Publications

@inproceedings{tyukin2015scavenger,

title = {Scavenger - A Framework for the Efficient Evaluation of Dynamic and Modular Algorithms},

author = {Andrey Tyukin and Stefan Kramer and J\"{o}rg Wicker},

editor = {Albert Bifet and Michael May and Bianca Zadrozny and Ricard Gavalda and Dino Pedreschi and Jaime Cardoso and Myra Spiliopoulou},

url = {http://dx.doi.org/10.1007/978-3-319-23461-8_40},

doi = {10.1007/978-3-319-23461-8_40},

isbn = {978-3-319-23460-1},

year  = {2015},

date = {2015-01-01},

booktitle = {Machine Learning and Knowledge Discovery in Databases},

volume = {9286},

pages = {325-328},

publisher = {Springer International Publishing},

series = {Lecture Notes in Computer Science},

abstract = {Machine Learning methods and algorithms are often highly modular in the sense that they rely on a large number of subalgorithms that are in principle interchangeable. For example, it is often possible to use various kinds of pre- and post-processing and various base classifiers or regressors as components of the same modular approach. We propose a framework, called Scavenger, that allows evaluating whole families of conceptually similar algorithms efficiently. The algorithms are represented as compositions, couplings and products of atomic subalgorithms. This allows partial results to be cached and shared between different instances of a modular algorithm, so that potentially expensive partial results need not be recomputed multiple times. Furthermore, our framework deals with issues of the parallel execution, load balancing, and with the backup of partial results for the case of implementation or runtime errors. Scavenger is licensed under the GPLv3 and can be downloaded freely at https://github.com/jorro/scavenger.},

keywords = {autoencoders, distributed processing, framework, large-scale, Scavenger},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{tyukin2014bmad,

title = {BMaD -- A Boolean Matrix Decomposition Framework},

author = {Andrey Tyukin and Stefan Kramer and J\"{o}rg Wicker},

editor = {Toon Calders and Floriana Esposito and Eyke H\"{u}llermeier and Rosa Meo},

url = {http://dx.doi.org/10.1007/978-3-662-44845-8_40},

doi = {10.1007/978-3-662-44845-8_40},

isbn = {978-3-662-44844-1},

year  = {2014},

date = {2014-01-01},

booktitle = {Machine Learning and Knowledge Discovery in Databases},

volume = {8726},

pages = {481-484},

publisher = {Springer Berlin Heidelberg},

series = {Lecture Notes in Computer Science},

abstract = {Boolean matrix decomposition is a method to obtain a compressed 

representation of a matrix with Boolean entries. We present a modular 

framework that unifies several Boolean matrix decomposition algorithms, and 

provide methods to evaluate their performance. The main advantages of 

the framework are its modular approach and hence the flexible 

combination of the steps of a Boolean matrix decomposition and the 

capability of handling missing values. The framework is licensed under 

the GPLv3 and can be downloaded freely at 

urlhttp://projects.informatik.uni-mainz.de/bmad.},

keywords = {Boolean matrix decomposition, data mining, framework},

pubstate = {published},

tppubtype = {inproceedings}

}