34899
2013
2013
eng
41
64
24
1
92
article
Springer
Dordrecht
1
--
--
--
Active evaluation of ranking functions based on graded relevance
Evaluating the quality of ranking functions is a core task in web search and other information retrieval domains. Because query distributions and item relevance change over time, ranking models often cannot be evaluated accurately on held-out training data. Instead, considerable effort is spent on manually labeling the relevance of query results for test queries in order to track ranking performance. We address the problem of estimating ranking performance as accurately as possible on a fixed labeling budget. Estimates are based on a set of most informative test queries selected by an active sampling distribution. Query labeling costs depend on the number of result items as well as item-specific attributes such as document length. We derive cost-optimal sampling distributions for the commonly used performance measures Discounted Cumulative Gain and Expected Reciprocal Rank. Experiments on web search engine data illustrate significant reductions in labeling costs.
Machine learning
10.1007/s10994-013-5372-5
0885-6125 (print)
wos:2011-2013
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)
SEP 24-28, 2012
WOS:000321273100003
Bristol, ENGLAND
Sawade, C (reprint author), Univ Potsdam, Dept Comp Sci, August Bebel Str 89, D-14482 Potsdam, Germany., sawade@cs.uni-potsdam.de; steffen.bickel@nokia.com; timo@virginia.edu; scheffer@cs.uni-potsdam.de; landwehr@cs.uni-potsdam.de
Christoph Sawade
Steffen Bickel
Timo von Oertzen
Tobias Scheffer
Niels Landwehr
eng
uncontrolled
Information retrieval
eng
uncontrolled
Ranking
eng
uncontrolled
Active evaluation
Institut für Informatik und Computational Science
Referiert
Institut für Informatik
37029
2011
2011
eng
239
272
34
2
82
article
Springer
Dordrecht
1
--
--
--
Stochastic relational processes efficient inference and applications
One of the goals of artificial intelligence is to develop agents that learn and act in complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and non-deterministic transition behavior. While standard probabilistic sequence models provide efficient inference and learning techniques for sequential data, they typically cannot fully capture the relational complexity. On the other hand, statistical relational learning techniques are often too inefficient to cope with complex sequential data. In this paper, we introduce a simple model that occupies an intermediate position in this expressiveness/efficiency trade-off. It is based on CP-logic (Causal Probabilistic Logic), an expressive probabilistic logic for modeling causality. However, by specializing CP-logic to represent a probability distribution over sequences of relational state descriptions and employing a Markov assumption, inference and learning become more tractable and effective. Specifically, we show how to solve part of the inference and learning problems directly at the first-order level, while transforming the remaining part into the problem of computing all satisfying assignments for a Boolean formula in a binary decision diagram. We experimentally validate that the resulting technique is able to handle probabilistic relational domains with a substantial number of objects and relations.
Machine learning
10.1007/s10994-010-5213-8
0885-6125 (print)
wos:2011-2013
International Conference on Machine Learning/ Workshop on Machine Learning and Graphs
2008
WOS:000288121900007
Helsinki, FINLAND
Thon, I (reprint author), Katholieke Univ Leuven, Dept Comp Sci, Celestijnenlaan 200A, B-3001 Heverlee, Belgium., ingo.thon@cs.kuleuven.be; landwehr@cs.uni-potsdam.de; luc.deraedt@cs.kuleuven.be
Ingo Thon
Niels Landwehr
Luc De Raedt
eng
uncontrolled
Statistical relational learning
eng
uncontrolled
Stochastic relational process
eng
uncontrolled
Markov processes
eng
uncontrolled
Time series
eng
uncontrolled
CP-Logic
Institut für Informatik und Computational Science
Referiert
Institut für Informatik
37185
2011
2011
eng
151
177
27
2
113
article
IOS Press
Amsterdam
1
--
--
--
Relational feature mining with hierarchical multitask kFOIL
We introduce hierarchical kFOIL as a simple extension of the multitask kFOIL learning algorithm. The algorithm first learns a core logic representation common to all tasks, and then refines it by specialization on a per-task basis. The approach can be easily generalized to a deeper hierarchy of tasks. A task clustering algorithm is also proposed in order to automatically generate the task hierarchy. The approach is validated on problems of drug-resistance mutation prediction and protein structural classification. Experimental results show the advantage of the hierarchical version over both single and multi task alternatives and its potential usefulness in providing explanatory features for the domain. Task clustering allows to further improve performance when a deeper hierarchy is considered.
Fundamenta informaticae
10.3233/FI-2011-604
0169-2968 (print)
wos:2011-2013
WOS:000299978700004
Cilia, E (reprint author), Univ Libre Bruxelles, Dept Informat, Brussels, Belgium., ecilia@ulb.ac.be; landwehr@cs.uni-potsdam.de; passerini@disi.unitn.it
NIH [P41 RR-01081]
Elisa Cilia
Niels Landwehr
Andrea Passerini
Institut für Informatik und Computational Science
Referiert
Institut für Informatik