Some News

An invited talk at the UGent Data Science Seminar in Ghent, Belgium, June 13, 2019:
The slides from the talk Label tree algorithms for extreme classification can be found here.

The NIPS paper on extreme classification:
Our recent paper with Marek Wydmuch, Kalina Jasinska, Michail Kuznetsov, and Robert BusaFekete has been accepted for NIPS 2018.
The arxiv version of the paper can be found here.

Talk about crossdevice identification of users at IT Research Workshop at IFIP WCC, 21 September 2018:
The slides from the talk can be found here.

Tutorial on multitarget prediction at ECMLPKDD 2018 in Dublin:
With Willem Waegeman and Eyke Hüllermeier we gave a tutorial on multitarget prediction. The slides from the tutorial can be found here.

The Dagstuhl seminar on Extreme Classification, 1520 July 2018:
With Sami Bengio, Thorsten Joachims, Marius Kloft, and Manik Varma we organized the Dagstuhl Seminar on Extreme Classification.

The WebConf workshop on Extreme MultiLabel Classification for Social Media, 23 April 2018:
With Akshay Soni, Aasish Pappu, and Robert BusaFekete from Yahoo! Research we organize a workshop at the prestigous Web Conference on Extreme MultiLabel Classification. For more details check the workshop webpage.

Tutorial on Extreme MultiLabel Classification at European Conference on Information Retrieval:
With Rohit Babbar with gave a tutorial at ECIR: [tutorial homepage].

Presentation at the Seminars of the Intelligent Decision Support Systems Lab:
With Kalina Jasińska and Marek Wydmuch we gave a talk for the IDSS lab about probabilistic label trees: [pdf].

A lecture given during the Predoc Summer School on Learning Systems on Monday, July 3, 2017, at ETH Zürich.
Slides from the lecture: [pdf].

A lecture at Adam Mickiewicz University in Poznań
Slides from my talk on extreme multilabel classification given in a series of open lectures on "Mutlivariate statistical methods for engineering" [pdf].

The 3year project "Consistent and scalable learning algorithms for structured output prediction" financed by the National Science Centre (NCN) has been finished.
The main achievements of the project are:
 A new extreme classficiation algorithm for multilabel problems, referred to as Probabilistic Label Trees [icml paper] [code].
 A theoretical analysis of complex performance measures [MLJ paper]
 An analysis of Fagin' threshold algorithm in a wide spectrum of machine learning problems [DAMI paper]
 A theoretical analysis of probabilistic classifier trees [ECML PKDD paper]
 An online algorithm for Fmeasure maximization [NIPS paper]

An invited talk at the TFML 2017 conference
Slides from my talk on Extreme ZeroShot Learning [pdf].

The Best Paper Award for a paper with Wojciech Kotłowski at the Asian Conference on Machine Learning 2015:
The paper, entitled Surrogate regret bounds for generalized classification
performance metrics, can be found here
Tutorials
Selected publications

Extreme FMeasure Maximization using Sparse Probability Estimates
Kalina Jasinska, Krzysztof Dembczyński, Robert BusaFekete, Karlson Pfannschmidt, Timo Klerx, Eyke Hüllermeier
Proceedings of the 33rd International Conference on Machine
Learning, New York, NY, USA, 2016. JMLR: W&CP, 48 14351444, 2016

On the Bayesoptimality of Fmeasure maximizers
Krzysztof Dembczyński, Willem Waegeman, Arkadiusz Jachnik, Weiwei Cheng, Eyke Hüllermeier
Journal of Machine Learning Research, 15 33333388, 2014

On loss minimization and label dependence in multilabel classification
Willem Waegeman, Krzysztof Dembczyński, Weiwei Cheng, Eyke Hüllermeier
Machine Learning, 88 545, 2013
Listed in Notable Computing Books and Articles 2012 by ACM Computing Reviews

Learning monotone nonlinear models
using the Choquet integral
Ali Fallah Tehrani, Weiwei Cheng, Krzysztof Dembczynski, Eyke Hüllermeier
Machine Learning, 89 183211, 2013

ENDER: a statistical framework for boosting decision rules
Krzysztof Dembczyński, Wojciech Kotłowski, Roman Słowiński
Data Mining and Knowledge Discovery, 21 5290, 2010

Optimizing the Fmeasure in multilabel classification: Plugin rule approach versus structured loss minimization
Krzysztof Dembczyński, Arkadiusz Jachnik, Wojcieh Kotlowski, Willem Waegeman, Eyke Hüllermeier
International Conference on Machine Learning (ICML 2013) 2013

An analysis of chaining in multilabel classification
Krzysztof Dembczyński, Willem Waegeman, Eyke Hüllermeier
European Conference on Artificial Intelligence (ECAI 2012) 294299, 2012
Best Paper Award

Consistent multilabel ranking through univariate losses
Krzysztof Dembczyński, Wojciech Kotłowski, Eyke Hüllermeier
International Conference on Machine Learning (ICML 2012) 2012

An exact algorithm for Fmeasure maximization
Krzysztof Dembczyński, Willem Waegeman, Weiwei Cheng, Eyke Hüllermeier
Advances in Neural Information Processing Systems 25 (NIPS 2011) 2011

Bipartite ranking through minimization of univariate loss
Krzysztof Dembczyński, Wojciech Kotłowski, Eyke Hüllermeier
International Conference on Machine Learning (ICML 2011) 2011

Bayes optimal multilabel classification via probabilistic classifier chains
Krzysztof Dembczyński, Weiwei Cheng, Eyke Hüllermeier
International Conference on Machine Learning (ICML 2010) 2010
You can find more my publications on Google Scholar.
Teaching
Software

extremeText (XT)
extremeText is a our new implementation of probabilistic label trees built upon the fastText package. It significanlty improve fastText on multilabel problems. The github repository: https://github.com/mwydmuch/extremeText.

Extreme MultiLabel Classification (XMLC)
Extreme MultiLabel Classification (XMLC) deals with multilabel problems with hundreds of thousands of labels. Implementation of Probabilistic Label Trees (PLT) suited for this kind of problems can be found here: https://github.com/busarobi/XMLC.

Probabilistic Classifier Chains:
Probabistitic Classifier Chains (PCC) are a learning method for multilabel classification problems. You can find the code in the GitHub repository: https://github.com/multilabelclassification/PCC.

Ensembles of Decision Rules:
There are two fast and accurate boosting algorithms for learning decision rule models. For multiclass problems use MLRules (Maximum Likelihood Rule Ensembles), while for regression problems use RegEnder (regression ensemble of decision rule).
