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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.

OpenBox: A Python Toolkit for Generalized Black-box Optimization
Huaijun Jiang, Yu Shen, Yang Li, Beicheng Xu, Sixian Du, Wentao Zhang, Ce Zhang, Bin Cui; (120):1−11, 2024.
[abs][pdf][bib]      [code]

QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration
Felix Chalumeau, Bryan Lim, Raphaël Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Guillaume Richard, Arthur Flajolet, Thomas Pierrot, Antoine Cully; (108):1−16, 2024.
[abs][pdf][bib]      [code]

ptwt - The PyTorch Wavelet Toolbox
Moritz Wolter, Felix Blanke, Jochen Garcke, Charles Tapley Hoyt; (80):1−7, 2024.
[abs][pdf][bib]      [code]

On Unbiased Estimation for Partially Observed Diffusions
Jeremy Heng, Jeremie Houssineau, Ajay Jasra; (66):1−66, 2024.
[abs][pdf][bib]      [code]

Causal-learn: Causal Discovery in Python
Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang; (60):1−8, 2024.
[abs][pdf][bib]      [code]

Invariant and Equivariant Reynolds Networks
Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai; (42):1−36, 2024.
[abs][pdf][bib]      [code]

Pygmtools: A Python Graph Matching Toolkit
Runzhong Wang, Ziao Guo, Wenzheng Pan, Jiale Ma, Yikai Zhang, Nan Yang, Qi Liu, Longxuan Wei, Hanxue Zhang, Chang Liu, Zetian Jiang, Xiaokang Yang, Junchi Yan; (33):1−7, 2024.
[abs][pdf][bib]      [code]

Scaling Up Models and Data with t5x and seqio
Adam Roberts, Hyung Won Chung, Gaurav Mishra, Anselm Levskaya, James Bradbury, Daniel Andor, Sharan Narang, Brian Lester, Colin Gaffney, Afroz Mohiuddin, Curtis Hawthorne, Aitor Lewkowycz, Alex Salcianu, Marc van Zee, Jacob Austin, Sebastian Goodman, Livio Baldini Soares, Haitang Hu, Sasha Tsvyashchenko, Aakanksha Chowdhery, Jasmijn Bastings, Jannis Bulian, Xavier Garcia, Jianmo Ni, Andrew Chen, Kathleen Kenealy, Kehang Han, Michelle Casbon, Jonathan H. Clark, Stephan Lee, Dan Garrette, James Lee-Thorp, Colin Raffel, Noam Shazeer, Marvin Ritter, Maarten Bosma, Alexandre Passos, Jeremy Maitin-Shepard, Noah Fiedel, Mark Omernick, Brennan Saeta, Ryan Sepassi, Alexander Spiridonov, Joshua Newlan, Andrea Gesmundo; (377):1−8, 2023.
[abs][pdf][bib]      [code]

TorchOpt: An Efficient Library for Differentiable Optimization
Jie Ren*, Xidong Feng*, Bo Liu*, Xuehai Pan*, Yao Fu, Luo Mai, Yaodong Yang; (367):1−14, 2023.
[abs][pdf][bib]      [code]

Avalanche: A PyTorch Library for Deep Continual Learning
Antonio Carta, Lorenzo Pellegrini, Andrea Cossu, Hamed Hemati, Vincenzo Lomonaco; (363):1−6, 2023.
[abs][pdf][bib]      [code]

MARLlib: A Scalable and Efficient Multi-agent Reinforcement Learning Library
Siyi Hu, Yifan Zhong, Minquan Gao, Weixun Wang, Hao Dong, Xiaodan Liang, Zhihui Li, Xiaojun Chang, Yaodong Yang; (315):1−23, 2023.
[abs][pdf][bib]      [code]

Fairlearn: Assessing and Improving Fairness of AI Systems
Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio; (257):1−8, 2023.
[abs][pdf][bib]      [code]

Torchhd: An Open Source Python Library to Support Research on Hyperdimensional Computing and Vector Symbolic Architectures
Mike Heddes, Igor Nunes, Pere Vergés, Denis Kleyko, Danny Abraham, Tony Givargis, Alexandru Nicolau, Alexander Veidenbaum; (255):1−10, 2023.
[abs][pdf][bib]      [code]

skrl: Modular and Flexible Library for Reinforcement Learning
Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Simon Bøgh, Nestor Arana-Arexolaleiba; (254):1−9, 2023.
[abs][pdf][bib]      [code]

MultiZoo and MultiBench: A Standardized Toolkit for Multimodal Deep Learning
Paul Pu Liang, Yiwei Lyu, Xiang Fan, Arav Agarwal, Yun Cheng, Louis-Philippe Morency, Ruslan Salakhutdinov; (234):1−7, 2023.
[abs][pdf][bib]      [code]

Merlion: End-to-End Machine Learning for Time Series
Aadyot Bhatnagar, Paul Kassianik, Chenghao Liu, Tian Lan, Wenzhuo Yang, Rowan Cassius, Doyen Sahoo, Devansh Arpit, Sri Subramanian, Gerald Woo, Amrita Saha, Arun Kumar Jagota, Gokulakrishnan Gopalakrishnan, Manpreet Singh, K C Krithika, Sukumar Maddineni, Daeki Cho, Bo Zong, Yingbo Zhou, Caiming Xiong, Silvio Savarese, Steven Hoi, Huan Wang; (226):1−6, 2023.
[abs][pdf][bib]      [code]

LibMTL: A Python Library for Deep Multi-Task Learning
Baijiong Lin, Yu Zhang; (209):1−7, 2023.
[abs][pdf][bib]      [code]

L0Learn: A Scalable Package for Sparse Learning using L0 Regularization
Hussein Hazimeh, Rahul Mazumder, Tim Nonet; (205):1−8, 2023.
[abs][pdf][bib]      [code]

CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges
Adrien Pavao, Isabelle Guyon, Anne-Catherine Letournel, Dinh-Tuan Tran, Xavier Baro, Hugo Jair Escalante, Sergio Escalera, Tyler Thomas, Zhen Xu; (198):1−6, 2023.
[abs][pdf][bib]      [code]

MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning
Ming Zhou, Ziyu Wan, Hanjing Wang, Muning Wen, Runzhe Wu, Ying Wen, Yaodong Yang, Yong Yu, Jun Wang, Weinan Zhang; (150):1−12, 2023.
[abs][pdf][bib]      [code]

SQLFlow: An Extensible Toolkit Integrating DB and AI
Jun Zhou, Ke Zhang, Lin Wang, Hua Wu, Yi Wang, ChaoChao Chen; (116):1−9, 2023.
[abs][pdf][bib]      [code]

FedLab: A Flexible Federated Learning Framework
Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu; (100):1−7, 2023.
[abs][pdf][bib]      [code]

Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond
Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne; (34):1−11, 2023.
[abs][pdf][bib]      [code]

HiClass: a Python Library for Local Hierarchical Classification Compatible with Scikit-learn
Fábio M. Miranda, Niklas Köhnecke, Bernhard Y. Renard; (29):1−17, 2023.
[abs][pdf][bib]      [code]

Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping
XuranMeng, JeffYao; (28):1−40, 2023.
[abs][pdf][bib]      [code]

Python package for causal discovery based on LiNGAM
Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu; (14):1−8, 2023.
[abs][pdf][bib]      [code]

AutoKeras: An AutoML Library for Deep Learning
Haifeng Jin, François Chollet, Qingquan Song, Xia Hu; (6):1−6, 2023.
[abs][pdf][bib]      [code]

OMLT: Optimization & Machine Learning Toolkit
Francesco Ceccon, Jordan Jalving, Joshua Haddad, Alexander Thebelt, Calvin Tsay, Carl D Laird, Ruth Misener; (349):1−8, 2022.
[abs][pdf][bib]      [code]

WarpDrive: Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
Tian Lan, Sunil Srinivasa, Huan Wang, Stephan Zheng; (316):1−6, 2022.
[abs][pdf][bib]      [code]

d3rlpy: An Offline Deep Reinforcement Learning Library
Takuma Seno, Michita Imai; (315):1−20, 2022.
[abs][pdf][bib]      [code]

JsonGrinder.jl: automated differentiable neural architecture for embedding arbitrary JSON data
Šimon Mandlík, Matěj Račinský, Viliam Lisý, Tomáš Pevný; (298):1−5, 2022.
[abs][pdf][bib]      [code]

ReservoirComputing.jl: An Efficient and Modular Library for Reservoir Computing Models
Francesco Martinuzzi, Chris Rackauckas, Anas Abdelrehim, Miguel D. Mahecha, Karin Mora; (288):1−8, 2022.
[abs][pdf][bib]      [code]

Deepchecks: A Library for Testing and Validating Machine Learning Models and Data
Shir Chorev, Philip Tannor, Dan Ben Israel, Noam Bressler, Itay Gabbay, Nir Hutnik, Jonatan Liberman, Matan Perlmutter, Yurii Romanyshyn, Lior Rokach; (285):1−6, 2022.
[abs][pdf][bib]      [code]

CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms
Shengyi Huang, Rousslan Fernand Julien Dossa, Chang Ye, Jeff Braga, Dipam Chakraborty, Kinal Mehta, João G.M. Araújo; (274):1−18, 2022.
[abs][pdf][bib]      [code]

Tianshou: A Highly Modularized Deep Reinforcement Learning Library
Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Yi Su, Hang Su, Jun Zhu; (267):1−6, 2022.
[abs][pdf][bib]      [code]

abess: A Fast Best-Subset Selection Library in Python and R
Jin Zhu, Xueqin Wang, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu; (202):1−7, 2022.
[abs][pdf][bib]      [code]

InterpretDL: Explaining Deep Models in PaddlePaddle
Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Zeyu Chen, Dejing Dou; (197):1−6, 2022.
[abs][pdf][bib]      [code]

ktrain: A Low-Code Library for Augmented Machine Learning
Arun S. Maiya; (158):1−6, 2022.
[abs][pdf][bib]      [code]

Darts: User-Friendly Modern Machine Learning for Time Series
Julien Herzen, Francesco Lässig, Samuele Giuliano Piazzetta, Thomas Neuer, Léo Tafti, Guillaume Raille, Tomas Van Pottelbergh, Marek Pasieka, Andrzej Skrodzki, Nicolas Huguenin, Maxime Dumonal, Jan Kościsz, Dennis Bader, Frédérick Gusset, Mounir Benheddi, Camila Williamson, Michal Kosinski, Matej Petrik, Gaël Grosch; (124):1−6, 2022.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

DoubleML - An Object-Oriented Implementation of Double Machine Learning in Python
Philipp Bach, Victor Chernozhukov, Malte S. Kurz, Martin Spindler; (53):1−6, 2022.
[abs][pdf][bib]      [code]

Toolbox for Multimodal Learn (scikit-multimodallearn)
Dominique Benielli, Baptiste Bauvin, Sokol Koço, Riikka Huusari, Cécile Capponi, Hachem Kadri, François Laviolette; (51):1−7, 2022.
[abs][pdf][bib]      [code]

Stable-Baselines3: Reliable Reinforcement Learning Implementations
Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, Noah Dormann; (268):1−8, 2021.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

sklvq: Scikit Learning Vector Quantization
Rick van Veen, Michael Biehl, Gert-Jan de Vries; (231):1−6, 2021.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

TensorHive: Management of Exclusive GPU Access for Distributed Machine Learning Workloads
Paweł Rościszewski, Michał Martyniak, Filip Schodowski; (215):1−5, 2021.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

mlr3pipelines - Flexible Machine Learning Pipelines in R
Martin Binder, Florian Pfisterer, Michel Lang, Lennart Schneider, Lars Kotthoff, Bernd Bischl; (184):1−7, 2021.
[abs][pdf][bib]      [code]

Alibi Explain: Algorithms for Explaining Machine Learning Models
Janis Klaise, Arnaud Van Looveren, Giovanni Vacanti, Alexandru Coca; (181):1−7, 2021.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

MushroomRL: Simplifying Reinforcement Learning Research
Carlo D'Eramo, Davide Tateo, Andrea Bonarini, Marcello Restelli, Jan Peters; (131):1−5, 2021.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

ChainerRL: A Deep Reinforcement Learning Library
Yasuhiro Fujita, Prabhat Nagarajan, Toshiki Kataoka, Takahiro Ishikawa; (77):1−14, 2021.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD
Joseph D. Ramsey, Daniel Malinsky, Kevin V. Bui; (238):1−6, 2020.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn
Sebastian Pölsterl; (212):1−6, 2020.
[abs][pdf][bib]      [code]

Scikit-network: Graph Analysis in Python
Thomas Bonald, Nathan de Lara, Quentin Lutz, Bertrand Charpentier; (185):1−6, 2020.
[abs][pdf][bib]      [code]

apricot: Submodular selection for data summarization in Python
Jacob Schreiber, Jeffrey Bilmes, William Stafford Noble; (161):1−6, 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]

Probabilistic Learning on Graphs via Contextual Architectures
Davide Bacciu, Federico Errica, Alessio Micheli; (134):1−39, 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]

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]

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]

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]

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]

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]

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]

pyDML: A Python Library for Distance Metric Learning
Juan Luis Suárez, Salvador García, Francisco Herrera; (96):1−7, 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]

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]

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]

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

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

PyOD: A Python Toolbox for Scalable Outlier Detection
Yue Zhao, Zain Nasrullah, Zheng Li; (96):1−7, 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]

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]

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]

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]

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]

TensorLy: Tensor Learning in Python
Jean Kossaifi, Yannis Panagakis, Anima Anandkumar, Maja Pantic; (26):1−6, 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]

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

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

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]

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]

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

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]

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]

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

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]

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

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]

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]

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]

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]

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]

JSAT: Java Statistical Analysis Tool, a Library for Machine Learning
Edward Raff; (23):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]

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]

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]

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

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

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]

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]

CVXPY: A Python-Embedded Modeling Language for Convex Optimization
Steven Diamond, Stephen Boyd; (83):1−5, 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]

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]

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]

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

partykit: A Modular Toolkit for Recursive Partytioning in R
Torsten Hothorn, Achim Zeileis; (118):3905−3909, 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]

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]

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

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]

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

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]

Introducing CURRENNT: The Munich Open-Source CUDA RecurREnt Neural Network Toolkit
Felix Weninger; (17):547−551, 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]

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

BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits
Ruben Martinez-Cantin; (115):3915−3919, 2014.
[abs][pdf][bib]      [code]

SPMF: A Java Open-Source Pattern Mining Library
Philippe Fournier-Viger, Antonio Gomariz, Ted Gueniche, Azadeh Soltani, Cheng-Wei Wu, Vincent S. Tseng; (104):3569−3573, 2014.
[abs][pdf][bib]      [code]

The Gesture Recognition Toolkit
Nicholas Gillian, Joseph A. Paradiso; (101):3483−3487, 2014.
[abs][pdf][bib]      [code]

ooDACE Toolbox: A Flexible Object-Oriented Kriging Implementation
Ivo Couckuyt, Tom Dhaene, Piet Demeester; (91):3183−3186, 2014.
[abs][pdf][bib]      [code]

pystruct - Learning Structured Prediction in Python
Andreas C. Müller, Sven Behnke; (59):2055−2060, 2014.
[abs][pdf][bib]      [code]

Manopt, a Matlab Toolbox for Optimization on Manifolds
Nicolas Boumal, Bamdev Mishra, P.-A. Absil, Rodolphe Sepulchre; (42):1455−1459, 2014.
[abs][pdf][bib]      [code]

Conditional Random Field with High-order Dependencies for Sequence Labeling and Segmentation
Nguyen Viet Cuong, Nan Ye, Wee Sun Lee, Hai Leong Chieu; (28):981−1009, 2014.
[abs][pdf][bib]      [code]

LIBOL: A Library for Online Learning Algorithms
Steven C.H. Hoi, Jialei Wang, Peilin Zhao; (15):495−499, 2014.
[abs][pdf][bib]      [code]

The FASTCLIME Package for Linear Programming and Large-Scale Precision Matrix Estimation in R
Haotian Pang, Han Liu, Robert V, erbei; (14):489−493, 2014.
[abs][pdf][bib]      [code]

Information Theoretical Estimators Toolbox
Zoltán Szabó; (9):283−287, 2014.
[abs][pdf][bib]      [code]

EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines
Marc Claesen, Frank De Smet, Johan A.K. Suykens, Bart De Moor; (4):141−145, 2014.
[abs][pdf][bib]      [code]

GURLS: A Least Squares Library for Supervised Learning
Andrea Tacchetti, Pavan K. Mallapragada, Matteo Santoro, Lorenzo Rosasco; (100):3201−3205, 2013.
[abs][pdf][bib]      [code]

Divvy: Fast and Intuitive Exploratory Data Analysis
Joshua M. Lewis, Virginia R. de Sa, Laurens van der Maaten; (98):3159−3163, 2013.
[abs][pdf][bib]      [code] [webpage]

QuantMiner for Mining Quantitative Association Rules
Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod, Daniel Cassard; (97):3153−3157, 2013.
[abs][pdf][bib]      [code]

The CAM Software for Nonnegative Blind Source Separation in R-Java
Niya Wang, Fan Meng, Li Chen, Subha Madhavan, Robert Clarke, Eric P. Hoffman, Jianhua Xuan, Yue Wang; (88):2899−2903, 2013.
[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]

Tapkee: An Efficient Dimension Reduction Library
Sergey Lisitsyn, Christian Widmer, Fernando J. Iglesias Garcia; (72):2355−2359, 2013.
[abs][pdf][bib]      [code]

Orange: Data Mining Toolbox in Python
Janez Demšar, Tomaž Curk, Aleš Erjavec, Črt Gorup, Tomaž Hočevar, Mitar Milutinovič, Martin Možina, Matija Polajnar, Marko Toplak, Anže Starič, Miha Štajdohar, Lan Umek, Lan Žagar, Jure Žbontar, Marinka Žitnik, Blaž Zupan; (71):2349−2353, 2013.
[abs][pdf][bib]      [code]

JKernelMachines: A Simple Framework for Kernel Machines
David Picard, Nicolas Thome, Matthieu Cord; (43):1417−1421, 2013.
[abs][pdf][bib]      [code]

GPstuff: Bayesian Modeling with Gaussian Processes
Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari; (35):1175−1179, 2013.
[abs][pdf][bib]      [code]

MLPACK: A Scalable C++ Machine Learning Library
Ryan R. Curtin, James R. Cline, N. P. Slagle, William B. March, Parikshit Ram, Nishant A. Mehta, Alexander G. Gray; (24):801−805, 2013.
[abs][pdf][bib]      [code]

A C++ Template-Based Reinforcement Learning Library: Fitting the Code to the Mathematics
Hervé Frezza-Buet, Matthieu Geist; (18):625−628, 2013.
[abs][pdf][bib]      [code]

SVDFeature: A Toolkit for Feature-based Collaborative Filtering
Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, Yong Yu; (116):3619−3622, 2012.
[abs][pdf][bib]      [code]

DARWIN: A Framework for Machine Learning and Computer Vision Research and Development
Stephen Gould; (113):3533−3537, 2012.
[abs][pdf][bib]      [code]

Sally: A Tool for Embedding Strings in Vector Spaces
Konrad Rieck, Christian Wressnegger, Alexander Bikadorov; (104):3247−3251, 2012.
[abs][pdf][bib]      [code]

Oger: Modular Learning Architectures For Large-Scale Sequential Processing
David Verstraeten, Benjamin Schrauwen, Sander Dieleman, Philemon Brakel, Pieter Buteneers, Dejan Pecevski; (96):2995−2998, 2012.
[abs][pdf][bib]      [code]

PREA: Personalized Recommendation Algorithms Toolkit
Joonseok Lee, Mingxuan Sun, Guy Lebanon; (87):2699−2703, 2012.
[abs][pdf][bib]      [code]

A Topic Modeling Toolbox Using Belief Propagation
Jia Zeng; (73):2233−2236, 2012.
[abs][pdf][bib]      [code]

DEAP: Evolutionary Algorithms Made Easy
Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, Christian Gagné; (70):2171−2175, 2012.
[abs][pdf][bib]      [code]

Pattern for Python
Tom De Smedt, Walter Daelemans; (66):2063−2067, 2012.
[abs][pdf][bib]      [code]

Jstacs: A Java Framework for Statistical Analysis and Classification of Biological Sequences
Jan Grau, Jens Keilwagen, André Gohr, Berit Haldemann, Stefan Posch, Ivo Grosse; (62):1967−1971, 2012.
[abs][pdf][bib]      [code]

glm-ie: Generalised Linear Models Inference & Estimation Toolbox
Hannes Nickisch; (54):1699−1703, 2012.
[abs][pdf][bib]      [code]

The huge Package for High-dimensional Undirected Graph Estimation in R
Tuo Zhao, Han Liu, Kathryn Roeder, John Lafferty, Larry Wasserman; (37):1059−1062, 2012.
[abs][pdf][bib]      [code]

NIMFA : A Python Library for Nonnegative Matrix Factorization
Marinka Žitnik, Blaž Zupan; (30):849−853, 2012.
[abs][pdf][bib]      [code]

GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression
Chiwoo Park, Jianhua Z. Huang, Yu Ding; (26):775−779, 2012.
[abs][pdf][bib]      [code]

ML-Flex: A Flexible Toolbox for Performing Classification Analyses In Parallel
Stephen R. Piccolo, Lewis J. Frey; (19):555−559, 2012.
[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]

The Stationary Subspace Analysis Toolbox
Jan Saputra Müller, Paul von Bünau, Frank C. Meinecke, Franz J. Király, Klaus-Robert Müller; (93):3065−3069, 2011.
[abs][pdf][bib]      [code]

Scikit-learn: Machine Learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Édouard Duchesnay; (85):2825−2830, 2011.
[abs][pdf][bib]      [code]

LPmade: Link Prediction Made Easy
Ryan N. Lichtenwalter, Nitesh V. Chawla; (75):2489−2492, 2011.
[abs][pdf][bib]      [code]

MULAN: A Java Library for Multi-Label Learning
Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Jozef Vilcek, Ioannis Vlahavas; (71):2411−2414, 2011.
[abs][pdf][bib]      [code]

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

MSVMpack: A Multi-Class Support Vector Machine Package
Fabien Lauer, Yann Guermeur; (66):2293−2296, 2011.
[abs][pdf][bib]      [code]

The arules R-Package Ecosystem: Analyzing Interesting Patterns from Large Transaction Data Sets
Michael Hahsler, Sudheer Chelluboina, Kurt Hornik, Christian Buchta; (57):2021−2025, 2011.
[abs][pdf][bib]      [code]

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

Gaussian Processes for Machine Learning (GPML) Toolbox
Carl Edward Rasmussen, Hannes Nickisch; (100):3011−3015, 2010.
[abs][pdf][bib]      [code]

libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models
Joris M. Mooij; (74):2169−2173, 2010.
[abs][pdf][bib]      [code]

Model-based Boosting 2.0
Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner; (71):2109−2113, 2010.
[abs][pdf][bib]      [code]

A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design
Dirk Gorissen, Ivo Couckuyt, Piet Demeester, Tom Dhaene, Karel Crombecq; (68):2051−2055, 2010.
[abs][pdf][bib]      [code]

The SHOGUN Machine Learning Toolbox
Sören Sonnenburg, Gunnar Rätsch, Sebastian Henschel, Christian Widmer, Jonas Behr, Alexander Zien, Fabio de Bona, Alexander Binder, Christian Gehl, Vojt{{\ve}}ch Franc; (60):1799−1802, 2010.
[abs][pdf][bib]      [code]

FastInf: An Efficient Approximate Inference Library
Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan; (57):1733−1736, 2010.
[abs][pdf][bib]      [code]

MOA: Massive Online Analysis
Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer; (52):1601−1604, 2010.
[abs][pdf][bib]      [code]

SFO: A Toolbox for Submodular Function Optimization
Andreas Krause; (38):1141−1144, 2010.
[abs][pdf][bib]      [code]

Continuous Time Bayesian Network Reasoning and Learning Engine
Christian R. Shelton, Yu Fan, William Lam, Joon Lee, Jing Xu; (37):1137−1140, 2010.
[abs][pdf][bib]      [code]

Error-Correcting Output Codes Library
Sergio Escalera, Oriol Pujol, Petia Radeva; (20):661−664, 2010.
[abs][pdf][bib]      [code]

DL-Learner: Learning Concepts in Description Logics
Jens Lehmann; (91):2639−2642, 2009.
[abs][pdf][bib]      [code]

RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments
Brian Tanner, Adam White; (74):2133−2136, 2009.
[abs][pdf][bib]      [code]

Dlib-ml: A Machine Learning Toolkit
Davis E. King; (60):1755−1758, 2009.
[abs][pdf][bib]      [code]

Model Monitor (M2): Evaluating, Comparing, and Monitoring Models
Troy Raeder, Nitesh V. Chawla; (47):1387−1390, 2009.
[abs][pdf][bib]      [code]

Java-ML: A Machine Learning Library
Thomas Abeel, Yves Van de Peer, Yvan Saeys; (34):931−934, 2009.
[abs][pdf][bib]      [code]

Nieme: Large-Scale Energy-Based Models
Francis Maes; (26):743−746, 2009.
[abs][pdf][bib]      [code]

Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data
Abhik Shah, Peter Woolf; (6):159−162, 2009.
[abs][pdf][bib]      [code]

JNCC2: The Java Implementation Of Naive Credal Classifier 2
Giorgio Corani, Marco Zaffalon; (90):2695−2698, 2008.
[abs][pdf][bib]      [code]

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.
[abs][pdf][bib]      [code]

Shark
Christian Igel, Verena Heidrich-Meisner, Tobias Glasmachers; (33):993−996, 2008.
[abs][pdf][bib]      [code]

A Library for Locally Weighted Projection Regression
Stefan Klanke, Sethu Vijayakumar, Stefan Schaal; (21):623−626, 2008.
[abs][pdf][bib]      [code]

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