A

Arenz, Oleg (2022):
Monte Carlo Chess. (Publisher's Version)
Darmstadt, Technische Universität Darmstadt, DOI: 10.26083/tuprints-00022930,
[Bachelor Thesis]

C

Czech, Johannes ; Willig, Moritz ; Beyer, Alena ; Kersting, Kristian ; Fürnkranz, Johannes (2020):
Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data.
In: Frontiers in Artificial Intelligence, 3, Frontiers, ISSN 2624-8212,
DOI: 10.25534/tuprints-00013370,
[Article]

D

Dang, Hien (2021):
Adaptive Personalization in Driver Assistance Systems. (Publisher's Version)
Darmstadt, Technische Universität Darmstadt,
DOI: 10.26083/tuprints-00017507,
[Ph.D. Thesis]

G

Güttinger, Dennis (2013):
A New Metaheuristic Approach for Stabilizing the Solution Quality of Simulated Annealing and Applications.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

J

Janssen, Frederik (2012):
Heuristic Rule Learning.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

Jäger, Jonas (2019):
Self-Imitation Regularization: Regularizing Neural Networks by Leveraging Their Dark Knowledge.
Darmstadt, Technische Universität, [Bachelor Thesis]

K

Kauschke, Sebastian (2019):
Patching - A Framework for Adapting Immutable Classifiers to Evolving Domains.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

Kulessa, Moritz ; Mencía, Eneldo Loza ; Fürnkranz, Johannes (2021):
A Unifying Framework and Comparative Evaluation of Statistical and Machine Learning Approaches to Non-Specific Syndromic Surveillance. (Publisher's Version)
In: Computers, 10 (3), MDPI, e-ISSN 2073-431X,
DOI: 10.26083/tuprints-00019322,
[Article]

L

Loza Mencía, Eneldo (2013):
Efficient Pairwise Multilabel Classification.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

N

Nam, Jinseok (2019):
Learning Label Structures with Neural Networks for Multi-label Classification.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

P

Park, Sang-Hyeun (2012):
Efficient Decomposition-Based Multiclass and Multilabel Classification.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

R

Rapp, Michael (2022):
Multi-label Rule Learning. (Publisher's Version)
Darmstadt, Technische Universität Darmstadt,
DOI: 10.26083/tuprints-00022099,
[Ph.D. Thesis]

Rapp, Michael ; Kulessa, Moritz ; Loza Mencía, Eneldo ; Fürnkranz, Johannes (2022):
Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance. (Publisher's Version)
In: Frontiers in Big Data, 4, Frontiers Media, e-ISSN 2624-909X,
DOI: 10.26083/tuprints-00020807,
[Article]

S

Schulz, Axel (2014):
Mining User-Generated Content for Incidents.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

Sulzmann, Jan-Nikolas (2019):
Rule Learning: From Local Patterns to Global Models.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

T

Tavakol, Maryam (2019):
Contextual Models for Sequential Recommendation.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

Thoma, Nils (2018):
Learning to Detect Personal Information in German Text Documents.
Darmstadt, Technische Universität, [Bachelor Thesis]

W

Wirth, Christian (2017):
Efficient Preference-based Reinforcement Learning.
Darmstadt, Technische Universität,
[Ph.D. Thesis]

Z

Zopf, Markus (2019):
Towards Context-free Information Importance Estimation.
Darmstadt, Technische Universität,
[Ph.D. Thesis]