| Neural transformation learning for deep anomaly detection beyond images C Qiu, T Pfrommer, M Kloft, S Mandt, M Rudolph International conference on machine learning, 8703-8714, 2021 | 144 | 2021 |
| Latent Outlier Exposure for Anomaly Detection with Contaminated Data C Qiu, A Li, M Kloft, M Rudolph, S Mandt International Conference on Machine Learning, 18153-18167, 2022 | 60 | 2022 |
| Raising the Bar in Graph-level Anomaly Detection C Qiu, M Kloft, S Mandt, M Rudolph International Joint Conference on Artificial Intelligence, 2022 | 53 | 2022 |
| Learning topometric semantic maps from occupancy grids M Hiller, C Qiu, F Particke, C Hofmann, J Thielecke 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 33 | 2019 |
| Zero-shot anomaly detection via batch normalization A Li, C Qiu, M Kloft, P Smyth, M Rudolph, S Mandt Advances in Neural Information Processing Systems 36, 2024 | 20 | 2024 |
| Detecting anomalies within time series using local neural transformations T Schneider, C Qiu, M Kloft, DA Latif, S Staab, S Mandt, M Rudolph arXiv preprint arXiv:2202.03944, 2022 | 20 | 2022 |
| Deep anomaly detection under labeling budget constraints A Li, C Qiu, M Kloft, P Smyth, S Mandt, M Rudolph International Conference on Machine Learning, 19882-19910, 2023 | 15 | 2023 |
| Federated text-driven prompt generation for vision-language models C Qiu, X Li, CK Mummadi, MR Ganesh, Z Li, L Peng, WY Lin The Twelfth International Conference on Learning Representations, 2024 | 12* | 2024 |
| Switching recurrent Kalman networks G Nguyen-Quynh, P Becker, C Qiu, M Rudolph, G Neumann arXiv preprint arXiv:2111.08291, 2021 | 4 | 2021 |
| Anomaly detection of tabular data using llms A Li, Y Zhao, C Qiu, M Kloft, P Smyth, M Rudolph, S Mandt arXiv preprint arXiv:2406.16308, 2024 | 2 | 2024 |
| Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data C Fung, C Qiu, A Li, M Rudolph arXiv preprint arXiv:2310.10461, 2023 | 2 | 2023 |
| Switching recurrent kalman network G Nguyen, C Qiu, P Becker, M Rudolph, G Neumann US Patent App. 17/516,330, 2023 | 2 | 2023 |
| Self-Supervised Anomaly Detection with Neural Transformations C Qiu Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, 2023 | 2 | 2023 |
| Forecasting with deep state space models C Qiu, MR Rudolph US Patent App. 17/407,621, 2022 | 2 | 2022 |
| History marginalization improves forecasting in variational recurrent neural networks C Qiu, S Mandt, M Rudolph Entropy 23 (12), 1563, 2021 | 2* | 2021 |
| Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior L Perini, M Rudolph, S Schmedding, C Qiu arXiv preprint arXiv:2405.13699, 2024 | 1 | 2024 |
| Anomalous region detection with local neural transformations M Rudolph, C Qiu, T Schneider US Patent App. 17/372,204, 2023 | 1 | 2023 |
| Machine learned anomaly detection C Qiu, MR Rudolph, T Pfrommer US Patent App. 17/651,917, 2022 | 1 | 2022 |
| Adaptively centered representation for zero-shot anomaly detection methods C Qiu, M Rudolph, A Li US Patent App. 18/170,253, 2024 | | 2024 |
| Method and system for graph level anomaly detection C Qiu, M Rudolph US Patent 11,978,188, 2024 | | 2024 |
Maja RudolphUW-MadisonVerified email at wisc.edu
