Conference & Journal Papers

• Felipe Oviedo, Anum Kazerouni, Philipp Liznerski, Yixi Xu, Michael Hirano, Robert Anton Vandermeulen, Marius Kloft, Elyse Blum, Adam Alessio, Chris Li, William Weeks, Rahul Dodhia, Juan Lavista Ferres, Habib Rahbar, Savannah Partridge, Cancer detection in breast MRI screening via explainable artificial intelligence anomaly detection, Radiology 2025
• Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam, Dimension-Independent Rates for Structured Neural Density Estimation (preprint), ICML 2025
• Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam, Breaking the curse of dimensionality in structured density estimation, NeurIPS 2024
• Lukas Muttenthaler, Robert A. Vandermeulen*, Qiuyi Zhang, Thomas Unterthiner, Klaus-Robert Müller, Set Learning for Accurate and Calibrated Models, ICLR 2024
• Robert A. Vandermeulen and René Saitenmacher, Generalized Identifiability Bounds for Mixture Models with Grouped Samples, IEEE: Transactions on Information Theory 2024
• Lukas Muttenthaler, Lorenz Linhardt, Jonas Dippel, Robert A. Vandermeulen, Katherine Hermann, Andrew K. Lampinen, Simon Kornblith, Improving neural network representations using human similarity judgments, NeurIPS 2023
• Lukas Muttenthaler, Jonas Dippel, Lorenz Linhardt, Robert A. Vandermeulen, Simon Kornblith, Human alignment of neural network representations, ICLR 2023
• Lukas Muttenthaler, Charles Yang Zheng, Patrick McClure, Robert A. Vandermeulen, Martin N Hebart, Francisco Pereira, VICE: Variational Interpretable Concept Embeddings, NeurIPS 2022
• Philipp Liznerski, Lukas Ruff*, Robert A. Vandermeulen*, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft, Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Images, TMLR 2022
• Robert A. Vandermeulen and Antoine Ledent, Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation, NeurIPS 2021
• Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen, Hakan Bilen, Transfer-Based Semantic Anomaly Detection, ICML 2021 [CODE]
• Saurabh Varshneya, Antoine Ledent, Robert A. Vandermeulen, Yunwen Lei, Matthias Enders, DamianBorth, Marius Kloft, Learning Interpretable Concept Groups in CNNs, IJCAI 2021
• Philipp Liznerski, Lukas Ruff*, Robert A. Vandermeulen*, Billy Joe Franks, Marius Kloft, Klaus-Robert Müller, Explainable Deep One-Class Classification, ICLR 2021 [CODE]
• Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller, A Unifying Review of Deep and Shallow Anomaly Detection, Proceedings of the IEEE 2021
• Alexander Ritchie, Robert A. Vandermeulen*, Clayton Scott, Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations, NeurIPS 2020
• Fabian Jirasek, Rodrigo A. S. Alves, Julie Damay, Robert A. Vandermeulen, Robert Bamler, Michael Bortz, Stephan Mandt, Marius Kloft, Hans Hasse, Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion, Journal of Physical Chemistry Letters 2020
• Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Klaus-Robert Müller, Marius Kloft, Deep Semi-Supervised Anomaly Detection, ICLR 2020 [CODE]
• Robert A. Vandermeulen and Clayton D. Scott, An Operator Theoretic Approach to Nonparametric Mixture Models, Annals of Statistics 2019
• Lukas Ruff, Yury Zemlyanskiy, Robert Vandermeulen, Thomas Schnake, Marius Kloft, Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on Text, ACL 2019 [CODE]
• Lukas Ruff, Robert Vandermeulen*, Nico Goernitz, Lucas Deecke, Shoaib Ahmed Siddiqui, Alexander Binder, Emmanuel Müller, Marius Kloft, Deep One-Class Classification, ICML 2018 [CODE]
• Lucas Deecke, Robert Vandermeulen*, Lukas Ruff, Stephan Mandt, Marius Kloft, Image Anomaly Detection with Generative Adversarial Networks, ECML PKDD 2018
• Robert A. Vandermeulen and Clayton D. Scott, Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space, NeurIPS 2014
• Robert A. Vandermeulen and Clayton D. Scott, Consistency of Robust Kernel Density Estimators, COLT 2013

Workshop Papers

• Lukas Muttenthaler, Lorenz Linhardt, Jonas Dippel, Robert A. Vandermeulen, Simon Kornblith, Human alignment of neural network representations, NeurIPS: SVRHM Workshop 2022
• Lukas Ruff, Robert A. Vandermeulen*, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft, Rethinking Assumptions in Deep Anomaly Detection, ICML: Workshop on Uncertainty & Robustness in Deep Learning 2021
• Robert A. Vandermeulen, René Saitenmacher, Alexander Ritchie, A Proposal for Supervised Density Estimation, NeurIPS: Preregistration Workshop 2020
• Waleed Mustafa, Robert A. Vandermeulen, Marius Kloft, Input Hessian Regularization of Neural Networks, ICML Workshop: Beyond First Order Methods in ML Systems 2020
• Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, Marius Kloft, Deep Support Vector Data Description for Unsupervised and Semi-Supervised Anomaly Detection, ICML Workshop: Uncertainty & Robustness in Deep Learning 2019
• Robert A. Vandermeulen and Clayton D. Scott, An Operator Theoretic Approach to Nonparametric Mixture Models (VIDEO), Information Theory and Applications 2016

Miscellaneous

• Robert A. Vandermeulen, Sample Complexity Using Infinite Multiview Models, 2023
• Robert A. Vandermeulen, Improving Nonparametric Density Estimation with Tensor Decompositions, 2020
• Lucas Deecke, Lukas Ruff, Robert A. Vandermeulen, Hakan Bilen, Deep Anomaly Detection by Residual Adaptation, 2020
• Lukas Ruff, Robert A. Vandermeulen*, Billy Joe Franks, Klaus-Robert Müller, Marius Kloft, Rethinking Assumptions in Deep Anomaly Detection, 2020
• Robert A. Vandermeulen and Clayton D. Scott, On The Identifiability of Mixture Models from Grouped Samples, 2015

* indicates equal contribution as first author.