TY - JOUR
AU - Richter, Cedric
AU - Hüllermeier, Eyke
AU - Jakobs, Marie-Christine
AU - Wehrheim, Heike
ID - 16725
JF - Journal of Automated Software Engineering
TI - Algorithm Selection for Software Validation Based on Graph Kernels
ER -
TY - CONF
AB - 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.
AU - Tornede, Alexander
AU - Wever, Marcel Dominik
AU - Werner, Stefan
AU - Mohr, Felix
AU - Hüllermeier, Eyke
ID - 18276
T2 - ACML 2020
TI - Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis
ER -
TY - GEN
AB - 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.
AU - El Mesaoudi-Paul, Adil
AU - Bengs, Viktor
AU - Hüllermeier, Eyke
ID - 18017
T2 - arXiv:2002.04275
TI - Online Preselection with Context Information under the Plackett-Luce Model
ER -
TY - CONF
AU - Bengs, Viktor
AU - Hüllermeier, Eyke
ID - 21534
T2 - International Conference on Machine Learning
TI - Preselection Bandits
ER -
TY - JOUR
AU - Bengs, Viktor
AU - Holzmann, Hajo
ID - 14028
JF - Electronic Journal of Statistics
SN - 1935-7524
TI - Adaptive confidence sets for kink estimation
ER -
TY - JOUR
AB - 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.
AU - Waegeman, Willem
AU - Dembczynski, Krzysztof
AU - Hüllermeier, Eyke
ID - 15002
IS - 2
JF - Data Mining and Knowledge Discovery
SN - 1573-756X
TI - Multi-target prediction: a unifying view on problems and methods
VL - 33
ER -
TY - CONF
AU - Hüllermeier, Eyke
AU - Couso, Ines
AU - Diestercke, Sebastian
ID - 15014
T2 - Proceedings SUM 2019, International Conference on Scalable Uncertainty Management
TI - Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants
ER -
TY - CONF
AU - Melnikov, Vitaly
AU - Hüllermeier, Eyke
ID - 15007
T2 - Proceedings ACML, Asian Conference on Machine Learning (Proceedings of Machine Learning Research, 101)
TI - Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA
ER -
TY - JOUR
AU - Merten, Marie-Luis
AU - Seemann, Nina
AU - Wever, Marcel Dominik
ID - 17565
IS - 142
JF - Niederdeutsches Jahrbuch
TI - Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff
ER -
TY - GEN
AB - 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.
AU - Bengs, Viktor
AU - Holzmann, Hajo
ID - 18018
T2 - arXiv:1903.09864
TI - Uniform approximation in classical weak convergence theory
ER -