Reliable Machine Learning

Current Machine Learning model evaluation methods, e.g., the use of test sets, will only detect whether a model’s predictions match the data. They cannot exclude the possibility that both predictions and data are biased. More targeted efforts to reduce or eliminate training data biases require either manual adjustments of the models, domain knowledge, or assume that data or model issues can easily be identified. This is seldom the case. Model auditing generally relies on manual analysis of the models, on existing data, and knowledgeable auditors. Automatically identifying deficiencies in both data and training, and how they impact application of models, is still an unsolved question. Bias mitigation approaches require either the ground-truth distribution or concrete information on the bias — or unbiased / differently biased data from other sources. We aim to develop a model-agnostic framework that will not be limited by any of these requirements.

2024

Kim, Jonathan; Urschler, Martin; Riddle, Pat; Wicker, Jörg

Attacking the Loop: Adversarial Attacks on Graph-based Loop Closure Detection Proceedings Article Forthcoming

In: Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Forthcoming, (accepted).

Abstract | Links | BibTeX

2023

Lyu, Jiachen; Dost, Katharina; Koh, Yun Sing; Wicker, Jörg

Regional Bias in Monolingual English Language Models Unpublished Forthcoming

Forthcoming.

Abstract | Links | BibTeX

Dost, Katharina; Tam, Jason; Lorsbach, Tim; Schmidt, Sebastian; Wicker, Jörg

Defining Applicability Domain in Biodegradation Pathway Prediction Unpublished Forthcoming

Forthcoming.

Abstract | Links | BibTeX

Hafner, Jasmin; Lorsbach, Tim; Schmidt, Sebastian; Brydon, Liam; Dost, Katharina; Zhang, Kunyang; Fenner, Kathrin; Wicker, Jörg

Advancements in Biotransformation Pathway Prediction: Enhancements, Datasets, and Novel Functionalities in enviPath Unpublished Forthcoming

Forthcoming.

Abstract | Links | BibTeX

Chang, Xinglong; Dost, Katharina; Dobbie, Gillian; Wicker, Jörg

Poison is Not Traceless: Fully-Agnostic Detection of Poisoning Attacks Unpublished Forthcoming

Forthcoming.

Abstract | Links | BibTeX

Chang, Xinglong; Dobbie, Gillian; Wicker, Jörg

Fast Adversarial Label-Flipping Attack on Tabular Data Unpublished Forthcoming

Forthcoming.

Abstract | Links | BibTeX

Pullar-Strecker, Zac; Chang, Xinglong; Brydon, Liam; Ziogas, Ioannis; Dost, Katharina; Wicker, Jörg

Memento: Facilitating Effortless, Efficient, and Reliable ML Experiments Proceedings Article

In: Morales, Gianmarco De Francisci; Perlich, Claudia; Ruchansky, Natali; Kourtellis, Nicolas; Baralis, Elena; Bonchi, Francesco (Ed.): Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track, pp. 310-314, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-43430-3.

Abstract | Links | BibTeX

Chang, Luke; Dost, Katharina; Zhai, Kaiqi; Demontis, Ambra; Roli, Fabio; Dobbie, Gillian; Wicker, Jörg

BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability Proceedings Article

In: Kashima, Hisashi; Ide, Tsuyoshi; Peng, Wen-Chih (Ed.): The 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 3-14, Springer Nature Switzerland, Cham, 2023, ISSN: 978-3-031-33374-3.

Abstract | Links | BibTeX

Chen, Zeyu; Dost, Katharina; Zhu, Xuan; Chang, Xinglong; Dobbie, Gillian; Wicker, Jörg

Targeted Attacks on Time Series Forecasting Proceedings Article

In: Kashima, Hisashi; Ide, Tsuyoshi; Peng, Wen-Chih (Ed.): The 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), pp. 314-327, Springer Nature Switzerland, Cham, 2023, ISSN: 978-3-031-33383-5.

Abstract | Links | BibTeX

Dost, Katharina; Pullar-Strecker, Zac; Brydon, Liam; Zhang, Kunyang; Hafner, Jasmin; Riddle, Pat; Wicker, Jörg

Combatting over-specialization bias in growing chemical databases Journal Article

In: Journal of Cheminformatics, vol. 15, iss. 1, pp. 53, 2023, ISSN: 1758-2946.

Abstract | Links | BibTeX

2022

Dost, Katharina; Duncanson, Hamish; Ziogas, Ioannis; Riddle, Pat; Wicker, Jörg

Divide and Imitate: Multi-Cluster Identification and Mitigation of Selection Bias Proceedings Article

In: 26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2022), pp. 149-160, Springer-Verlag, Berlin, Heidelberg, 2022, ISBN: 978-3-031-05935-3.

Abstract | Links | BibTeX

2020

Chester, Andrew; Koh, Yun Sing; Wicker, Jörg; Sun, Quan; Lee, Junjae

Balancing Utility and Fairness against Privacy in Medical Data Proceedings Article

In: IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1226-1233, IEEE, 2020.

Abstract | Links | BibTeX

Dost, Katharina; Taskova, Katerina; Riddle, Pat; Wicker, Jörg

Your Best Guess When You Know Nothing: Identification and Mitigation of Selection Bias Proceedings Article

In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 996-1001, IEEE, 2020, ISSN: 2374-8486.

Abstract | Links | BibTeX

2017

Wicker, Jörg; Kramer, Stefan

The Best Privacy Defense is a Good Privacy Offense: Obfuscating a Search Engine User’s Profile Journal Article

In: Data Mining and Knowledge Discovery, vol. 31, no. 5, pp. 1419-1443, 2017, ISSN: 1573-756X.

Abstract | Links | BibTeX