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 codePDF BibTeX

• 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 codePDF BibTeX

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

  Jonas Eschmann, Dario Albani, Giuseppe Loianno (301):1−19, 2024 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• pgmpy: A Python Toolkit for Bayesian Networks

  Ankur Ankan, Johannes Textor (265):1−8, 2024 codePDF BibTeX

• PromptBench: A Unified Library for Evaluation of Large Language Models

  Kaijie Zhu, Qinlin Zhao, Hao Chen, Jindong Wang, Xing Xie (254):1−22, 2024 codePDF BibTeX

• 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 codePDF BibTeX

• BenchMARL: Benchmarking Multi-Agent Reinforcement Learning

  Matteo Bettini, Amanda Prorok, Vincent Moens (217):1−10, 2024 codePDF BibTeX

• PAMI: An Open-Source Python Library for Pattern Mining

  Uday Kiran Rage, Veena Pamalla, Masashi Toyoda, Masaru Kitsuregawa (209):1−6, 2024 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• ptwt - The PyTorch Wavelet Toolbox

  Moritz Wolter, Felix Blanke, Jochen Garcke, Charles Tapley Hoyt (80):1−7, 2024 codePDF BibTeX

• On Unbiased Estimation for Partially Observed Diffusions

  Jeremy Heng, Jeremie Houssineau, Ajay Jasra (66):1−66, 2024 codePDF BibTeX

• 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 codePDF BibTeX

• Invariant and Equivariant Reynolds Networks

  Akiyoshi Sannai, Makoto Kawano, Wataru Kumagai (42):1−36, 2024 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• Avalanche: A PyTorch Library for Deep Continual Learning

  Antonio Carta, Lorenzo Pellegrini, Andrea Cossu, Hamed Hemati, Vincenzo Lomonaco (363):1−6, 2023 codePDF BibTeX

• 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 codePDF BibTeX

• Fairlearn: Assessing and Improving Fairness of AI Systems

  Hilde Weerts, Miroslav Dudík, Richard Edgar, Adrin Jalali, Roman Lutz, Michael Madaio (257):1−8, 2023 codePDF BibTeX

• 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 codePDF BibTeX

• skrl: Modular and Flexible Library for Reinforcement Learning

  Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Simon Bøgh, Nestor Arana-Arexolaleiba (254):1−9, 2023 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• LibMTL: A Python Library for Deep Multi-Task Learning

  Baijiong Lin, Yu Zhang (209):1−7, 2023 codePDF BibTeX

• L0Learn: A Scalable Package for Sparse Learning using L0 Regularization

  Hussein Hazimeh, Rahul Mazumder, Tim Nonet (205):1−8, 2023 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• SQLFlow: An Extensible Toolkit Integrating DB and AI

  Jun Zhou, Ke Zhang, Lin Wang, Hua Wu, Yi Wang, ChaoChao Chen (116):1−9, 2023 codePDF BibTeX

• FedLab: A Flexible Federated Learning Framework

  Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu (100):1−7, 2023 codePDF BibTeX

• 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 codePDF BibTeX

• HiClass: a Python Library for Local Hierarchical Classification Compatible with Scikit-learn

  Fábio M. Miranda, Niklas Köhnecke, Bernhard Y. Renard (29):1−17, 2023 codePDF BibTeX

• Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping

  XuranMeng, JeffYao (28):1−40, 2023 codePDF BibTeX

• Python package for causal discovery based on LiNGAM

  Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu (14):1−8, 2023 codePDF BibTeX

• AutoKeras: An AutoML Library for Deep Learning

  Haifeng Jin, François Chollet, Qingquan Song, Xia Hu (6):1−6, 2023 codePDF BibTeX

• OMLT: Optimization & Machine Learning Toolkit

  Francesco Ceccon, Jordan Jalving, Joshua Haddad, Alexander Thebelt, Calvin Tsay, Carl D Laird, Ruth Misener (349):1−8, 2022 codePDF BibTeX

• 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 codePDF BibTeX

• d3rlpy: An Offline Deep Reinforcement Learning Library

  Takuma Seno, Michita Imai (315):1−20, 2022 codePDF BibTeX

• JsonGrinder.jl: automated differentiable neural architecture for embedding arbitrary JSON data

  Šimon Mandlík, Matěj Račinský, Viliam Lisý, Tomáš Pevný (298):1−5, 2022 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• InterpretDL: Explaining Deep Models in PaddlePaddle

  Xuhong Li, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Zeyu Chen, Dejing Dou (197):1−6, 2022 codePDF BibTeX

• ktrain: A Low-Code Library for Augmented Machine Learning

  Arun S. Maiya (158):1−6, 2022 codePDF BibTeX

• 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 codePDF BibTeX

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

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

• SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization

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

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

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

• Toolbox for Multimodal Learn (scikit-multimodallearn)

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

• Stable-Baselines3: Reliable Reinforcement Learning Implementations

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

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

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

• sklvq: Scikit Learning Vector Quantization

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

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

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

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

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

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

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

• mlr3pipelines - Flexible Machine Learning Pipelines in R

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

• Alibi Explain: Algorithms for Explaining Machine Learning Models

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

• The ensmallen library for flexible numerical optimization

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

• MushroomRL: Simplifying Reinforcement Learning Research

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

• River: machine learning for streaming data in Python

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

• mvlearn: Multiview Machine Learning in Python

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

• OpenML-Python: an extensible Python API for OpenML

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

• POT: Python Optimal Transport

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

• ChainerRL: A Deep Reinforcement Learning Library

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

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

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

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

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

• Pykg2vec: A Python Library for Knowledge Graph Embedding

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

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

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

• Geomstats: A Python Package for Riemannian Geometry in Machine Learning

  Nina Miolane, Nicolas Guigui, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Daniel Brooks, Bernhard Kainz, Claire Donnat, Susan Holmes, Xavier Pennec (223):1−9, 2020 codePDF BibTeX

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

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

• Scikit-network: Graph Analysis in Python

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

• apricot: Submodular selection for data summarization in Python

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

• metric-learn: Metric Learning Algorithms in Python

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

• Probabilistic Learning on Graphs via Contextual Architectures

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

• AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models

  Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John T. Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang (130):1−6, 2020 codePDF BibTeX

• Apache Mahout: Machine Learning on Distributed Dataflow Systems

  Robin Anil, Gokhan Capan, Isabel Drost-Fromm, Ted Dunning, Ellen Friedman, Trevor Grant, Shannon Quinn, Paritosh Ranjan, Sebastian Schelter, Özgür Yılmazel (127):1−6, 2020 codePDF BibTeX

• Tslearn, A Machine Learning Toolkit for Time Series Data

  Romain Tavenard, Johann Faouzi, Gilles Vandewiele, Felix Divo, Guillaume Androz, Chester Holtz, Marie Payne, Roman Yurchak, Marc Rußwurm, Kushal Kolar, Eli Woods (118):1−6, 2020 codePDF BibTeX

• GluonTS: Probabilistic and Neural Time Series Modeling in Python

  Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, Yuyang Wang (116):1−6, 2020 codePDF BibTeX

• MFE: Towards reproducible meta-feature extraction

  Edesio Alcobaça, Felipe Siqueira, Adriano Rivolli, Luís P. F. Garcia, Jefferson T. Oliva, André C. P. L. F. de Carvalho (111):1−5, 2020 codePDF BibTeX

• ThunderGBM: Fast GBDTs and Random Forests on GPUs

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

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

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

• pyDML: A Python Library for Distance Metric Learning

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

• Cornac: A Comparative Framework for Multimodal Recommender Systems

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

• Kymatio: Scattering Transforms in Python

  Mathieu Andreux, Tomás Angles, Georgios Exarchakis, Roberto Leonarduzzi, Gaspar Rochette, Louis Thiry, John Zarka, Stéphane Mallat, Joakim Andén, Eugene Belilovsky, Joan Bruna, Vincent Lostanlen, Muawiz Chaudhary, Matthew J. Hirn, Edouard Oyallon, Sixin Zhang, Carmine Cella, Michael Eickenberg (60):1−6, 2020 codePDF BibTeX

• GraKeL: A Graph Kernel Library in Python

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

• pyts: A Python Package for Time Series Classification

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

• Tensor Train Decomposition on TensorFlow (T3F)

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

• ORCA: A Matlab/Octave Toolbox for Ordinal Regression

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

• PyOD: A Python Toolbox for Scalable Outlier Detection

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

• iNNvestigate Neural Networks!

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

• AffectiveTweets: a Weka Package for Analyzing Affect in Tweets

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

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

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

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

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

• Pyro: Deep Universal Probabilistic Programming

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

• TensorLy: Tensor Learning in Python

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

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

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

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

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

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

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

• Scikit-Multiflow: A Multi-output Streaming Framework

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

• OpenEnsembles: A Python Resource for Ensemble Clustering

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

• ThunderSVM: A Fast SVM Library on GPUs and CPUs

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

• ELFI: Engine for Likelihood-Free Inference

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

• SGDLibrary: A MATLAB library for stochastic optimization algorithms

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

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

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

• KELP: a Kernel-based Learning Platform

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

• Pycobra: A Python Toolbox for Ensemble Learning and Visualisation

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

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

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

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

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

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

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

• GPflow: A Gaussian Process Library using TensorFlow

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

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

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

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

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

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

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

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

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

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

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

• Refinery: An Open Source Topic Modeling Web Platform

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

• SnapVX: A Network-Based Convex Optimization Solver

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

• fastFM: A Library for Factorization Machines

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

• Megaman: Scalable Manifold Learning in Python

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

• JCLAL: A Java Framework for Active Learning

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

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

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

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

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

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

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

• MLlib: Machine Learning in Apache Spark

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

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

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

• Harry: A Tool for Measuring String Similarity

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

• partykit: A Modular Toolkit for Recursive Partytioning in R

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

• CEKA: A Tool for Mining the Wisdom of Crowds

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

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

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

• The Libra Toolkit for Probabilistic Models

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

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

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

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

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

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

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

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

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

• A Classification Module for Genetic Programming Algorithms in JCLEC

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

• SAMOA: Scalable Advanced Massive Online Analysis

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

• BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits

  Ruben Martinez-Cantin (115):3915−3919, 2014 codePDF BibTeX

• 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 codePDF BibTeX

• The Gesture Recognition Toolkit

  Nicholas Gillian, Joseph A. Paradiso (101):3483−3487, 2014 codePDF BibTeX

• ooDACE Toolbox: A Flexible Object-Oriented Kriging Implementation

  Ivo Couckuyt, Tom Dhaene, Piet Demeester (91):3183−3186, 2014 codePDF BibTeX

• pystruct - Learning Structured Prediction in Python

  Andreas C. Müller, Sven Behnke (59):2055−2060, 2014 codePDF BibTeX

• Manopt, a Matlab Toolbox for Optimization on Manifolds

  Nicolas Boumal, Bamdev Mishra, P.-A. Absil, Rodolphe Sepulchre (42):1455−1459, 2014 codePDF BibTeX

• 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 codePDF BibTeX

• LIBOL: A Library for Online Learning Algorithms

  Steven C.H. Hoi, Jialei Wang, Peilin Zhao (15):495−499, 2014 codePDF BibTeX

• 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 codePDF BibTeX

• Information Theoretical Estimators Toolbox

  Zoltán Szabó (9):283−287, 2014 codePDF BibTeX

• 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 codePDF BibTeX

• GURLS: A Least Squares Library for Supervised Learning

  Andrea Tacchetti, Pavan K. Mallapragada, Matteo Santoro, Lorenzo Rosasco (100):3201−3205, 2013 codePDF BibTeX

• Divvy: Fast and Intuitive Exploratory Data Analysis

  Joshua M. Lewis, Virginia R. de Sa, Laurens van der Maaten (98):3159−3163, 2013 codewebpagePDF BibTeX

• QuantMiner for Mining Quantitative Association Rules

  Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod, Daniel Cassard (97):3153−3157, 2013 codePDF BibTeX

• 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 codePDF BibTeX

• BudgetedSVM: A Toolbox for Scalable SVM Approximations

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

• Tapkee: An Efficient Dimension Reduction Library

  Sergey Lisitsyn, Christian Widmer, Fernando J. Iglesias Garcia (72):2355−2359, 2013 codePDF BibTeX

• 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 codePDF BibTeX

• JKernelMachines: A Simple Framework for Kernel Machines

  David Picard, Nicolas Thome, Matthieu Cord (43):1417−1421, 2013 codePDF BibTeX

• GPstuff: Bayesian Modeling with Gaussian Processes

  Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari (35):1175−1179, 2013 codePDF BibTeX

• 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 codePDF BibTeX

• A C++ Template-Based Reinforcement Learning Library: Fitting the Code to the Mathematics

  Hervé Frezza-Buet, Matthieu Geist (18):625−628, 2013 codePDF BibTeX

• SVDFeature: A Toolkit for Feature-based Collaborative Filtering

  Tianqi Chen, Weinan Zhang, Qiuxia Lu, Kailong Chen, Zhao Zheng, Yong Yu (116):3619−3622, 2012 codePDF BibTeX

• DARWIN: A Framework for Machine Learning and Computer Vision Research and Development

  Stephen Gould (113):3533−3537, 2012 codePDF BibTeX

• Sally: A Tool for Embedding Strings in Vector Spaces

  Konrad Rieck, Christian Wressnegger, Alexander Bikadorov (104):3247−3251, 2012 codePDF BibTeX

• 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 codePDF BibTeX

• PREA: Personalized Recommendation Algorithms Toolkit

  Joonseok Lee, Mingxuan Sun, Guy Lebanon (87):2699−2703, 2012 codePDF BibTeX

• A Topic Modeling Toolbox Using Belief Propagation

  Jia Zeng (73):2233−2236, 2012 codePDF BibTeX

• DEAP: Evolutionary Algorithms Made Easy

  Félix-Antoine Fortin, François-Michel De Rainville, Marc-André Gardner, Marc Parizeau, Christian Gagné (70):2171−2175, 2012 codePDF BibTeX

• Pattern for Python

  Tom De Smedt, Walter Daelemans (66):2063−2067, 2012 codePDF BibTeX

• 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 codePDF BibTeX

• glm-ie: Generalised Linear Models Inference & Estimation Toolbox

  Hannes Nickisch (54):1699−1703, 2012 codePDF BibTeX

• 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 codePDF BibTeX

• NIMFA : A Python Library for Nonnegative Matrix Factorization

  Marinka Žitnik, Blaž Zupan (30):849−853, 2012 codePDF BibTeX

• GPLP: A Local and Parallel Computation Toolbox for Gaussian Process Regression

  Chiwoo Park, Jianhua Z. Huang, Yu Ding (26):775−779, 2012 codePDF BibTeX

• ML-Flex: A Flexible Toolbox for Performing Classification Analyses In Parallel

  Stephen R. Piccolo, Lewis J. Frey (19):555−559, 2012 codePDF BibTeX

• MULTIBOOST: A Multi-purpose Boosting Package

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

• The Stationary Subspace Analysis Toolbox

  Jan Saputra Müller, Paul von Bünau, Frank C. Meinecke, Franz J. Király, Klaus-Robert Müller (93):3065−3069, 2011 codePDF BibTeX

• 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 codePDF BibTeX

• LPmade: Link Prediction Made Easy

  Ryan N. Lichtenwalter, Nitesh V. Chawla (75):2489−2492, 2011 codePDF BibTeX

• MULAN: A Java Library for Multi-Label Learning

  Grigorios Tsoumakas, Eleftherios Spyromitros-Xioufis, Jozef Vilcek, Ioannis Vlahavas (71):2411−2414, 2011 codePDF BibTeX

• Waffles: A Machine Learning Toolkit

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

• MSVMpack: A Multi-Class Support Vector Machine Package

  Fabien Lauer, Yann Guermeur (66):2293−2296, 2011 codePDF BibTeX

• 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 codePDF BibTeX

• CARP: Software for Fishing Out Good Clustering Algorithms

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

• Gaussian Processes for Machine Learning (GPML) Toolbox

  Carl Edward Rasmussen, Hannes Nickisch (100):3011−3015, 2010 codePDF BibTeX

• libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical Models

  Joris M. Mooij (74):2169−2173, 2010 codePDF BibTeX

• Model-based Boosting 2.0

  Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner (71):2109−2113, 2010 codePDF BibTeX

• 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 codePDF BibTeX

• 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 codePDF BibTeX

• FastInf: An Efficient Approximate Inference Library

  Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elidan (57):1733−1736, 2010 codePDF BibTeX

• MOA: Massive Online Analysis

  Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (52):1601−1604, 2010 codePDF BibTeX

• SFO: A Toolbox for Submodular Function Optimization

  Andreas Krause (38):1141−1144, 2010 codePDF BibTeX

• Continuous Time Bayesian Network Reasoning and Learning Engine

  Christian R. Shelton, Yu Fan, William Lam, Joon Lee, Jing Xu (37):1137−1140, 2010 codePDF BibTeX

• Error-Correcting Output Codes Library

  Sergio Escalera, Oriol Pujol, Petia Radeva (20):661−664, 2010 codePDF BibTeX

• DL-Learner: Learning Concepts in Description Logics

  Jens Lehmann (91):2639−2642, 2009 codePDF BibTeX

• RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments

  Brian Tanner, Adam White (74):2133−2136, 2009 codePDF BibTeX

• Dlib-ml: A Machine Learning Toolkit

  Davis E. King (60):1755−1758, 2009 codePDF BibTeX

• Model Monitor (M2): Evaluating, Comparing, and Monitoring Models

  Troy Raeder, Nitesh V. Chawla (47):1387−1390, 2009 codePDF BibTeX

• Java-ML: A Machine Learning Library

  Thomas Abeel, Yves Van de Peer, Yvan Saeys (34):931−934, 2009 codePDF BibTeX

• Nieme: Large-Scale Energy-Based Models

  Francis Maes (26):743−746, 2009 codePDF BibTeX

• Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data

  Abhik Shah, Peter Woolf (6):159−162, 2009 codePDF BibTeX

• JNCC2: The Java Implementation Of Naive Credal Classifier 2

  Giorgio Corani, Marco Zaffalon (90):2695−2698, 2008 codePDF BibTeX

• LIBLINEAR: A Library for Large Linear Classification

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

• Shark

  Christian Igel, Verena Heidrich-Meisner, Tobias Glasmachers (33):993−996, 2008 codePDF BibTeX

• A Library for Locally Weighted Projection Regression

  Stefan Klanke, Sethu Vijayakumar, Stefan Schaal (21):623−626, 2008 codePDF BibTeX