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All Outputs (12)

Active search for computer-aided drug design (2018)
Journal Article
Oglic, D., Oatley, S. A., Macdonald, S. J., McInally, T., Garnett, R., Hirst, J. D., & Gärtner, T. (in press). Active search for computer-aided drug design. Molecular Informatics, 37, https://doi.org/10.1002/minf.201700130

We consider lead discovery as active search in a space of labelled graphs. In particular, we extend our recent data-driven adaptive Markov chain approach, and evaluate it on a focused drug design problem, where we search for an antagonist of an av in... Read More about Active search for computer-aided drug design.

Co-regularised support vector regression (2017)
Conference Proceeding
Ullrich, K., Kamp, M., Gärtner, T., Vogt, M., & Wrobel, S. (2017). Co-regularised support vector regression.

We consider a semi-supervised learning scenario for regression, where only few labelled examples, many unlabelled instances and different data representations (multiple views) are available. For this setting, we extend support vector regression with... Read More about Co-regularised support vector regression.

Nyström method with Kernel K-means++ samples as landmarks (2017)
Conference Proceeding
Oglic, D., & Gaertner, T. (2017). Nyström method with Kernel K-means++ samples as landmarks.

We investigate, theoretically and empirically, the effectiveness of kernel K-means++ samples as landmarks in the Nyström method for low-rank approximation of kernel matrices. Previous empirical studies (Zhang et al., 2008; Kumar et al.,2012) observe... Read More about Nyström method with Kernel K-means++ samples as landmarks.

Active search in intensionally specified structured spaces (2017)
Conference Proceeding
Oglic, D., Garnett, R., & Gärtner, T. (2017). Active search in intensionally specified structured spaces.

We consider an active search problem in intensionally specified structured spaces. The ultimate goal in this setting is to discover structures from structurally different partitions of a fixed but unknown target class. An example of such a process is... Read More about Active search in intensionally specified structured spaces.

Greedy feature construction (2016)
Journal Article
Oglic, D., & Gaertner, T. (2016). Greedy feature construction. Advances in Neural Information Processing Systems, 29,

We present an effective method for supervised feature construction. The main goal of the approach is to construct a feature representation for which a set of linear hypotheses is of sufficient capacity -- large enough to contain a satisfactory soluti... Read More about Greedy feature construction.

Introducing the 'active search' method for iterative virtual screening (2015)
Journal Article
Garnett, R., Gärtner, T., Vogt, M., & Bajorath, J. (2015). Introducing the 'active search' method for iterative virtual screening. Journal of Computer-Aided Molecular Design, 29(4), 305-314. https://doi.org/10.1007/s10822-015-9832-9

A method is introduced for sequential similarity searching for active compounds. Given a set of known actives and a screening database, a strategy is devised to optimally rank test compounds by observing the outcome of each iteration before selecting... Read More about Introducing the 'active search' method for iterative virtual screening.

Interactive knowledge-based kernel PCA (2014)
Conference Proceeding
Oglic, D., Paurat, D., & Gartner, T. (2014). Interactive knowledge-based kernel PCA. In T. Calders, F. Esposito, E. Hüllermeier, & R. Meo (Eds.), Machine Learning and Knowledge Discovery in Databases.European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part II (501-516). https://doi.org/10.1007/978-3-662-44851-9_32

Data understanding is an iterative process in which domain experts combine their knowledge with the data at hand to explore and confirm hypotheses. One important set of tools for exploring hypotheses about data are visualizations. Often, however, tra... Read More about Interactive knowledge-based kernel PCA.

Predicting unexpected influxes of players in EVE online (2014)
Conference Proceeding
Garnett, R., Gartner, T., Ellersiek, T., Guðmondsson, E., & Óskarsson, P. (2014). Predicting unexpected influxes of players in EVE online. In IEEE Conference on Computational Intelligence and Games 2014: Proceedings. https://doi.org/10.1109/CIG.2014.6932878

EVE Online is a massively multiplayer online role-playing game (MMORPG) taking place in a large galaxy consisting of about 7 500 star systems. In comparison to many other online role-playing games, the users interact in the same instance of a persist... Read More about Predicting unexpected influxes of players in EVE online.

Beating human analysts in nowcasting corporate earnings by using publicly available stock price and correlation features (2014)
Conference Proceeding
Kamp, M., Boley, M., & Gartner, T. (2014). Beating human analysts in nowcasting corporate earnings by using publicly available stock price and correlation features. In Proceedings of the 2014 SIAM International Conference on Data Miningdoi:10.1137/1.9781611973440.74

Corporate earnings are a crucial indicator for investment and business valuation. Despite their importance and the fact that classic econometric approaches fail to match analyst forecasts by orders of magnitude, the automatic prediction of corporate... Read More about Beating human analysts in nowcasting corporate earnings by using publicly available stock price and correlation features.