco Herrera; (96):1−7, 2020.
[abs][pdf][bib]      [code]

AI-Toolbox: A C++ library for Reinforcement Learning and Planning (with Python Bindings)
Eugenio Bargiacchi, Diederik M. Roijers, Ann Nowé; (102):1−12, 2020.
[abs][pdf][bib]      [code]

ThunderGBM: Fast GBDTs and Random Forests on GPUs
Zeyi Wen, Hanfeng Liu, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen; (108):1−5, 2020.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

Probabilistic Learning on Graphs via Contextual Architectures
Davide Bacciu, Federico Errica, Alessio Micheli; (134):1−39, 2020.
[abs][pdf][bib]      [code]

metric-learn: Metric Learning Algorithms in Python
William de Vazelhes, CJ Carey, Yuan Tang, Nathalie Vauquier, Aurélien Bellet; (138):1−6, 2020.
[abs][pdf][bib]      [code]

scikit-multilearn: A Python library for Multi-Label Classification
Piotr Szymański, Tomasz Kajdanowicz; (6):1−22, 2019.
[abs][pdf][bib]      [code]

spark-crowd: A Spark Package for Learning from Crowdsourced Big Data
Enrique G. Rodrigo, Juan A. Aledo, José A. Gámez; (19):1−5, 2019.
[abs][pdf][bib]      [code]

TensorLy: Tensor Learning in Python
Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic; (26):1−6, 2019.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code] [webpage]

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.
[abs][pdf][bib]      [code]

AffectiveTweets: a Weka Package for Analyzing Affect in Tweets
Felipe Bravo-Marquez, Eibe Frank, Bernhard Pfahringer, Saif M. Mohammad; (92):1−6, 2019.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

PyOD: A Python Toolbox for Scalable Outlier Detection
Yue Zhao, Zain Nasrullah, Zheng Li; (96):1−7, 2019.
[abs][pdf][bib]      [code]

ORCA: A Matlab/Octave Toolbox for Ordinal Regression
Javier Sánchez-Monedero, Pedro A. Gutiérrez, María Pérez-Ortiz; (125):1−5, 2019.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [webpage] [code]

ThunderSVM: A Fast SVM Library on GPUs and CPUs
Zeyi Wen, Jiashuai Shi, Qinbin Li, Bingsheng He, Jian Chen; (21):1−5, 2018.
[abs][pdf][bib]      [webpage] [code]

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.
[abs][pdf][bib]      [webpage] [code]

Scikit-Multiflow: A Multi-output Streaming Framework
Jacob Montiel, Jesse Read, Albert Bifet, Talel Abdessalem; (72):1−5, 2018.
[abs][pdf][bib]      [code]

Seglearn: A Python Package for Learning Sequences and Time Series
David M. Burns, Cari M. Whyne; (83):1−7, 2018.
[abs][pdf][bib]      [code] [webpage]

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.
[abs][pdf][bib]      [code] [stanford.edu]

Refinery: An Open Source Topic Modeling Web Platform
Daeil Kim, Benjamin F. Swanson, Michael C. Hughes, Erik B. Sudderth; (12):1−5, 2017.
[abs][pdf][bib]      [code] [webpage]

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.
[abs][pdf][bib]      [code] [webpage]

JSAT: Java Statistical Analysis Tool, a Library for Machine Learning
Edward Raff; (23):1−5, 2017.
[abs][pdf][bib]      [code] [webpage]

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.
[abs][pdf][bib]      [code] [webpage]

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.
[abs][pdf][bib]      [code] [webpage]

GFA: Exploratory Analysis of Multiple Data Sources with Group Factor Analysis
Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski; (39):1−5, 2017.
[abs][pdf][bib]      [code] [r-project.org]

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.
[abs][pdf][bib]      [code] [webpage]

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.
[abs][pdf][bib]      [code]

openXBOW -- Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit
Maximilian Schmitt, Björn Schuller; (96):1−5, 2017.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code] [webpage]

Pycobra: A Python Toolbox for Ensemble Learning and Visualisation
Benjamin Guedj, Bhargav Srinivasa Desikan; (190):1−5, 2018.
[abs][pdf][bib]      [code] [webpage]

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.
[abs][pdf][bib]      [code] [webpage]

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.
[abs][pdf][bib]      [code] [webpage]

SGDLibrary: A MATLAB library for stochastic optimization algorithms
Hiroyuki Kasai; (215):1−5, 2018.
[abs][pdf][bib]      [code]

Harry: A Tool for Measuring String Similarity
Konrad Rieck, Christian Wressnegger; (9):1−5, 2016.
[abs][pdf][bib]      [code] [webpage]

MEKA: A Multi-label/Multi-target Extension to WEKA
Jesse Read, Peter Reutemann, Bernhard Pfahringer, Geoff Holmes; (21):1−5, 2016.
[abs][pdf][bib]      [code] [webpage]

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.
[abs][pdf][bib]      [code] [webpage]

Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches
Jure Žbontar, Yann LeCun; (65):1−32, 2016.
[abs][pdf][bib]      [code]

CVXPY: A Python-Embedded Modeling Language for Convex Optimization
Steven Diamond, Stephen Boyd; (83):1−5, 2016.
[abs][pdf][bib]      [code] [webpage]

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.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

Megaman: Scalable Manifold Learning in Python
James McQueen, Marina Meilă, Jacob VanderPlas, Zhongyue Zhang; (148):1−5, 2016.
[abs][pdf][bib]      [code] [webpage]

fastFM: A Library for Factorization Machines
Immanuel Bayer; (184):1−5, 2016.
[abs][pdf][bib]      [code] [webpage]

SAMOA: Scalable Advanced Massive Online Analysis
Gianmarco De Francisci Morales, Albert Bifet; (5):149−153, 2015.
[abs][pdf][bib]      [code]

A Classification Module for Genetic Programming Algorithms in JCLEC
Alberto Cano, José María Luna, Amelia Zafra, Sebastián Ventura; (15):491−494, 2015.
[abs][pdf][bib]      [code]

Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit
Felix Weninger; (17):547−551, 2015.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code] [webpage]

Encog: Library of Interchangeable Machine Learning Models for Java and C#
Jeff Heaton; (36):1243−1247, 2015.
[abs][pdf][bib]      [code] [webpage]

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.
[abs][pdf][bib]      [code]

The Libra Toolkit for Probabilistic Models
Daniel Lowd, Amirmohammad Rooshenas; (75):2459−2463, 2015.
[abs][pdf][bib]      [code]

pyGPs -- A Python Library for Gaussian Process Regression and Classification
Marion Neumann, Shan Huang, Daniel E. Marthaler, Kristian Kersting; (80):2611−2616, 2015.
[abs][pdf][bib]      [code]

CEKA: A Tool for Mining the Wisdom of Crowds
Jing Zhang, Victor S. Sheng, Bryce A. Nicholson, Xindong Wu; (88):2853−2858, 2015.
[abs][pdf][bib]      [code]

partykit: A Modular Toolkit for Recursive Partytioning in R
Torsten Hothorn, Achim Zeileis; (118):3905−3909, 2015.
[abs][pdf][bib]      [code]

BudgetedSVM: A Toolbox for Scalable SVM Approximations
Nemanja Djuric, Liang Lan, Slobodan Vucetic, Zhuang Wang; (84):3813−3817, 2013.
[abs][pdf][bib]      [code]

MULTIBOOST: A Multi-purpose Boosting Package
Djalel Benbouzid, Róbert Busa-Fekete, Norman Casagrande, François-David Collin, Balázs Kégl; (18):549−553, 2012.
[abs][pdf][bib]      [code]

CARP: Software for Fishing Out Good Clustering Algorithms
Volodymyr Melnykov, Ranjan Maitra; (3):69−73, 2011.
[abs][pdf][bib]      [code]

Waffles: A Machine Learning Toolkit
Michael Gashler; (69):2383−2387, 2011.
[abs][pdf][bib]      [code]




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