CONTRIBUTIONS

Individual Contributions {#contributions}

Contribution Statistics

Google Summer of Code Projects

We greatly appreciate the support by Google and the hard work of our students and mentors!

2019

• Detox++ [Ahmed; Viktor, Wuwei]

2018

• Continuous Detoxification [Wuwei; Viktor, Michele]
• Shogun DevOps: inside the black box 2.0 [Shubham; Heiko, Giovanni]

2017

• Data Project: patient monitoring and decision support using health data [Olivier; Lea, Heiko]
• Shogun DevOps: inside the black box [Giovanni; Viktor, Rahul]
• Fundamental Machine Learning Algorithms [Michele; Viktor]
• The Shogun Detox 2 [Weijie; Fernando, Viktor]

2016

• New Parameter Framework and Plugin Based Architecture for Shogun [Sanuj; Sergey, Heiko, Viktor]
• The Shogun Detox: refactor linear algebra and serialization framework [Pan Deng; Heiko, Viktor, Rahul]
• Fundamental ML : The usual suspects [Saurabh; Heiko, Viktor]

2014

• OpenCV Integration and Computer Vision Applications [Abhijeet Kislay; Kevin Hughes]
• Large-Scale Multi-Label Classification [Abinash Panda; Thoralf Klein]
• Large-scale structured prediction with approximate inference [Jiaolong Xu; Shell Hu]
• Essential Deep Learning Modules [Khaled Nasr; Sergey Lisitsyn, Theofanis Karaletsos]
• Fundamental Machine Learning: decision trees, kernel density estimation [Parijat Mazumdar ; Fernando Iglesias]
• Shogun Missionary & Shogun in Education [Saurabh Mahindre; Heiko Strathmann]
• Testing and Measuring Variable Interactions With Kernels [Soumyajit De; Dino Sejdinovic, Heiko Strathmann]
• Variational Learning for Gaussian Processes [Wu Lin; Heiko Strathmann, Emtiyaz Khan]

2013

• Gaussian Processes for binary classification [Roman Votjakov]
• Sampling log-determinants for large sparse matrices [Soumyajit De]
• Metric Learning via LMNN [Fernando Iglesias]
• Independent Component Analysis (ICA) [Kevin Hughes]
• Hashing Feature Framework [Evangelos Anagnostopoulos]
• Structured Output Learning [Hu Shell]
• A web-demo framework [Liu Zhengyang]

2012

• Kernel Hypothesis Testing [Heiko Strathmann]
• Latent SVM [Viktor Gal]
• Multitask Learning [Sergey Listsyn]
• Bundle Methods [Michal Uricar]
• Multiclass methods [Chiyuan Zhang]
• Gaussian Process regression [Jacob Walker]
• Structured Output Framework [Fernando Iglesias]

2011

• Support for new languages [Baozeng Ding]
• Dimensionality reduction algorithms [Sergey Lisitsyn]
• Streaming / Online Feature Framework [Shashwat Lal Das]
• Model selection framework [Heiko Strathmann]
• Gaussian Mixture Models [Alesis Novik]

Individual contributions

Alex J. Smola

• the pr_loqo optimizer

Antoine Bordes

• LaRank

Thorsten Joachims

• SVMLight

Chih-Chung Chang and and Chih-Jen Lin

• LibSVM

Xiang-Rui Wang and Chih-Jen Lin

• LIBLINEAR

Thomas Serafini, Luca Zanni, Gaetano Zanghirati

• the Gradient Projection Decomposition Technique (GPDT) - SVM

Vikas Sindhwani

• SVM-lin: Fast SVM Solvers for Supervised and Semi-supervised Learning

Vojtech Franc

• Generalized Nearest Point Problem Solver based L2 (slacks) SVM
• Optimized Cutting Plane Support Vector Machines (Ocas)

Jean-Philippe Vert and Hiroto Saigo

• Local Alignment Kernel

Leon Bottou

• Stochastic Gradient Descent (SGD) SVM

Marius Kloft

• 2-norm and q-norm MKL
• SMO based true Multi-Class SVM

Alexander Zien

• Newton based q-norm MKL
• POIM code for WD kernels

Christian Gehl

• Distance Metrics

Christian Widmer

• Dual and Multitask Learning
• Serialization support

Jonas Behr

• Structured Learning

Elpmis Lee

• Translation of the documentation to Chinese

Baozeng Ding

• Support for modular java, c#, ruby, lua interfaces

Shashwat Lal Das

• Streaming / Online Feature Framework for SimpleFeatures, SparseFeatures, StringFeatures, SGD-QN, Online SGD, Online Liblinear, Online Vowpal Vabit

Heiko Strathmann

• Model selection/Cross-validation for arbitrary Machines
• Statistics module
• Subset support in features
• Various bugfixes and structural improvements
• Serialization improvements and fixes/ Migration framework
• Machine Locking for precomputed kernel matrices
• Statistical hypothesis testing framework / Kernel Two-Sample/Independence tests

Alesis Novik

• Gaussian Mixture Models

Evgeniy Andreev:

• FibonacciHeap
• Python 3 support
• CoverTree
• HashSet

Justin Patera

• Ruby examples

Daniel Korn

• C# examples

Fernando José Iglesias Garcia

• Generic multiclass OvO training
• Quadratic Discriminant Analysis
• Metric Learning via LMNN

J. Liu, S. Ji and J. Ye

• SLEP: A Sparse Learning Package C and ported code

J. Zhou, J. Chen and J. Ye

• MALSAR: Multi-tAsk Learning via StructurAL Regularization ported code

We also acknowledge support from Alexander Binder, Alexander Zien, Andre Noll, Cheng Soon Ong, Christian Gehl, Christian Widmer, Christoph Lampert, Fabio De Bona, Jonas Behr, Konrad Rieck, Mikio Braun, Torsten Werner, Vojtech Franc, Yaroslav Halchenko