| 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 | 52 | 2021 |
| 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 | 19 | 2019 |
| 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 | 15 | 2022 |
| Raising the Bar in Graph-level Anomaly Detection C Qiu, M Kloft, S Mandt, M Rudolph International Joint Conference on Artificial Intelligence, 2022 | 10 | 2022 |
| 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 | 8 | 2022 |
| History Marginalization Improves Forecasting in Variational Recurrent Neural Networks C Qiu, S Mandt, M Rudolph Entropy 23 (12), 1563, 2021 | 2* | 2021 |
| Switching Recurrent Kalman Networks G Nguyen-Quynh, P Becker, C Qiu, M Rudolph, G Neumann arXiv preprint arXiv:2111.08291, 2021 | 2 | 2021 |
| Deterministic inference of neural stochastic differential equations A Look, C Qiu, M Rudolph, J Peters, M Kandemir arXiv preprint arXiv:2006.08973, 2020 | 2 | 2020 |
| Switching recurrent kalman network G Nguyen, C Qiu, P Becker, M Rudolph, G Neumann US Patent App. 17/516,330, 2023 | | 2023 |
| Zero-Shot Anomaly Detection without Foundation Models A Li, C Qiu, M Kloft, P Smyth, M Rudolph, S Mandt arXiv preprint arXiv:2302.07849, 2023 | | 2023 |
| Deep Anomaly Detection under Labeling Budget Constraints A Li, C Qiu, P Smyth, M Kloft, S Mandt, M Rudolph arXiv preprint arXiv:2302.07832, 2023 | | 2023 |
| Deep Active Anomaly Detection With Diverse Queries A Li, C Qiu, P Smyth, M Kloft, S Mandt, M Rudolph | | 2023 |
| Anomalous region detection with local neural transformations M Rudolph, C Qiu, T Schneider US Patent App. 17/372,204, 2023 | | 2023 |
| Machine learned anomaly detection C Qiu, MR Rudolph, T Pfrommer US Patent App. 17/651,917, 2022 | | 2022 |
| Data driven recognition of anomalies and continuation of sensor data T Bu, C Qiu, MR Rudolph US Patent App. 17/570,542, 2022 | | 2022 |
| Forecasting with deep state space models C Qiu, MR Rudolph US Patent App. 17/407,621, 2022 | | 2022 |
| Predicting a state of a computer-controlled entity A Look, C Qiu, M Kandemir US Patent App. 17/231,757, 2021 | | 2021 |
Maja RudolphSenior Research Scientist, Bosch Center for AIVerified email at cs.columbia.edu
