@inproceedings{17407,
author = {Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke},
booktitle = {Discovery Science},
title = {{Extreme Algorithm Selection with Dyadic Feature Representation}},
year = {2020},
}
@inproceedings{19953,
abstract = {Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphism test. Here, we propose a novel graph convolution operator that is based on the 2-dimensional WL test. We formally show that the resulting 2-WL-GNN architecture is more discriminative than existing GNN approaches. This theoretical result is complemented by experimental studies using synthetic and real data. On multiple common graph classification benchmarks, we demonstrate that the proposed model is competitive with state-of-the-art graph kernels and GNNs.},
author = {Damke, Clemens and Melnikov, Vitaly and Hüllermeier, Eyke},
booktitle = {Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)},
editor = {Jialin Pan, Sinno and Sugiyama, Masashi},
keyword = {graph neural networks, Weisfeiler-Lehman test, cycle detection},
location = {Bangkok, Thailand},
pages = {49--64},
publisher = {PMLR},
title = {{A Novel Higher-order Weisfeiler-Lehman Graph Convolution}},
volume = {129},
year = {2020},
}
@inproceedings{17408,
author = {Hanselle, Jonas Manuel and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke},
booktitle = {KI 2020: Advances in Artificial Intelligence},
title = {{Hybrid Ranking and Regression for Algorithm Selection}},
year = {2020},
}
@inbook{18014,
author = {El Mesaoudi-Paul, Adil and Weiß, Dimitri and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin},
booktitle = {Learning and Intelligent Optimization. LION 2020.},
isbn = {9783030535513},
issn = {0302-9743},
pages = {216 -- 232},
publisher = {Springer},
title = {{Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach}},
doi = {10.1007/978-3-030-53552-0_22},
volume = {12096},
year = {2020},
}
@unpublished{17605,
abstract = {Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP) tasks. This problem is commonly tackled using machine learning methods, i.e., by training a POS tagger on a sufficiently large corpus of labeled data.
While the problem of POS tagging can essentially be considered as solved for modern languages, historical corpora turn out to be much more difficult, especially due to the lack of native speakers and sparsity of training data. Moreover, most texts have no sentences as we know them today, nor a common orthography.
These irregularities render the task of automated POS tagging more difficult and error-prone. Under these circumstances, instead of forcing the POS tagger to predict and commit to a single tag, it should be enabled to express its uncertainty. In this paper, we consider POS tagging within the framework of set-valued prediction, which allows the POS tagger to express its uncertainty via predicting a set of candidate POS tags instead of guessing a single one. The goal is to guarantee a high confidence that the correct POS tag is included while keeping the number of candidates small.
In our experimental study, we find that extending state-of-the-art POS taggers to set-valued prediction yields more precise and robust taggings, especially for unknown words, i.e., words not occurring in the training data.},
author = {Heid, Stefan Helmut and Wever, Marcel Dominik and Hüllermeier, Eyke},
booktitle = {Journal of Data Mining and Digital Humanities},
publisher = {episciences},
title = {{Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction}},
year = {2020},
}
@inproceedings{15629,
abstract = {In multi-label classification (MLC), each instance is associated with a set of class labels, in contrast to standard classification where an instance is assigned a single label. Binary relevance (BR) learning, which reduces a multi-label to a set of binary classification problems, one per label, is arguably the most straight-forward approach to MLC. In spite of its simplicity, BR proved to be competitive to more sophisticated MLC methods, and still achieves state-of-the-art performance for many loss functions. Somewhat surprisingly, the optimal choice of the base learner for tackling the binary classification problems has received very little attention so far. Taking advantage of the label independence assumption inherent to BR, we propose a label-wise base learner selection method optimizing label-wise macro averaged performance measures. In an extensive experimental evaluation, we find that or approach, called LiBRe, can significantly improve generalization performance.},
author = {Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke},
location = {Konstanz, Germany},
publisher = {Springer},
title = {{LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification}},
year = {2020},
}
@inproceedings{20306,
author = {Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke},
booktitle = {Workshop MetaLearn 2020 @ NeurIPS 2020},
location = {Online},
title = {{Towards Meta-Algorithm Selection}},
year = {2020},
}
@inproceedings{17424,
author = {Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke},
booktitle = {Proceedings of the ECMLPKDD 2020},
title = {{AutoML for Predictive Maintenance: One Tool to RUL them all}},
year = {2020},
}
@inbook{19521,
author = {Pfannschmidt, Karlson and Hüllermeier, Eyke},
booktitle = {Lecture Notes in Computer Science},
isbn = {9783030582845},
issn = {0302-9743},
title = {{Learning Choice Functions via Pareto-Embeddings}},
doi = {10.1007/978-3-030-58285-2_30},
year = {2020},
}
@article{16725,
author = {Richter, Cedric and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike},
journal = {Journal of Automated Software Engineering},
publisher = {Springer},
title = {{Algorithm Selection for Software Validation Based on Graph Kernels}},
year = {2020},
}
@unpublished{18017,
abstract = {We consider an extension of the contextual multi-armed bandit problem, in
which, instead of selecting a single alternative (arm), a learner is supposed
to make a preselection in the form of a subset of alternatives. More
specifically, in each iteration, the learner is presented a set of arms and a
context, both described in terms of feature vectors. The task of the learner is
to preselect $k$ of these arms, among which a final choice is made in a second
step. In our setup, we assume that each arm has a latent (context-dependent)
utility, and that feedback on a preselection is produced according to a
Plackett-Luce model. We propose the CPPL algorithm, which is inspired by the
well-known UCB algorithm, and evaluate this algorithm on synthetic and real
data. In particular, we consider an online algorithm selection scenario, which
served as a main motivation of our problem setting. Here, an instance (which
defines the context) from a certain problem class (such as SAT) can be solved
by different algorithms (the arms), but only $k$ of these algorithms can
actually be run.},
author = {El Mesaoudi-Paul, Adil and Bengs, Viktor and Hüllermeier, Eyke},
booktitle = {arXiv:2002.04275},
title = {{Online Preselection with Context Information under the Plackett-Luce Model}},
year = {2020},
}
@inproceedings{18276,
abstract = {Algorithm selection (AS) deals with the automatic selection of an algorithm
from a fixed set of candidate algorithms most suitable for a specific instance
of an algorithmic problem class, where "suitability" often refers to an
algorithm's runtime. Due to possibly extremely long runtimes of candidate
algorithms, training data for algorithm selection models is usually generated
under time constraints in the sense that not all algorithms are run to
completion on all instances. Thus, training data usually comprises censored
information, as the true runtime of algorithms timed out remains unknown.
However, many standard AS approaches are not able to handle such information in
a proper way. On the other side, survival analysis (SA) naturally supports
censored data and offers appropriate ways to use such data for learning
distributional models of algorithm runtime, as we demonstrate in this work. We
leverage such models as a basis of a sophisticated decision-theoretic approach
to algorithm selection, which we dub Run2Survive. Moreover, taking advantage of
a framework of this kind, we advocate a risk-averse approach to algorithm
selection, in which the avoidance of a timeout is given high priority. In an
extensive experimental study with the standard benchmark ASlib, our approach is
shown to be highly competitive and in many cases even superior to
state-of-the-art AS approaches.},
author = {Tornede, Alexander and Wever, Marcel Dominik and Werner, Stefan and Mohr, Felix and Hüllermeier, Eyke},
booktitle = {ACML 2020},
location = {Bangkok, Thailand},
title = {{Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis}},
year = {2020},
}
@article{14028,
author = {Bengs, Viktor and Holzmann, Hajo},
issn = {1935-7524},
journal = {Electronic Journal of Statistics},
pages = {1523--1579},
title = {{Adaptive confidence sets for kink estimation}},
doi = {10.1214/19-ejs1555},
year = {2019},
}
@article{15002,
abstract = {Many problem settings in machine learning are concerned with the simultaneous prediction of multiple target variables of diverse type. Amongst others, such problem settings arise in multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. These subfields of machine learning are typically studied in isolation, without highlighting or exploring important relationships. In this paper, we present a unifying view on what we call multi-target prediction (MTP) problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research.},
author = {Waegeman, Willem and Dembczynski, Krzysztof and Hüllermeier, Eyke},
issn = {1573-756X},
journal = {Data Mining and Knowledge Discovery},
number = {2},
pages = {293--324},
title = {{Multi-target prediction: a unifying view on problems and methods}},
doi = {10.1007/s10618-018-0595-5},
volume = {33},
year = {2019},
}
@inproceedings{15014,
author = {Hüllermeier, Eyke and Couso, Ines and Diestercke, Sebastian},
booktitle = {Proceedings SUM 2019, International Conference on Scalable Uncertainty Management},
title = {{Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants}},
year = {2019},
}
@inproceedings{15007,
author = {Melnikov, Vitaly and Hüllermeier, Eyke},
booktitle = {Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)},
title = {{Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA}},
doi = {10.1016/j.jmva.2019.02.017},
year = {2019},
}
@article{17565,
author = {Merten, Marie-Luis and Seemann, Nina and Wever, Marcel Dominik},
journal = {Niederdeutsches Jahrbuch},
number = {142},
pages = {124--146},
title = {{Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff}},
year = {2019},
}
@unpublished{18018,
abstract = {A common statistical task lies in showing asymptotic normality of certain
statistics. In many of these situations, classical textbook results on weak
convergence theory suffice for the problem at hand. However, there are quite
some scenarios where stronger results are needed in order to establish an
asymptotic normal approximation uniformly over a family of probability
measures. In this note we collect some results in this direction. We restrict
ourselves to weak convergence in $\mathbb R^d$ with continuous limit measures.},
author = {Bengs, Viktor and Holzmann, Hajo},
booktitle = {arXiv:1903.09864},
title = {{Uniform approximation in classical weak convergence theory}},
year = {2019},
}
@unpublished{19523,
abstract = {We study the problem of learning choice functions, which play an important
role in various domains of application, most notably in the field of economics.
Formally, a choice function is a mapping from sets to sets: Given a set of
choice alternatives as input, a choice function identifies a subset of most
preferred elements. Learning choice functions from suitable training data comes
with a number of challenges. For example, the sets provided as input and the
subsets produced as output can be of any size. Moreover, since the order in
which alternatives are presented is irrelevant, a choice function should be
symmetric. Perhaps most importantly, choice functions are naturally
context-dependent, in the sense that the preference in favor of an alternative
may depend on what other options are available. We formalize the problem of
learning choice functions and present two general approaches based on two
representations of context-dependent utility functions. Both approaches are
instantiated by means of appropriate neural network architectures, and their
performance is demonstrated on suitable benchmark tasks.},
author = {Pfannschmidt, Karlson and Gupta, Pritha and Hüllermeier, Eyke},
booktitle = {arXiv:1901.10860},
title = {{Learning Choice Functions: Concepts and Architectures}},
year = {2019},
}
@inproceedings{15003,
author = {Mortier, Thomas and Wydmuch, Marek and Dembczynski, Krzysztof and Hüllermeier, Eyke and Waegeman, Willem},
booktitle = {Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8, 2019},
title = {{Set-Valued Prediction in Multi-Class Classification}},
year = {2019},
}