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Posts
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portfolio
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publications
Edward: A library for probabilistic modeling, inference, and criticism
Published in arXiv preprint arXiv:1610.09787, 2016
Recommended citation: Dustin Tran, Alp Kucukelbir, Adji Dieng, Maja Rudolph, Dawen Liang, David Blei. (2016). "Edward: A library for probabilistic modeling, inference, and criticism." arXiv preprint arXiv:1610.09787
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Exponential family embeddings
Published in Neural Information Processing Systems, 2016
Recommended citation: Maja Rudolph, Francisco Ruiz, Stephan Mandt, David Blei. (2016). "Exponential family embeddings." Neural Information Processing Systems, 2016.
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Objective variables for probabilistic revenue maximization in second-price auctions with reserve
Published in Proceedings of the 25th International Conference on World Wide Web, 2016
Recommended citation: Maja Rudolph, Joseph Ellis, David Blei. (2016). "Objective variables for probabilistic revenue maximization in second-price auctions with reserve." Proceedings of the 25th International Conference on World Wide Web, 2016.
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Dynamic Bernoulli embeddings for language evolution
Published in arXiv preprint arXiv:1703.08052, 2017
Recommended citation: Maja Rudolph, David Blei. (2017). "Dynamic Bernoulli embeddings for language evolution." arXiv preprint arXiv:1703.08052
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Structured embedding models for grouped data
Published in Neural Information Processing Systems, 2017
Recommended citation: Maja Rudolph, Francisco Ruiz, Susan Athey, David Blei. (2017). "Structured embedding models for grouped data." Neural Information Processing Systems, 2017.
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Dynamic embeddings for language evolution
Published in Proceedings of the 2018 World Wide Web Conference, 2018
Recommended citation: Maja Rudolph, David Blei. (2018). "Dynamic embeddings for language evolution." Proceedings of the 2018 World Wide Web Conference, 2018.
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Extending machine language models toward human-level language understanding
Published in arXiv preprint arXiv:1912.05877, 2019
Recommended citation: James McClelland, Felix Hill, Maja Rudolph, Jason Baldridge, Hinrich Sch{\"u}tze. (2019). "Extending machine language models toward human-level language understanding." arXiv preprint arXiv:1912.05877
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Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models
Published in Proceedings of the National Academy of Sciences, 2020
Recommended citation: James McClelland, Felix Hill, Maja Rudolph, Jason Baldridge, Hinrich Sch{\"u}tze. (2020). "Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models." Proceedings of the National Academy of Sciences
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Neural Transformation Learning for Deep Anomaly Detection Beyond Images
Published in ICML 2021, 2021
Recommended citation: Chen Qiu, Timo Pfrommer, Marius Kloft, Stephan Mandt, Maja Rudolph. (2021). "Neural Transformation Learning for Deep Anomaly Detection Beyond Images." ICML 2021, 2021.
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Complex-valued autoencoders for object discovery
Published in arXiv preprint arXiv:2204.02075, 2022
Recommended citation: Sindy L{\"o}we, Phillip Lippe, Maja Rudolph, Max Welling. (2022). "Complex-valued autoencoders for object discovery." arXiv preprint arXiv:2204.02075
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Detecting anomalies within time series using local neural transformations
Published in arXiv preprint arXiv:2202.03944, 2022
Recommended citation: Tim Schneider, Chen Qiu, Marius Kloft, Decky Latif, Steffen Staab, Stephan Mandt, Maja Rudolph. (2022). "Detecting anomalies within time series using local neural transformations." arXiv preprint arXiv:2202.03944
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Latent outlier exposure for anomaly detection with contaminated data
Published in International conference on machine learning, 2022
Recommended citation: Chen Qiu, Aodong Li, Marius Kloft, Maja Rudolph, Stephan Mandt. (2022). "Latent outlier exposure for anomaly detection with contaminated data." International conference on machine learning, 2022.
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Modeling irregular time series with continuous recurrent units
Published in International conference on machine learning, 2022
Recommended citation: Mona Schirmer, Mazin Eltayeb, Stefan Lessmann, Maja Rudolph. (2022). "Modeling irregular time series with continuous recurrent units." International conference on machine learning, 2022.
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Raising the Bar in Graph-level Anomaly Detection
Published in IJCAI 2022, 2022
Recommended citation: Chen Qiu, Marius Kloft, Stephan Mandt, Maja Rudolph. (2022). "Raising the Bar in Graph-level Anomaly Detection." IJCAI 2022, 2022.
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Deep anomaly detection under labeling budget constraints
Published in International Conference on Machine Learning, 2023
Recommended citation: Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Stephan Mandt, Maja Rudolph. (2023). "Deep anomaly detection under labeling budget constraints." International Conference on Machine Learning, 2023.
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LoRA ensembles for large language model fine-tuning
Published in arXiv preprint arXiv:2310.00035, 2023
Recommended citation: Xi Wang, Laurence Aitchison, Maja Rudolph. (2023). "LoRA ensembles for large language model fine-tuning." arXiv preprint arXiv:2310.00035
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Timesead: Benchmarking deep multivariate time-series anomaly detection
Published in Transactions on Machine Learning Research, 2023
Recommended citation: Dennis Wagner, Tobias Michels, Florian Schulz, Arjun Nair, Maja Rudolph, Marius Kloft. (2023). "Timesead: Benchmarking deep multivariate time-series anomaly detection." Transactions on Machine Learning Research
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Zero-shot anomaly detection via batch normalization
Published in Advances in Neural Information Processing Systems, 2023
Recommended citation: Aodong Li, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt. (2023). "Zero-shot anomaly detection via batch normalization." Advances in Neural Information Processing Systems
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Anomaly detection of tabular data using llms
Published in arXiv preprint arXiv:2406.16308, 2024
Recommended citation: Aodong Li, Yunhan Zhao, Chen Qiu, Marius Kloft, Padhraic Smyth, Maja Rudolph, Stephan Mandt. (2024). "Anomaly detection of tabular data using llms." arXiv preprint arXiv:2406.16308
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On the challenges and opportunities in generative ai
Published in TMLR, 2025
Recommended citation: Laura Manduchi, Kushagra Pandey, Clara Meister, Robert Bamler, Ryan Cotterell, Sina D{\"a}ubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, others. (2025). "On the challenges and opportunities in generative ai." TMLR
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talks
Session Chair: Bayesian Foundation Models
Published:
Model Selection for Anomaly Detection
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teaching
Introduction to Applied Deep Learning
LIS 640 - Graduate Course, UW-Madison, Information School, 2025
Statistical Frontiers in Foundation Models and LLMs
STAT 992, UW - Madison, Department of Statistics, 2025
In this graduate seminar we will explore statistical frontiers in foundation models and large language models (LLMs).