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Machine Learning and Data Mining Research at DIKU

The amount and complexity of available data is steadily increasing. To make use of this wealth ofinformation, computing systems are needed that turn the data into knowledge. Machine learning isabout developing the required software that automatically analyses data for making predictions,categorizations, and recommendations. Machine learning algorithms are already an integral part oftoday's computing systems ­ for example in search engines, recommender systems, or biometricalapplications ­ and have reached superhuman performance in some domains. DIKU's researchpushes the boundaries and aims at more robust, more efficient, and more widely applicablemachine learning techniques.

State­of­the­art machine learningMachine learning is a branch of computer science and applied statistics covering software thatimproves its performance at a given task based on sample data or experience. The machinelearning research at DIKU, the Department of Computer Science at the University of Copenhagen,is concerned with the design and analysis of adaptive systems for pattern recognition andbehaviour generation.

We develop machine learning algorithms for making new discoveries in science[image from SkyML project].

Our fields of expertise are

classification, regression, and density estimation techniques for data mining and modelling,pattern recognition, and time series prediction; andcomputational intelligence methods for non­linear optimisation including vector optimisation andmulti­criteria decision making.

Successful real­world applications include the design of biometric and medical image processing

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Medical image analysis is a major application area[taken from Prasoon et al., 2012, (top) and

Winter et al., 2008 (bottom)].

We apply machine learning algorithms forhydroacoustic signal classification tosupport the verification of the

Comprehensive Nuclear­Test­Ban Treaty[Tuma et al., 2012].

systems, chemical processes and plants, advanceddriver assistance systems, robot controllers, timeseries predictors for physical processes, systems forsports analytics, acoustic signal classification systems,automatic quality control for production lines, andsequence analysis in bioinformatics.

To build efficient and autonomous machine learningsystems we draw inspiration from optimisation andcomputing theory as well as biological informationprocessing. We analyse our algorithms theoretically andcritically evaluate them on real­world problems.Increasing the robustness and improving scalability ofself­adaptive, learning computer systems are cross­cutting issues in our work. The following sectionshighlight some of our research activities.

Efficient autonomous machinelearningWe strive for computer systems that can dealautonomously and flexibly with our needs. They mustwork in scenarios that have not been fully specified and must be able to cope with unpredictedsituations. Incomplete descriptions of application scenarios are inevitable because we needalgorithms for domains where the designer's knowledge is not perfect, the solutions to particularproblems are simply unknown, and/or the sheer complexity and variability of the task and theenvironment precludes a sufficiently accurate domain description. Although such systems are ingeneral too complex to be designed manually, large amounts of data describing the task and theenvironment are often available or can be automatically obtained. To take proper advantage of thisavailable information, we need to develop systems that self­adapt and automatically improvebased on sample data – systems that learn.

Machine learning algorithms are already an integral part oftoday's computing systems, for example in internet searchengines, recommender systems, or biometrical applications.Highly specialised technical solutions for restricted taskdomains exist that have reached superhuman performance.Despite these successes, there are fundamental challengesthat must be met if we are to develop more general learningsystems.

First, present adaptive systems often lack autonomy androbustness. For example, they usually require a human expertto select the training examples, the learning method and itsparameters, and an appropriate representation or structure forthe learning system. This dependence on expert supervision isretarding the ubiquitous deployment of adaptive softwaresystems. We therefore work on algorithms that can handle large multimodal data sets, thatactively select training patterns, and that autonomously build appropriate internal representationsbased on data from different sources. These representations should foster learning, generalisation,and communication. Second, current adaptive systems succumb to scalability problems.

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Covariance matrix adaptationevolution strategy (CMA­ES).

On the one hand, the ever growing amounts of data require highly efficient large­scale learningalgorithms. On the other hand, learning and generalisation from very few examples is also achallenging problem. This scenario often occurs in man­machine interaction, for example insoftware personalisation or when generalisation from few database queries is required. We addressthe scaling problems by using task­specific architectures incorporating both new concepts inspiredby natural adaptive systems as well as recent methods from algorithmic engineering andmathematical programming.

Selected methodsWe address all major learning paradigms, unsupervised, supervised, and reinforcement learning.These are closely connected. For instance, unsupervised learning can be used to find appropriaterepresentations for supervised learning and reliable supervised learning techniques are theprerequisite for successful reinforcement learning. Over the years, we used, analysed, and refineda broad spectrum of machine learning techniques. Currently our methodological research focuseson the following methods.

Supervised learning

Schema of multi­class support vector machine classification[taken from Dogan et al., 2011].

Support vector machines (SVMs) and other kernel­based algorithms are state­of­the­art in patternrecognition. They perform well in many applications, especially in classification tasks. The kerneltrick allows for an easy handling of non­standard data (e.g., biological sequences, multimodal data)and permits a better mathematical analysis of the adaptive system because of the convenientstructure of the hypothesis space. Developing and analysing kernel­based methods, in particularincreasing autonomy and improving scalability of SVMs, is currently one of the most activebranches of our research.

Reinforcement learningThe feedback in today's most challenging applications for adaptivesystems is sparse, unspecific, and/or delayed, for instance inautonomous robotics or in man­machine interaction. Supervised learningcannot be used directly in such a case, but the task can be cast into areinforcement learning (RL) problem. Reinforcement learning is learningfrom the consequences of interactions with an environment without beingexplicitly taught. Because the performance of standard RL techniques isfalling short of expectations, we are developing new RL algorithmsrelying on gradient­based and evolutionary direct policy search.

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Markov random fieldfor rerepresenting data.

Contributing hypervolume ofcandidate solutions in

multi­objective optimization[Suttorp et al, 2006].

Direct policy search for adaptation in intelligent driver assistance systems[taken from Pellecchia et al., 2005].

Unsupervised and deep learningWe employ probabilistic generative models to learn and to describe probabilitydistributions. Our research focuses on Markov random fields, in which theconditional independence structure between random variables is described byan undirected graph. We are particularly interested in models that allow forlearning hierarchical representations of data in an unsupervised manner.

Non­linear optimisationLearning is closely linked to optimisation. Thus, we are alsoworking on general gradient­based and direct search andoptimisation algorithms. This includes randomised methods,especially evolutionary algorithms (EAs), which are inspiredby neo­Darwinian evolution theory. Efficient evolutionaryoptimisation can be achieved by an automatic adjustment ofthe search strategy. We are developing EAs with this ability,especially real­valued EAs that learn the metric underlying theproblem at hand (e.g., dependencies between variables).Currently, we are working on variable­metric EAs for RL andfor efficient vector (multi­objective) optimisation. The latterwill become increasingly relevant for industrial and scientificapplications in the future, because many problems areinherently multi­objective.

Team

Pengfei DiaoFabian GiesekeOswin KrauseChristian IgelMichiel KallenbergJan KremerDídac Rodríguez ArbonèsYevgeny SeldinKristoffer Stensbo­SmidtLauge Sørensen

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Matthias Tuma

Selected PublicationsPlease click here for a full list of Christian's papers and here for a full list of Yevgeny's papers.

Fabian Gieseke, Justin Heinermann, Cosmin Oancea, and Christian Igel. Buffer k­d Trees:Processing Massive Nearest Neighbor Queries on GPUs. JMLR W&CP 32 (ICML) pp. 172­180,2014

Yevgeny Seldin, Peter L. Bartlett, Koby Crammer, and Yasin Abbasi­Yadkori. Prediction with limitedadvice and multiarmed bandits with paid observations. In JMLR W&CP, 32 (ICML), 2014

Yevgeny Seldin and Aleksandrs Slivkins. One practical algorithm for both stochastic and adversarialbandits. In JMLR W&CP, 32 (ICML), 2014

Kai Brügge, Asja Fischer, and Christian Igel. The flip­the­state transition operator for restrictedBoltzmann machines. Machine Learning 13, pp. 53­69, 2013

Fabian Gieseke, Christian Igel, and Tapio Pahikkala. Polynomial runtime bounds for fixed­rankunsupervised least­squares classification. JMLR W&CP 29 (ACML), pp. 62­71, 2013

Oswin Krause, Asja Fischer, Tobias Glasmachers, and Christian Igel. Approximation properties ofDBNs with binary hidden units and real­valued visible units. JMLR W&CP 28 (ICML), pp. 419–426, 2013

Ilya Tolstikhin and Yevgeny Seldin. PAC­Bayes­Empirical­Bernstein Inequality. In Advances inNeural Information Processing Systems (NIPS), 2013

Kim Steenstrup Pedersen, Kristoffer Stensbo­Smidt, Andrew Zirm, and Christian Igel. Shape IndexDescriptors Applied to Texture­Based Galaxy Analysis. International Conference on ComputerVision (ICCV), pp 2440­2447, IEEE Press, 2013

Yevgeny Seldin, François Laviolette, Nicolò Cesa­Bianchi, John Shawe­Taylor, and Peter Auer. PAC­Bayesian inequalities for martingales. IEEE Transactions on Information Theory, 58(12), pp.7086­7093, 2012

Asja Fischer and Christian Igel. Bounding the Bias of Contrastive Divergence Learning. NeuralComputation 23, pp. 664­673, 2011

Yevgeny Seldin, Peter Auer, François Laviolette, John Shawe­Taylor, and Ronald Ortner. PAC­Bayesian analysis of contextual bandits. In Advances in Neural Information ProcessingSystems (NIPS), 2011

Tobias Glasmachers and Christian Igel. Maximum Likelihood Model Selection for 1­Norm SoftMargin SVMs with Multiple Parameters. IEEE Transactions on Pattern Analysis and MachineIntelligence 32(8), pp. 1522­1528, 2010 source code

Yevgeny Seldin and Naftali Tishby. PAC­Bayesian analysis of co­clustering and beyond. Journal ofMachine Learning Research 11, pp. 3595−3646, 2010

Thorsten Suttorp, Nikolaus Hansen, and Christian Igel. Efficient Covariance Matrix Update forVariable Metric Evolution Strategies. Machine Learning 75, pp. 167­197, 2009 source code

Verena Heidrich­Meisner and Christian Igel. Hoeffding and Bernstein Races for Selecting Policies inEvolutionary Direct Policy Search. In L. Bottou and M. Littman, eds.: Proceedings of the

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Department of Computer ScienceUniversity of CopenhagenUniversitetsparken 1 2100 København Ø

International Conference on Machine Learning (ICML 2009), pp. 401­408, 2009

Christian Igel, Verena Heidrich­Meisner, and Tobias Glasmachers. Shark. Journal of MachineLearning Research 9, pp. 993­996, 2008 source code

Tobias Glasmachers and Christian Igel. Maximum­Gain Working Set Selection for SVMs. Journal ofMachine Learning Research 7, pp. 1437­1466, 2006 source code

ContactChristian Igel, Professor mso, Dr. habil.

University of CopenhagenUniversitetsparken 52100 København Ø

Email: igel@diku.dkOffice:HCØ ­ Building E, Office 4.0.2Phone:(+45) 21849673

Contact:The Image Group / Machine Learning Lab

webmaster@diku.dk