Maja Waldron

Maja Waldron

AI researcher with expertise in machine learning, probabilistic modeling, anomaly detection, and generative models.

Publications

You can also find my articles on my Google Scholar profile.
I have previously been publishing under my maiden name Rudolph.
  1. Manduchi, L., Pandey, K., Meister, C., Bamler, R., Cotterell, R., Däubener, S., Fellenz, S., Fischer, A., Gärtner, T., Kirchler, M., & others. (2024). On the challenges and opportunities in generative ai. ArXiv Preprint ArXiv:2403.00025.
  2. Li, A., Zhao, Y., Qiu, C., Kloft, M., Smyth, P., Rudolph, M., & Mandt, S. (2024). Anomaly detection of tabular data using llms. ArXiv Preprint ArXiv:2406.16308.
  3. Li, A., Qiu, C., Kloft, M., Smyth, P., Mandt, S., & Rudolph, M. (2023). Deep anomaly detection under labeling budget constraints. International Conference on Machine Learning, 19882–19910.
  4. Wagner, D., Michels, T., Schulz, F. C. F., Nair, A., Rudolph, M., & Kloft, M. (2023). Timesead: Benchmarking deep multivariate time-series anomaly detection. Transactions on Machine Learning Research.
  5. Li, A., Qiu, C., Kloft, M., Smyth, P., Rudolph, M., & Mandt, S. (2023). Zero-shot anomaly detection via batch normalization. Advances in Neural Information Processing Systems, 36, 40963–40993.
  6. Wang, X., Aitchison, L., & Rudolph, M. (2023). LoRA ensembles for large language model fine-tuning. ArXiv Preprint ArXiv:2310.00035.
  7. Schirmer, M., Eltayeb, M., Lessmann, S., & Rudolph, M. (2022). Modeling irregular time series with continuous recurrent units. International Conference on Machine Learning, 19388–19405.
  8. Schneider, T., Qiu, C., Kloft, M., Latif, D. A., Staab, S., Mandt, S., & Rudolph, M. (2022). Detecting anomalies within time series using local neural transformations. ArXiv Preprint ArXiv:2202.03944.
  9. Qiu, C., Li, A., Kloft, M., Rudolph, M., & Mandt, S. (2022). Latent outlier exposure for anomaly detection with contaminated data. International Conference on Machine Learning, 18153–18167.
  10. Löwe, S., Lippe, P., Rudolph, M., & Welling, M. (2022). Complex-valued autoencoders for object discovery. ArXiv Preprint ArXiv:2204.02075.
  11. Qiu, C., Kloft, M., Mandt, S., & Rudolph, M. (2022). Raising the Bar in Graph-level Anomaly Detection. IJCAI 2022.
  12. Qiu, C., Pfrommer, T., Kloft, M., Mandt, S., & Rudolph, M. (2021). Neural Transformation Learning for Deep Anomaly Detection Beyond Images. ICML 2021.
  13. McClelland, J. L., Hill, F., Rudolph, M., Baldridge, J., & Schütze, H. (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, 117(42), 25966–25974.
  14. McClelland, J. L., Hill, F., Rudolph, M., Baldridge, J., & Schütze, H. (2019). Extending machine language models toward human-level language understanding. ArXiv Preprint ArXiv:1912.05877.
  15. Rudolph, M., & Blei, D. (2018). Dynamic embeddings for language evolution. Proceedings of the 2018 World Wide Web Conference, 1003–1011.
  16. Rudolph, M., Ruiz, F., Athey, S., & Blei, D. (2017). Structured embedding models for grouped data. Neural Information Processing Systems.
  17. Rudolph, M., & Blei, D. (2017). Dynamic Bernoulli embeddings for language evolution. ArXiv Preprint ArXiv:1703.08052.
  18. Tran, D., Kucukelbir, A., Dieng, A. B., Rudolph, M., Liang, D., & Blei, D. M. (2016). Edward: A library for probabilistic modeling, inference, and criticism. ArXiv Preprint ArXiv:1610.09787.
  19. Rudolph, M., Ruiz, F., Mandt, S., & Blei, D. (2016). Exponential family embeddings. Neural Information Processing Systems.
  20. Rudolph, M. R., Ellis, J. G., & Blei, D. M. (2016). Objective variables for probabilistic revenue maximization in second-price auctions with reserve. Proceedings of the 25th International Conference on World Wide Web, 1113–1122.