| 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 | 331 | 2016 |
| Dynamic embeddings for language evolution M Rudolph, D Blei Proceedings of the 2018 World Wide Web Conference, 1003-1011, 2018 | 149 | 2018 |
| Exponential family embeddings M Rudolph, F Ruiz, S Mandt, D Blei Neural Information Processing Systems, 2016 | 140 | 2016 |
| 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 | 55 | 2020 |
| Structured embedding models for grouped data M Rudolph, F Ruiz, S Athey, D Blei Neural Information Processing Systems, 2017 | 49 | 2017 |
| Neural Transformation Learning for Deep Anomaly Detection Beyond Images C Qiu, T Pfrommer, M Kloft, S Mandt, M Rudolph ICML 2021, 2021 | 40 | 2021 |
| Dynamic Bernoulli embeddings for language evolution M Rudolph, D Blei arXiv preprint arXiv:1703.08052, 2017 | 35 | 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 | 34 | 2019 |
| 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 | 18 | 2016 |
| 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 | 12 | 2022 |
| Modeling irregular time series with continuous recurrent units M Schirmer, M Eltayeb, S Lessmann, M Rudolph International Conference on Machine Learning, 19388-19405, 2022 | 9 | 2022 |
| Raising the Bar in Graph-level Anomaly Detection C Qiu, M Kloft, S Mandt, M Rudolph IJCAI 2022, 2022 | 8 | 2022 |
| Complex-valued autoencoders for object discovery S Löwe, P Lippe, M Rudolph, M Welling arXiv preprint arXiv:2204.02075, 2022 | 8 | 2022 |
| Edward: A library for probabilistic modeling, inference, and criticism. arXiv 2016 D Tran, A Kucukelbir, AB Dieng, M Rudolph, D Liang, DM Blei arXiv preprint arXiv:1610.09787, 2016 | 8 | 2016 |
| 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 | 7 | 2022 |
| A joint model for who-to-follow and what-to-view recommendations on behance MR Rudolph, M Hoffman, A Hertzmann Proceedings of the 25th International Conference Companion on World Wide Web …, 2016 | 3 | 2016 |
| 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 |
| History Marginalization Improves Forecasting in Variational Recurrent Neural Networks C Qiu, S Mandt, M Rudolph Entropy 23 (12), 1563, 2021 | 1 | 2021 |
| Variational dynamic mixtures C Qiu, S Mandt, M Rudolph arXiv preprint arXiv:2010.10403, 2020 | 1 | 2020 |
| Deep Anomaly Detection on Tennessee Eastman Process Data F Hartung, BJ Franks, T Michels, D Wagner, P Liznerski, S Reithermann, ... arXiv preprint arXiv:2303.05904, 2023 | | 2023 |
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
