Maja Rudolph

According to our database, Maja Rudolph authored at least 31 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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, 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

CoRR, 2024

Towards Fast Stochastic Sampling in Diffusion Generative Models.

Kushagra Pandey, Maja Rudolph, Stephan Mandt

CoRR, 2024

Hybrid Modeling Design Patterns.

Maja Rudolph, Stefan Kurz, Barbara Rakitsch

CoRR, 2024

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

Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data.

Clement Fung, Chen Qiu, Aodong Li, Maja Rudolph

CoRR, 2023

Efficient Integrators for Diffusion Generative Models.

Kushagra Pandey, Maja Rudolph, Stephan Mandt

CoRR, 2023

LoRA ensembles for large language model fine-tuning.

Xi Wang, Laurence Aitchison, Maja Rudolph

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, Benjamin Klöpper, Stephan Mandt, Michael Bortz, Jakob Burger, Hans Hasse, Marius Kloft

CoRR, 2023

Zero-Shot Anomaly Detection without Foundation Models.

Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt

CoRR, 2023

Zero-Shot Anomaly Detection via Batch Normalization.

Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Deep Anomaly Detection under Labeling Budget Constraints.

Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph

Proceedings of the International Conference on Machine Learning, 2023

2022

Complex-Valued Autoencoders for Object Discovery.

Sindy Löwe, Phillip Lippe, Maja Rudolph, Max Welling

Trans. Mach. Learn. Res., 2022

Detecting Anomalies within Time Series using Local Neural Transformations.

Tim Schneider, Chen Qiu, Marius Kloft, Decky Aspandi-Latif, Steffen Staab, Stephan Mandt, Maja Rudolph

CoRR, 2022

Raising the Bar in Graph-level Anomaly Detection.

Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

Modeling Irregular Time Series with Continuous Recurrent Units.

Mona Schirmer, Mazin Eltayeb, Stefan Lessmann, Maja Rudolph

Proceedings of the International Conference on Machine Learning, 2022

Latent Outlier Exposure for Anomaly Detection with Contaminated Data.

Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt

Proceedings of the International Conference on Machine Learning, 2022

2021

History Marginalization Improves Forecasting in Variational Recurrent Neural Networks.

Chen Qiu, Stephan Mandt, Maja Rudolph

Entropy, 2021

Switching Recurrent Kalman Networks.

Giao Nguyen-Quynh, Philipp Becker, Chen Qiu, Maja Rudolph, Gerhard Neumann

CoRR, 2021

Continuous-Discrete Recurrent Kalman Networks for Irregular Time Series.

Mona Schirmer, Mazin Eltayeb, Maja Rudolph

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

Neural Transformation Learning for Deep Anomaly Detection Beyond Images.

Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Variational Dynamic Mixtures.

Chen Qiu, Stephan Mandt, Maja Rudolph

CoRR, 2020

Deterministic Inference of Neural Stochastic Differential Equations.

Andreas Look, Chen Qiu, Maja Rudolph, Jan Peters, Melih Kandemir

CoRR, 2020

2019

Extending Machine Language Models toward Human-Level Language Understanding.

James L. McClelland, Felix Hill, Maja Rudolph, Jason Baldridge, Hinrich Schütze

CoRR, 2019

2018

Exponential Family Embeddings.

Maja Rudolph

PhD thesis, 2018

Dynamic Embeddings for Language Evolution.

Maja Rudolph, David M. Blei

Proceedings of the 2018 World Wide Web Conference on World Wide Web, 2018

2017

Dynamic Bernoulli Embeddings for Language Evolution.

Maja Rudolph, David M. Blei

CoRR, 2017

Structured Embedding Models for Grouped Data.

Maja Rudolph, Francisco J. R. Ruiz, Susan Athey, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

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

A Joint Model for Who-to-Follow and What-to-View Recommendations on Behance.

Maja R. Rudolph, Matthew D. Hoffman, Aaron Hertzmann

Proceedings of the 25th International Conference on World Wide Web, 2016

Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve.

Maja R. Rudolph, Joseph G. Ellis, David M. Blei

Proceedings of the 25th International Conference on World Wide Web, 2016

Exponential Family Embeddings.

Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016