| Edward: A library for probabilistic modeling, inference, and criticism D Tran, A Kucukelbir, AB Dieng, M Rudolph, D Liang, DM Blei arXiv preprint arXiv:1610.09787, 2016 | 370 | 2016 |
| Dynamic embeddings for language evolution M Rudolph, D Blei Proceedings of the 2018 World Wide Web Conference, 1003-1011, 2018 | 204 | 2018 |
| Exponential family embeddings M Rudolph, F Ruiz, S Mandt, D Blei Neural Information Processing Systems, 2016 | 160 | 2016 |
| Neural Transformation Learning for Deep Anomaly Detection Beyond Images C Qiu, T Pfrommer, M Kloft, S Mandt, M Rudolph ICML 2021, 2021 | 156 | 2021 |
| Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models JL McClelland, F Hill, M Rudolph, J Baldridge, H Schütze Proceedings of the National Academy of Sciences 117 (42), 25966-25974, 2020 | 107 | 2020 |
| Modeling irregular time series with continuous recurrent units M Schirmer, M Eltayeb, S Lessmann, M Rudolph International conference on machine learning, 19388-19405, 2022 | 95 | 2022 |
| 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 | 65 | 2022 |
| Raising the Bar in Graph-level Anomaly Detection C Qiu, M Kloft, S Mandt, M Rudolph IJCAI 2022, 2022 | 59 | 2022 |
| Structured embedding models for grouped data M Rudolph, F Ruiz, S Athey, D Blei Neural Information Processing Systems, 2017 | 56 | 2017 |
| Extending machine language models toward human-level language understanding JL McClelland, F Hill, M Rudolph, J Baldridge, H Schütze arXiv preprint arXiv:1912.05877, 2019 | 44 | 2019 |
| Dynamic Bernoulli embeddings for language evolution M Rudolph, D Blei arXiv preprint arXiv:1703.08052, 2017 | 40 | 2017 |
| Complex-valued autoencoders for object discovery S Löwe, P Lippe, M Rudolph, M Welling arXiv preprint arXiv:2204.02075, 2022 | 38 | 2022 |
| LoRA ensembles for large language model fine-tuning X Wang, L Aitchison, M Rudolph arXiv preprint arXiv:2310.00035, 2023 | 29 | 2023 |
| Timesead: Benchmarking deep multivariate time-series anomaly detection D Wagner, T Michels, FCF Schulz, A Nair, M Rudolph, M Kloft Transactions on Machine Learning Research, 2023 | 27 | 2023 |
| 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 | 24 | 2022 |
| 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, 40963-40993, 2023 | 23 | 2023 |
| Objective variables for probabilistic revenue maximization in second-price auctions with reserve MR Rudolph, JG Ellis, DM Blei Proceedings of the 25th International Conference on World Wide Web, 1113-1122, 2016 | 22 | 2016 |
| On the challenges and opportunities in generative ai L Manduchi, K Pandey, R Bamler, R Cotterell, S Däubener, S Fellenz, ... arXiv preprint arXiv:2403.00025, 2024 | 17 | 2024 |
| 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 | 16 | 2023 |
| Deep anomaly detection on Tennessee Eastman process data F Hartung, BJ Franks, T Michels, D Wagner, P Liznerski, S Reithermann, ... Chemie Ingenieur Technik 95 (7), 1077-1082, 2023 | 10 | 2023 |
Stephan MandtAssociate Professor, University of California, IrvineCorreu electrònic verificat a uci.edu