A Survey of Methods for Automated Algorithm Configuration
E. Schede, J. Brandt, A. Tornede, M.D. Wever, V. Bengs, E. Hüllermeier, K. Tierney, ArXiv:2202.01651 (2022).
LibreCat: /record/30868
arXiv: 2202.01651

Machine Learning for Online Algorithm Selection under Censored Feedback
A. Tornede, V. Bengs, E. Hüllermeier, Proceedings of the 36th AAAI Conference on Artificial Intelligence (2022).
LibreCat: /record/30867
arXiv: 2109.06234

Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach
A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, K. Tierney, in: Learning and Intelligent Optimization. LION 2020., Springer, Cham, 2020, pp. 216–232.
LibreCat: /record/18014
DOI: 10.1007/978-3-030-53552-0_22

Online Preselection with Context Information under the Plackett-Luce  Model
A. El Mesaoudi-Paul, V. Bengs, E. Hüllermeier, ArXiv:2002.04275 (n.d.).
LibreCat: /record/18017

Asymptotic confidence sets for the jump curve in bivariate regression problems
V. Bengs, M. Eulert, H. Holzmann, Journal of Multivariate Analysis (2019) 291–312.
LibreCat: /record/14027
DOI: 10.1016/j.jmva.2019.02.017

Adaptive confidence sets for kink estimation
V. Bengs, H. Holzmann, Electronic Journal of Statistics (2019) 1523–1579.
LibreCat: /record/14028
DOI: 10.1214/19-ejs1555

Confidence sets for change-point problems in nonparametric regression 
V. Bengs, Confidence Sets for Change-Point Problems in Nonparametric Regression , 2018.
LibreCat: /record/14031
DOI: https://doi.org/10.17192/z2018.0511