Maja Rudolph

Publication Activity (10 Years)

Years Active: 2016-2024
Publications (10 Years): 39

Top Topics

Recurrent Neural Networks

Probabilistic Model

Anomaly Detection

Top Venues

Trans. Mach. Learn. Res.

Publications

Kushagra Pandey, Maja Rudolph, Stephan Mandt
Efficient Integrators for Diffusion Generative Models. ICLR (2024)
Kushagra Pandey, Maja Rudolph, Stephan Mandt
Towards Fast Stochastic Sampling in Diffusion Generative Models. CoRR (2024)
Lorenzo Perini, Maja Rudolph, Sabrina Schmedding, Chen Qiu
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior. CoRR (2024)
Aodong Li, Yunhan Zhao, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt
Anomaly Detection of Tabular Data Using LLMs. CoRR (2024)
Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin
On the Challenges and Opportunities in Generative AI. CoRR (2024)
Maja Rudolph, Stefan Kurz, Barbara Rakitsch
Hybrid Modeling Design Patterns. CoRR (2024)
Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt
Zero-Shot Anomaly Detection via Batch Normalization. NeurIPS (2023)
Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt
Zero-Shot Anomaly Detection without Foundation Models. CoRR (2023)
Aodong Li, Chen Qiu, Padhraic Smyth, Marius Kloft, Stephan Mandt, Maja Rudolph
Deep Anomaly Detection under Labeling Budget Constraints. CoRR (2023)
Kushagra Pandey, Maja Rudolph, Stephan Mandt
Efficient Integrators for Diffusion Generative Models. CoRR (2023)
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, Benjamin Klöpper, Stephan Mandt, Michael Bortz, Jakob Burger, Hans Hasse, Marius Kloft
Deep Anomaly Detection on Tennessee Eastman Process Data. CoRR (2023)
Xi Wang, Laurence Aitchison, Maja Rudolph
LoRA ensembles for large language model fine-tuning. CoRR (2023)
Clement Fung, Chen Qiu, Aodong Li, Maja Rudolph
Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data. CoRR (2023)
Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph
Deep Anomaly Detection under Labeling Budget Constraints. ICML (2023)
Dennis Wagner, Tobias Michels, Florian C. F. Schulz, Arjun Nair, Maja Rudolph, Marius Kloft
TimeSeAD: Benchmarking Deep Multivariate Time-Series Anomaly Detection. Trans. Mach. Learn. Res. 2023 (2023)
Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph
Raising the Bar in Graph-level Anomaly Detection. CoRR (2022)
Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph
Raising the Bar in Graph-level Anomaly Detection. IJCAI (2022)
Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt
Latent Outlier Exposure for Anomaly Detection with Contaminated Data. CoRR (2022)
Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt
Latent Outlier Exposure for Anomaly Detection with Contaminated Data. ICML (2022)
Tim Schneider, Chen Qiu, Marius Kloft, Decky Aspandi-Latif, Steffen Staab, Stephan Mandt, Maja Rudolph
Detecting Anomalies within Time Series using Local Neural Transformations. CoRR (2022)
Mona Schirmer, Mazin Eltayeb, Stefan Lessmann, Maja Rudolph
Modeling Irregular Time Series with Continuous Recurrent Units. ICML (2022)
Sindy Löwe, Phillip Lippe, Maja Rudolph, Max Welling
Complex-Valued Autoencoders for Object Discovery. CoRR (2022)
Sindy Löwe, Phillip Lippe, Maja Rudolph, Max Welling
Complex-Valued Autoencoders for Object Discovery. Trans. Mach. Learn. Res. 2022 (2022)
Chen Qiu, Stephan Mandt, Maja Rudolph
History Marginalization Improves Forecasting in Variational Recurrent Neural Networks. Entropy 23 (12) (2021)
Mona Schirmer, Mazin Eltayeb, Stefan Lessmann, Maja Rudolph
Modeling Irregular Time Series with Continuous Recurrent Units. CoRR (2021)
Giao Nguyen-Quynh, Philipp Becker, Chen Qiu, Maja Rudolph, Gerhard Neumann
Switching Recurrent Kalman Networks. CoRR (2021)
Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph
Neural Transformation Learning for Deep Anomaly Detection Beyond Images. ICML (2021)
Mona Schirmer, Mazin Eltayeb, Maja Rudolph
Continuous-Discrete Recurrent Kalman Networks for Irregular Time Series. PKDD/ECML Workshops (1) (2021)
Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph
Neural Transformation Learning for Deep Anomaly Detection Beyond Images. CoRR (2021)
Andreas Look, Chen Qiu, Maja Rudolph, Jan Peters, Melih Kandemir
Deterministic Inference of Neural Stochastic Differential Equations. CoRR (2020)
Chen Qiu, Stephan Mandt, Maja Rudolph
Variational Dynamic Mixtures. CoRR (2020)
James L. McClelland, Felix Hill, Maja Rudolph, Jason Baldridge, Hinrich Schütze
Extending Machine Language Models toward Human-Level Language Understanding. CoRR (2019)
Maja Rudolph, David M. Blei
Dynamic Embeddings for Language Evolution. WWW (2018)
Maja Rudolph, Francisco J. R. Ruiz, Susan Athey, David M. Blei
Structured Embedding Models for Grouped Data. CoRR (2017)
Maja Rudolph, David M. Blei
Dynamic Bernoulli Embeddings for Language Evolution. CoRR (2017)
Maja Rudolph, Francisco J. R. Ruiz, Susan Athey, David M. Blei
Structured Embedding Models for Grouped Data. NIPS (2017)
Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei
Exponential Family Embeddings. CoRR (2016)
Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei
Exponential Family Embeddings. NIPS (2016)
Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei
Edward: A library for probabilistic modeling, inference, and criticism. CoRR (2016)