Automatic feature selection for anomaly detection. - Marius Kloft; Ulf Brefeld; Patrick Düssel; Christian Gehl; Pavel Laskov (AISec, 2008)

Active learning for network intrusion detection. - Nico Görnitz; Marius Kloft; Konrad Rieck; Ulf Brefeld (AISec, 2009)

A framework for quantitative security analysis of machine learning. - Pavel Laskov; Marius Kloft (AISec, 2009)

Efficient and Accurate Lp-Norm Multiple Kernel Learning. - Marius Kloft; Ulf Brefeld; Sören Sonnenburg; Pavel Laskov; Klaus-Robert Müller; Alexander Zien (NIPS, 2009)

Active and Semi-supervised Data Domain Description. - Nico Görnitz; Marius Kloft; Ulf Brefeld (ECML/PKDD (1), 2009)

Feature Selection for Density Level-Sets. - Marius Kloft; Shinichi Nakajima; Ulf Brefeld (ECML/PKDD (1), 2009)

A Unifying View of Multiple Kernel Learning. - Marius Kloft; Ulrich Rückert 002; Peter L. Bartlett (ECML/PKDD (2), 2010)

Non-Sparse Regularization and Efficient Training with Multiple Kernels - Marius Kloft; Ulf Brefeld; Sören Sonnenburg; Alexander Zien (CoRR, 2010)

Online Anomaly Detection under Adversarial Impact. - Marius Kloft; Pavel Laskov (AISTATS, 2010)

The Joint Submission of the TU Berlin and Fraunhofer FIRST (TUBFI) to the ImageCLEF2011 Photo Annotation Task. - Alexander Binder; Wojciech Samek; Marius Kloft; Christina Müller; Klaus-Robert Müller; Motoaki Kawanabe (CLEF (Notebook Papers/Labs/Workshop), 2011)

Maschinelles Lernen mit multiplen Kernen. - Marius Kloft (Ausgezeichnete Informatikdissertationen, 2011)

A new scatter-based multi-class support vector machine. - Robert Jenssen; Marius Kloft; Sören Sonnenburg; Alexander Zien; Klaus-Robert Müller (MLSP, 2011)

The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning. - Marius Kloft; Gilles Blanchard (NIPS, 2011)

Transfer Learning with Adaptive Regularizers. - Ulrich Rückert 002; Marius Kloft (ECML/PKDD (3), 2011)

Insights from Classifying Visual Concepts with Multiple Kernel Learning - Alexander Binder; Shinichi Nakajima; Marius Kloft; Christina Müller; Wojciech Samek; Ulf Brefeld; Klaus-Robert Müller; Motoaki Kawanabe (CoRR, 2011)

lp-Norm Multiple Kernel Learning. - Marius Kloft; Ulf Brefeld; Sören Sonnenburg; Alexander Zien (J. Mach. Learn. Res., 2011)

lp-Norm Multiple Kernel Learning. - Marius Kloft (2011)

Early detection of malicious behavior in JavaScript code. - Kristof Schütt; Marius Kloft; Alexander Bikadorov; Konrad Rieck (AISec, 2012)

Efficient Training of Graph-Regularized Multitask SVMs. - Christian Widmer; Marius Kloft; Nico Görnitz; Gunnar Rätsch (ECML/PKDD (1), 2012)

On the convergence rate of lp-norm multiple kernel learning. - Marius Kloft; Gilles Blanchard (J. Mach. Learn. Res., 2012)

Security analysis of online centroid anomaly detection. - Marius Kloft; Pavel Laskov (J. Mach. Learn. Res., 2012)

Multi-task Learning for Computational Biology: Overview and Outlook. - Christian Widmer; Marius Kloft; Gunnar Rätsch (Empirical Inference, 2013)

Learning Kernels Using Local Rademacher Complexity. - Corinna Cortes; Marius Kloft; Mehryar Mohri (NIPS, 2013)

Kernel-Based Machine Learning with Multiple Sources of Information. - Marius Kloft (it Inf. Technol., 2013)

Toward Supervised Anomaly Detection. - Nico Görnitz; Marius Kloft; Konrad Rieck; Ulf Brefeld (J. Artif. Intell. Res., 2013)

Decoding Brain States during Auditory Perception by Supervising Unsupervised Learning. - Anne Porbadnigk; Nico Görnitz; Marius Kloft; Klaus-Robert Müller (J. Comput. Sci. Eng., 2013)

Learning and Evaluation in Presence of Non-i.i.d. Label Noise. - Nico Görnitz; Anne Porbadnigk; Alexander Binder; Claudia Sannelli; Mikio L. Braun; Klaus-Robert Müller; Marius Kloft (AISTATS, 2014)

When brain and behavior disagree: Tackling systematic label noise in EEG data with machine learning. - Anne K. Porbadnigk; Nico Görnitz; Claudia Sannelli; Alexander Binder; Mikio L. Braun; Marius Kloft; Klaus-Robert Müller (BCI, 2014)

Localized Complexities for Transductive Learning. - Ilya O. Tolstikhin; Gilles Blanchard; Marius Kloft (COLT, 2014)

Regularization-Based Multitask Learning With Applications to Genome Biology and Biological Imaging. - Christian Widmer; Marius Kloft; Xinghua Lou; Gunnar Rätsch (Künstliche Intell., 2014)

Efficient Algorithms for Exact Inference in Sequence Labeling SVMs. - Alexander Bauer 001; Nico Görnitz; Franziska Biegler; Klaus-Robert Müller; Marius Kloft (IEEE Trans. Neural Networks Learn. Syst., 2014)

Hidden Markov Anomaly Detection. - Nico Görnitz; Mikio L. Braun; Marius Kloft (ICML, 2015)

Theory and Algorithms for the Localized Setting of Learning Kernels. - Yunwen Lei; Alexander Binder; Ürün Dogan; Marius Kloft (FE@NIPS, 2015)

Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms. - Yunwen Lei; Ürün Dogan; Alexander Binder; Marius Kloft (NIPS, 2015)

Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms. - Marina M.-C. Vidovic; Nico Görnitz; Klaus-Robert Müller; Gunnar Rätsch; Marius Kloft (ECML/PKDD (2), 2015)

Sparse Estimation in a Correlated Probit Model. - Stephan Mandt; Florian Wenzel; Shinichi Nakajima; John P. Cunningham; Christoph Lippert; Marius Kloft (CoRR, 2015)

Framework for Multi-task Multiple Kernel Learning and Applications in Genome Analysis. - Christian Widmer; Marius Kloft; Vipin T. Sreedharan; Gunnar Rätsch (CoRR, 2015)

Machine Learning with Interdependent and Non-identically Distributed Data (Dagstuhl Seminar 15152). - Trevor Darrell; Marius Kloft; Massimiliano Pontil; Gunnar Rätsch; Erik Rodner (Dagstuhl Reports, 2015)

Probabilistic clustering of time-evolving distance data. - Julia E. Vogt; Marius Kloft; Stefan Stark; Sudhir Raman; Sandhya Prabhakaran; Volker Roth 001; Gunnar Rätsch (Mach. Learn., 2015)

Extracting latent brain states - Towards true labels in cognitive neuroscience experiments. - Anne K. Porbadnigk; Nico Görnitz; Claudia Sannelli; Alexander Binder; Mikio L. Braun; Marius Kloft; Klaus-Robert Müller (NeuroImage, 2015)

Localized Multiple Kernel Learning - A Convex Approach. - Yunwen Lei; Alexander Binder; Ürün Dogan; Marius Kloft (ACML, 2016)

Tracked Without a Trace: Linking Sessions of Users by Unsupervised Learning of Patterns in Their DNS Traffic. - Matthias Kirchler; Dominik Herrmann; Jens Lindemann 001; Marius Kloft (AISec@CCS, 2016)

Huber-Norm Regularization for Linear Prediction Models. - Oleksandr Zadorozhnyi; Gunthard Benecke; Stephan Mandt; Tobias Scheffer; Marius Kloft (ECML/PKDD (1), 2016)

Behavior-based tracking of Internet users with semi-supervised learning. - Dominik Herrmann; Matthias Kirchler; Jens Lindemann 001; Marius Kloft (PST, 2016)

Separating Sparse Signals from Correlated Noise in Binary Classification. - Stephan Mandt; Florian Wenzel; Shinichi Nakajima; Christoph Lippert; Marius Kloft (CFA@UAI, 2016)

Distributed Optimization of Multi-Class SVMs. - Maximilian Alber; Julian Zimmert; Ürün Dogan; Marius Kloft (CoRR, 2016)

Feature Importance Measure for Non-linear Learning Algorithms. - Marina M.-C. Vidovic; Nico Görnitz; Klaus-Robert Müller; Marius Kloft (CoRR, 2016)

Bayesian Nonlinear Support Vector Machines for Big Data. - Florian Wenzel; Théo Galy-Fajou; Matthäus Deutsch; Marius Kloft (ECML/PKDD (1), 2017)

Generalization Error Bounds for Extreme Multi-class Classification. - Yunwen Lei; Ürün Dogan; Ding-Xuan Zhou; Marius Kloft (CoRR, 2017)

Sparse probit linear mixed model. - Stephan Mandt; Florian Wenzel; Shinichi Nakajima; John P. Cunningham; Christoph Lippert; Marius Kloft (Mach. Learn., 2017)

Scalable Generalized Dynamic Topic Models. - Patrick Jähnichen; Florian Wenzel; Marius Kloft; Stephan Mandt (AISTATS, 2018)

Deep One-Class Classification. - Lukas Ruff; Nico Görnitz; Lucas Deecke; Shoaib Ahmed Siddiqui; Robert A. Vandermeulen; Alexander Binder; Emmanuel Müller; Marius Kloft (ICML, 2018)

Image Anomaly Detection with Generative Adversarial Networks. - Lucas Deecke; Robert A. Vandermeulen; Lukas Ruff; Stephan Mandt; Marius Kloft (ECML/PKDD (1), 2018)

Distributed Optimization of All-in-one SVMs for Extreme Classfication. - Marius Kloft (WWW (Companion Volume), 2018)

Extreme Classification (Dagstuhl Seminar 18291). - Samy Bengio; Krzysztof Dembczynski; Thorsten Joachims; Marius Kloft; Manik Varma (Dagstuhl Reports, 2018)

Mixed kernel based extreme learning machine for electric load forecasting. - Yanhua Chen; Marius Kloft; Yi Yang 017; Caihong Li; Lian Li 003 (Neurocomputing, 2018)

Local Rademacher Complexity-based Learning Guarantees for Multi-Task Learning. - Niloofar Yousefi 001; Yunwen Lei; Marius Kloft; Mansooreh Mollaghasemi; Georgios C. Anagnostopoulos (J. Mach. Learn. Res., 2018)

Support Vector Data Descriptions and k-Means Clustering: One Class? - Nico Görnitz; Luiz Alberto Lima; Klaus-Robert Müller; Marius Kloft; Shinichi Nakajima (IEEE Trans. Neural Networks Learn. Syst., 2018)

Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation. - Florian Wenzel; Théo Galy-Fajou; Christian Donner; Marius Kloft; Manfred Opper (AAAI, 2019)

Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text. - Lukas Ruff; Yury Zemlyanskiy; Robert A. Vandermeulen; Thomas Schnake; Marius Kloft (ACL (1), 2019)

Learning a Multimodal Prior Distribution for Generative Adversarial Nets. - Thomas Goerttler; Marius Kloft (LWDA, 2019)

Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network. - Siqi Wang 001; Yijie Zeng; Xinwang Liu 002; En Zhu; Jianping Yin; Chuanfu Xu; Marius Kloft (NeurIPS, 2019)

Improved Generalisation Bounds for Deep Learning Through L∞ Covering Numbers. - Antoine Ledent; Yunwen Lei; Marius Kloft (CoRR, 2019)

Analyzing the Variance of Policy Gradient Estimators for the Linear-Quadratic Regulator. - James A. Preiss; Sébastien M. R. Arnold; Chen-Yu Wei; Marius Kloft (CoRR, 2019)

Data-Dependent Generalization Bounds for Multi-Class Classification. - Yunwen Lei; Ürün Dogan; Ding-Xuan Zhou; Marius Kloft (IEEE Trans. Inf. Theory, 2019)

Two-sample Testing Using Deep Learning. - Matthias Kirchler; Shahryar Khorasani; Marius Kloft; Christoph Lippert (AISTATS, 2020)

Deep Semi-Supervised Anomaly Detection. - Lukas Ruff; Robert A. Vandermeulen; Nico Görnitz; Alexander Binder; Emmanuel Müller; Klaus-Robert Müller; Marius Kloft (ICLR, 2020)

Simple and Effective Prevention of Mode Collapse in Deep One-Class Classification. - Penny Chong; Lukas Ruff; Marius Kloft; Alexander Binder (IJCNN, 2020)

Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events. - Guang Yu; Siqi Wang 001; Zhiping Cai; En Zhu; Chuanfu Xu; Jianping Yin; Marius Kloft (ACM Multimedia, 2020)

Sharper Generalization Bounds for Pairwise Learning. - Yunwen Lei; Antoine Ledent; Marius Kloft (NeurIPS, 2020)

An Empirical Study of the Discreteness Prior in Low-Rank Matrix Completion. - Rodrigo Alves; Antoine Ledent; Renato Assunção; Marius Kloft (Preregister@NeurIPS, 2020)

Rethinking Assumptions in Deep Anomaly Detection. - Lukas Ruff; Robert A. Vandermeulen; Billy Joe Franks; Klaus-Robert Müller; Marius Kloft (CoRR, 2020)

How Much Can I Trust You? - Quantifying Uncertainties in Explaining Neural Networks. - Kirill Bykov; Marina M.-C. Höhne; Klaus-Robert Müller; Shinichi Nakajima; Marius Kloft (CoRR, 2020)

Input Hessian Regularization of Neural Networks. - Waleed Mustafa; Robert A. Vandermeulen; Marius Kloft (CoRR, 2020)

Multiple Kernel $k$k-Means with Incomplete Kernels. - Xinwang Liu 002; Xinzhong Zhu; Miaomiao Li 001; Lei Wang 001; En Zhu; Tongliang Liu; Marius Kloft; Dinggang Shen; Jianping Yin; Wen Gao 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks. - Antoine Ledent; Waleed Mustafa; Yunwen Lei; Marius Kloft (AAAI, 2021)

Fine-grained Generalization Analysis of Vector-Valued Learning. - Liang Wu 015; Antoine Ledent; Yunwen Lei; Marius Kloft (AAAI, 2021)

Model Uncertainty Guides Visual Object Tracking. - Lijun Zhou; Antoine Ledent; Qintao Hu; Ting Liu; Jianlin Zhang 001; Marius Kloft (AAAI, 2021)

Explainable Deep One-Class Classification. - Philipp Liznerski; Lukas Ruff; Robert A. Vandermeulen; Billy Joe Franks; Marius Kloft; Klaus-Robert Müller (ICLR, 2021)

Neural Transformation Learning for Deep Anomaly Detection Beyond Images. - Chen Qiu 001; Timo Pfrommer; Marius Kloft; Stephan Mandt; Maja Rudolph (ICML, 2021)

Fine-grained Generalization Analysis of Structured Output Prediction. - Waleed Mustafa; Yunwen Lei; Antoine Ledent; Marius Kloft (IJCAI, 2021)

Learning Interpretable Concept Groups in CNNs. - Saurabh Varshneya; Antoine Ledent; Robert A. Vandermeulen; Yunwen Lei; Matthias Enders; Damian Borth; Marius Kloft (IJCAI, 2021)

Fine-grained Generalization Analysis of Inductive Matrix Completion. - Antoine Ledent; Rodrigo Alves; Yunwen Lei; Marius Kloft (NeurIPS, 2021)

Burst-induced Multi-Armed Bandit for Learning Recommendation. - Rodrigo Alves; Antoine Ledent; Marius Kloft (RecSys, 2021)

Explaining Bayesian Neural Networks. - Kirill Bykov; Marina M.-C. Höhne; Adelaida Creosteanu; Klaus-Robert Müller; Frederick Klauschen; Shinichi Nakajima; Marius Kloft (CoRR, 2021)

Explainability Requires Interactivity. - Matthias Kirchler; Martin Graf; Marius Kloft; Christoph Lippert (CoRR, 2021)

Trainability for Universal GNNs Through Surgical Randomness. - Billy Joe Franks; Markus Anders; Marius Kloft; Pascal Schweitzer (CoRR, 2021)

Efficient and Effective Regularized Incomplete Multi-View Clustering. - Xinwang Liu 002; Miaomiao Li 001; Chang Tang; Jingyuan Xia; Jian Xiong 002; Li Liu 002; Marius Kloft; En Zhu (IEEE Trans. Pattern Anal. Mach. Intell., 2021)

A Unifying Review of Deep and Shallow Anomaly Detection. - Lukas Ruff; Jacob R. Kauffmann; Robert A. Vandermeulen; Grégoire Montavon; Wojciech Samek; Marius Kloft; Thomas G. Dietterich; Klaus-Robert Müller (Proc. IEEE, 2021)

On the Generalization Analysis of Adversarial Learning. - Waleed Mustafa; Yunwen Lei; Marius Kloft (ICML, 2022)

Latent Outlier Exposure for Anomaly Detection with Contaminated Data. - Chen Qiu 001; Aodong Li; Marius Kloft; Maja Rudolph; Stephan Mandt (ICML, 2022)

Raising the Bar in Graph-level Anomaly Detection. - Chen Qiu 001; Marius Kloft; Stephan Mandt; Maja Rudolph (IJCAI, 2022)

transferGWAS: GWAS of images using deep transfer learning. - Matthias Kirchler; Stefan Konigorski; Matthias Norden; Christian Meltendorf; Marius Kloft; Claudia Schurmann; Christoph Lippert (Bioinform., 2022)

Detecting Anomalies within Time Series using Local Neural Transformations. - Tim Schneider; Chen Qiu 001; Marius Kloft; Decky Aspandi-Latif; Steffen Staab; Stephan Mandt; Maja Rudolph (CoRR, 2022)

Unsupervised Anomaly Detection for Auditing Data and Impact of Categorical Encodings. - Ajay Chawda; Stefanie Grimm; Marius Kloft (CoRR, 2022)

Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images. - Philipp Liznerski; Lukas Ruff; Robert A. Vandermeulen; Billy Joe Franks; Klaus-Robert Müller; Marius Kloft (Trans. Mach. Learn. Res., 2022)

Multiview Subspace Clustering via Co-Training Robust Data Representation. - Jiyuan Liu 003; Xinwang Liu 002; Yuexiang Yang; Xifeng Guo 001; Marius Kloft; Liangzhong He (IEEE Trans. Neural Networks Learn. Syst., 2022)

Generalization Bounds for Inductive Matrix Completion in Low-Noise Settings. - Antoine Ledent; Rodrigo Alves; Yunwen Lei; Yann Guermeur; Marius Kloft (AAAI, 2023)

A Call for Standardization and Validation of Text Style Transfer Evaluation. - Phil Ostheimer; Mayank Kumar Nagda; Marius Kloft; Sophie Fellenz (ACL (Findings), 2023)

Training Normalizing Flows from Dependent Data. - Matthias Kirchler; Christoph Lippert; Marius Kloft (ICML, 2023)

Deep Anomaly Detection under Labeling Budget Constraints. - Aodong Li; Chen Qiu 001; Marius Kloft; Padhraic Smyth; Stephan Mandt; Maja Rudolph (ICML, 2023)

Labeling Neural Representations with Inverse Recognition. - Kirill Bykov; Laura Kopf; Shinichi Nakajima; Marius Kloft; Marina M.-C. Höhne (NeurIPS, 2023)

Zero-Shot Anomaly Detection via Batch Normalization. - Aodong Li; Chen Qiu 001; Marius Kloft; Padhraic Smyth; Maja Rudolph; Stephan Mandt (NeurIPS, 2023)

Ordinal Regression for Difficulty Prediction of StepMania Levels. - Billy Joe Franks; Benjamin Dinkelmann; Marius Kloft; Sophie Fellenz (ECML/PKDD (6), 2023)

Ordinal Regression for Difficulty Estimation of StepMania Levels. - Billy Joe Franks; Benjamin Dinkelmann; Sophie Fellenz; Marius Kloft (CoRR, 2023)

Zero-Shot Anomaly Detection without Foundation Models. - Aodong Li; Chen Qiu 001; Marius Kloft; Padhraic Smyth; Maja Rudolph; Stephan Mandt (CoRR, 2023)

Deep Anomaly Detection on Tennessee Eastman Process Data. - Fabian Hartung; Billy Joe Franks; Tobias Michels; Dennis Wagner; Philipp Liznerski; Steffen Reithermann; Sophie Fellenz; Fabian Jirasek; Maja Rudolph; Daniel Neider; Heike Leitte; Chen Song; et al. (CoRR, 2023)

A Systematic Approach to Universal Random Features in Graph Neural Networks. - Billy Joe Franks; Markus Anders; Marius Kloft; Pascal Schweitzer (Trans. Mach. Learn. Res., 2023)

TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection. - Dennis Wagner; Tobias Michels; Florian C. F. Schulz; Arjun Nair; Maja Rudolph; Marius Kloft (Trans. Mach. Learn. Res., 2023)

E$^{3}$3Outlier: a Self-Supervised Framework for Unsupervised Deep Outlier Detection. - Siqi Wang 001; Yijie Zeng; Guang Yu; Zhen Cheng 004; Xinwang Liu 002; Sihang Zhou 001; En Zhu; Marius Kloft; Jianping Yin; Qing Liao 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Orthogonal Inductive Matrix Completion. - Antoine Ledent; Rodrigo Alves; Marius Kloft (IEEE Trans. Neural Networks Learn. Syst., 2023)

Evaluating Dynamic Topic Models. - Charu James; Mayank Nagda; Nooshin Haji Ghassemi; Marius Kloft; Sophie Fellenz (ACL (1), 2024)

Non-vacuous Generalization Bounds for Adversarial Risk in Stochastic Neural Networks. - Waleed Mustafa; Philipp Liznerski; Antoine Ledent; Dennis Wagner; Puyu Wang; Marius Kloft (AISTATS, 2024)

Text Style Transfer Evaluation Using Large Language Models. - Phil Sidney Ostheimer; Mayank Kumar Nagda; Marius Kloft; Sophie Fellenz (LREC/COLING, 2024)

Characterizing Text Datasets with Psycholinguistic Features. - Marcio Monteiro; Charu Karakkaparambil James; Marius Kloft; Sophie Fellenz (EMNLP (Findings), 2024)

Interpretable Tensor Fusion. - Saurabh Varshneya; Antoine Ledent; Philipp Liznerski; Andriy Balinskyy; Purvanshi Mehta; Waleed Mustafa; Marius Kloft (IJCAI, 2024)

Unraveling the Dynamics of Stable and Curious Audiences in Web Systems. - Rodrigo Alves; Antoine Ledent; Renato Assunção; Pedro O. S. Vaz de Melo; Marius Kloft (WWW, 2024)

Reimagining Anomalies: What If Anomalies Were Normal? - Philipp Liznerski; Saurabh Varshneya; Ece Calikus; Sophie Fellenz; Marius Kloft (CoRR, 2024)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; Christoph Lippert; et al. (CoRR, 2024)

AI-based Anomaly Detection for Clinical-Grade Histopathological Diagnostics. - Jonas Dippel; Niklas Prenißl; Julius Hense; Philipp Liznerski; Tobias Winterhoff; Simon Schallenberg; Marius Kloft; Oliver Buchstab; David Horst; Maximilian Alber; Lukas Ruff; Klaus-Robert Müller; et al. (CoRR, 2024)

Anomaly Detection of Tabular Data Using LLMs. - Aodong Li; Yunhan Zhao; Chen Qiu 001; Marius Kloft; Padhraic Smyth; Maja Rudolph; Stephan Mandt (CoRR, 2024)

Comgra: A Tool for Analyzing and Debugging Neural Networks. - Florian Dietz; Sophie Fellenz; Dietrich Klakow; Marius Kloft (CoRR, 2024)

SetPINNs: Set-based Physics-informed Neural Networks. - Mayank Nagda; Phil Ostheimer; Thomas Specht; Frank Rhein; Fabian Jirasek; Marius Kloft; Sophie Fellenz (CoRR, 2024)

Generalization Bounds with Logarithmic Negative-Sample Dependence for Adversarial Contrastive Learning. - Naghmeh Ghanooni; Waleed Mustafa; Yunwen Lei; Anthony Widjaja Lin; Marius Kloft (Trans. Mach. Learn. Res., 2024)

Recommendations with minimum exposure guarantees: A post-processing framework. - Ramon Lopes; Rodrigo Alves; Antoine Ledent; Rodrygo L. T. Santos; Marius Kloft (Expert Syst. Appl., 2024)

Uncertainty-Adjusted Recommendation via Matrix Factorization With Weighted Losses. - Rodrigo Alves; Antoine Ledent; Marius Kloft (IEEE Trans. Neural Networks Learn. Syst., 2024)

Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings. - Billy Joe Franks; Moshe Eliasof; Semih Cantürk; Guy Wolf; Carola-Bibiane Schönlieb; Sophie Fellenz; Marius Kloft (Trans. Mach. Learn. Res., 2025)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Clara Meister; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; et al. (Trans. Mach. Learn. Res., 2025)

Challenging Assumptions in Learning Generic Text Style Embeddings. - Phil Ostheimer; Marius Kloft; Sophie Fellenz (CoRR, 2025)

Multi-level Supervised Contrastive Learning. - Naghmeh Ghanooni; Barbod Pajoum; Harshit Rawal; Sophie Fellenz; Vo Nguyen Le Duy; Marius Kloft (CoRR, 2025)

Sparse Data Generation Using Diffusion Models. - Phil Ostheimer; Mayank Nagda; Marius Kloft; Sophie Fellenz (CoRR, 2025)

Self-Supervised Anomaly Detection With Neural Transformations. - Chen Qiu 001; Marius Kloft; Stephan Mandt; Maja Rudolph (IEEE Trans. Pattern Anal. Mach. Intell., 2025)

Continual Neural Topic Model. - Charu Karakkaparambil James; Waleed Mustafa; Marius Kloft; Sophie Fellenz (CoRR, 2025)