@unpublished{30868,
  abstract     = {{Algorithm configuration (AC) is concerned with the automated search of the
most suitable parameter configuration of a parametrized algorithm. There is
currently a wide variety of AC problem variants and methods proposed in the
literature. Existing reviews do not take into account all derivatives of the AC
problem, nor do they offer a complete classification scheme. To this end, we
introduce taxonomies to describe the AC problem and features of configuration
methods, respectively. We review existing AC literature within the lens of our
taxonomies, outline relevant design choices of configuration approaches,
contrast methods and problem variants against each other, and describe the
state of AC in industry. Finally, our review provides researchers and
practitioners with a look at future research directions in the field of AC.}},
  author       = {{Schede, Elias and Brandt, Jasmin and Tornede, Alexander and Wever, Marcel Dominik and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}},
  booktitle    = {{arXiv:2202.01651}},
  title        = {{{A Survey of Methods for Automated Algorithm Configuration}}},
  year         = {{2022}},
}

@unpublished{30867,
  abstract     = {{In online algorithm selection (OAS), instances of an algorithmic problem
class are presented to an agent one after another, and the agent has to quickly
select a presumably best algorithm from a fixed set of candidate algorithms.
For decision problems such as satisfiability (SAT), quality typically refers to
the algorithm's runtime. As the latter is known to exhibit a heavy-tail
distribution, an algorithm is normally stopped when exceeding a predefined
upper time limit. As a consequence, machine learning methods used to optimize
an algorithm selection strategy in a data-driven manner need to deal with
right-censored samples, a problem that has received little attention in the
literature so far. In this work, we revisit multi-armed bandit algorithms for
OAS and discuss their capability of dealing with the problem. Moreover, we
adapt them towards runtime-oriented losses, allowing for partially censored
data while keeping a space- and time-complexity independent of the time
horizon. In an extensive experimental evaluation on an adapted version of the
ASlib benchmark, we demonstrate that theoretically well-founded methods based
on Thompson sampling perform specifically strong and improve in comparison to
existing methods.}},
  author       = {{Tornede, Alexander and Bengs, Viktor and Hüllermeier, Eyke}},
  booktitle    = {{Proceedings of the 36th AAAI Conference on Artificial Intelligence}},
  publisher    = {{AAAI}},
  title        = {{{Machine Learning for Online Algorithm Selection under Censored Feedback}}},
  year         = {{2022}},
}

@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{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}},
}

@article{14027,
  author       = {{Bengs, Viktor and Eulert, Matthias and Holzmann, Hajo}},
  issn         = {{0047-259X}},
  journal      = {{Journal of Multivariate Analysis}},
  pages        = {{291--312}},
  title        = {{{Asymptotic confidence sets for the jump curve in bivariate regression problems}}},
  doi          = {{10.1016/j.jmva.2019.02.017}},
  year         = {{2019}},
}

@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}},
}

@phdthesis{14031,
  author       = {{Bengs, Viktor}},
  title        = {{{Confidence sets for change-point problems in nonparametric regression }}},
  doi          = {{https://doi.org/10.17192/z2018.0511}},
  year         = {{2018}},
}