Exponential Family Embeddings. - Maja Rudolph; Francisco J. R. Ruiz; Stephan Mandt; David M. Blei (NIPS, 2016)

Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve. - Maja R. Rudolph; Joseph G. Ellis; David M. Blei (WWW, 2016)

A Joint Model for Who-to-Follow and What-to-View Recommendations on Behance. - Maja R. Rudolph; Matthew D. Hoffman; Aaron Hertzmann (WWW (Companion Volume), 2016)

Edward: A library for probabilistic modeling, inference, and criticism. - Dustin Tran; Alp Kucukelbir; Adji B. Dieng; Maja Rudolph; Dawen Liang; David M. Blei (CoRR, 2016)

Structured Embedding Models for Grouped Data. - Maja Rudolph; Francisco J. R. Ruiz; Susan Athey; David M. Blei (NIPS, 2017)

Dynamic Bernoulli Embeddings for Language Evolution. - Maja Rudolph; David M. Blei (CoRR, 2017)

Dynamic Embeddings for Language Evolution. - Maja Rudolph; David M. Blei (WWW, 2018)

Exponential Family Embeddings. - Maja Rudolph (2018)

Extending Machine Language Models toward Human-Level Language Understanding. - James L. McClelland; Felix Hill; Maja Rudolph; Jason Baldridge; Hinrich Schütze (CoRR, 2019)

Deterministic Inference of Neural Stochastic Differential Equations. - Andreas Look; Chen Qiu 001; Maja Rudolph; Jan Peters 001; Melih Kandemir (CoRR, 2020)

Variational Dynamic Mixtures. - Chen Qiu 001; Stephan Mandt; Maja Rudolph (CoRR, 2020)

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

Continuous-Discrete Recurrent Kalman Networks for Irregular Time Series. - Mona Schirmer; Mazin Eltayeb; Maja Rudolph (PKDD/ECML Workshops (1), 2021)

Switching Recurrent Kalman Networks. - Giao Nguyen-Quynh; Philipp Becker; Chen Qiu 001; Maja Rudolph; Gerhard Neumann (CoRR, 2021)

History Marginalization Improves Forecasting in Variational Recurrent Neural Networks. - Chen Qiu 001; Stephan Mandt; Maja Rudolph (Entropy, 2021)

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

Modeling Irregular Time Series with Continuous Recurrent Units. - Mona Schirmer; Mazin Eltayeb; Stefan Lessmann; Maja Rudolph (ICML, 2022)

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

Complex-Valued Autoencoders for Object Discovery. - Sindy Löwe; Phillip Lippe; Maja Rudolph; Max Welling (Trans. Mach. Learn. Res., 2022)

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

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

LoRA ensembles for large language model fine-tuning. - Xi Wang; Laurence Aitchison; Maja Rudolph (CoRR, 2023)

Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data. - Clement Fung; Chen Qiu 001; Aodong Li; Maja Rudolph (CoRR, 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)

Efficient Integrators for Diffusion Generative Models. - Kushagra Pandey; Maja Rudolph; Stephan Mandt (ICLR, 2024)

Hybrid Modeling Design Patterns. - Maja Rudolph; Stefan Kurz; Barbara Rakitsch (CoRR, 2024)

Towards Fast Stochastic Sampling in Diffusion Generative Models. - Kushagra Pandey; Maja Rudolph; Stephan Mandt (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)

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

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)

Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior. - Lorenzo Perini; Maja Rudolph; Sabrina Schmedding; Chen Qiu 001 (Trans. Mach. Learn. Res., 2025)

Theoretical Physics Benchmark (TPBench) - a Dataset and Study of AI Reasoning Capabilities in Theoretical Physics. - Daniel J. H. Chung; Zhiqi Gao; Yurii Kvasiuk; Tianyi Li; Moritz Münchmeyer; Maja Rudolph; Frederic Sala; Sai Chaitanya Tadepalli (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)

Test-time Scaling Techniques in Theoretical Physics - A Comparison of Methods on the TPBench Dataset. - Zhiqi Gao; Tianyi Li; Yurii Kvasiuk; Sai Chaitanya Tadepalli; Maja Rudolph; Daniel J. H. Chung; Frederic Sala; Moritz Münchmeyer (CoRR, 2025)