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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.

Aequitas Flow: Streamlining Fair ML Experimentation

Sérgio Jesus, Pedro Saleiro, Inês Oliveira e Silva, Beatriz M. Jorge, Rita P. Ribeiro, João Gama, Pedro Bizarro, Rayid Ghani; (354):1−7, 2024.
[abs][pdf][bib]      [code]

Open-Source Conversational AI with SpeechBrain 1.0

Mirco Ravanelli, Titouan Parcollet, Adel Moumen, Sylvain de Langen, Cem Subakan, Peter Plantinga, Yingzhi Wang, Pooneh Mousavi, Luca Della Libera, Artem Ploujnikov, Francesco Paissan, Davide Borra, Salah Zaiem, Zeyu Zhao, Shucong Zhang, Georgios Karakasidis, Sung-Lin Yeh, Pierre Champion, Aku Rouhe, Rudolf Braun, Florian Mai, Juan Zuluaga-Gomez, Seyed Mahed Mousavi, Andreas Nautsch, Ha Nguyen, Xuechen Liu, Sangeet Sagar, Jarod Duret, Salima Mdhaffar, Gaëlle Laperrière, Mickael Rouvier, Renato De Mori, Yannick Estève; (333):1−11, 2024.
[abs][pdf][bib]      [code]

RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control

Jonas Eschmann, Dario Albani, Giuseppe Loianno; (301):1−19, 2024.
[abs][pdf][bib]      [code]

PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization

Qiqi Duan, Guochen Zhou, Chang Shao, Zhuowei Wang, Mingyang Feng, Yuwei Huang, Yajing Tan, Yijun Yang, Qi Zhao, Yuhui Shi; (296):1−28, 2024.
[abs][pdf][bib]      [code]

skscope: Fast Sparsity-Constrained Optimization in Python

Zezhi Wang, Junxian Zhu, Xueqin Wang, Jin Zhu, Huiyang Pen, Peng Chen, Anran Wang, Xiaoke Zhang; (290):1−9, 2024.
[abs][pdf][bib]      [code]

aeon: a Python Toolkit for Learning from Time Series

Matthew Middlehurst, Ali Ismail-Fawaz, Antoine Guillaume, Christopher Holder, David Guijo-Rubio, Guzal Bulatova, Leonidas Tsaprounis, Lukasz Mentel, Martin Walter, Patrick Schäfer, Anthony Bagnall; (289):1−10, 2024.
[abs][pdf][bib]      [code]

OmniSafe: An Infrastructure for Accelerating Safe Reinforcement Learning Research

Jiaming Ji, Jiayi Zhou, Borong Zhang, Juntao Dai, Xuehai Pan, Ruiyang Sun, Weidong Huang, Yiran Geng, Mickel Liu, Yaodong Yang; (285):1−6, 2024.
[abs][pdf][bib]      [code]

Pearl: A Production-Ready Reinforcement Learning Agent

Zheqing Zhu, Rodrigo de Salvo Braz, Jalaj Bhandari, Daniel Jiang, Yi Wan, Yonathan Efroni, Liyuan Wang, Ruiyang Xu, Hongbo Guo, Alex Nikulkov, Dmytro Korenkevych, Urun Dogan, Frank Cheng, Zheng Wu, Wanqiao Xu; (273):1−30, 2024.
[abs][pdf][bib]      [code]

pgmpy: A Python Toolkit for Bayesian Networks

Ankur Ankan, Johannes Textor; (265):1−8, 2024.
[abs][pdf][bib]      [code]

PromptBench: A Unified Library for Evaluation of Large Language Models

Kaijie Zhu, Qinlin Zhao, Hao Chen, Jindong Wang, Xing Xie; (254):1−22, 2024.
[abs][pdf][bib]      [code]

Fortuna: A Library for Uncertainty Quantification in Deep Learning

Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias Seeger, Andrew Gordon Wilson, Cedric Archambeau; (238):1−7, 2024.
[abs][pdf][bib]      [code]

BenchMARL: Benchmarking Multi-Agent Reinforcement Learning

Matteo Bettini, Amanda Prorok, Vincent Moens; (217):1−10, 2024.
[abs][pdf][bib]      [code]

PAMI: An Open-Source Python Library for Pattern Mining

Uday Kiran Rage, Veena Pamalla, Masashi Toyoda, Masaru Kitsuregawa; (209):1−6, 2024.
[abs][pdf][bib]      [code]

DoWhy-GCM: An Extension of DoWhy for Causal Inference in Graphical Causal Models

Patrick Blöbaum, Peter Götz, Kailash Budhathoki, Atalanti A. Mastakouri, Dominik Janzing; (147):1−7, 2024.
[abs][pdf][bib]      [code]

PyGOD: A Python Library for Graph Outlier Detection

Kay Liu, Yingtong Dou, Xueying Ding, Xiyang Hu, Ruitong Zhang, Hao Peng, Lichao Sun, Philip S. Yu; (141):1−9, 2024.
[abs][pdf][bib]      [code]

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]

© JMLR .