| How good is the bayes posterior in deep neural networks really? F Wenzel, K Roth, BS Veeling, J Świątkowski, L Tran, S Mandt, J Snoek, ... ICML 2020, 2020 | 491 | 2020 |
| Hyperparameter ensembles for robustness and uncertainty quantification F Wenzel, J Snoek, D Tran, R Jenatton NeurIPS 2020, 2020 | 325 | 2020 |
| Bayesian neural network priors revisited V Fortuin, A Garriga-Alonso, F Wenzel, G Rätsch, R Turner, ... ICRL 2021, 2021 | 206 | 2021 |
| Uncertainty baselines: Benchmarks for uncertainty & robustness in deep learning Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ... arXiv preprint arXiv:2106.04015, 2021 | 127 | 2021 |
| Assaying out-of-distribution generalization in transfer learning F Wenzel, A Dittadi, PV Gehler, CJ Simon-Gabriel, M Horn, D Zietlow, ... NeurIPS 2022, 2022 | 101 | 2022 |
| Quasi-Monte Carlo Variational Inference A Buchholz, F Wenzel, S Mandt ICML 2018, 2018 | 80 | 2018 |
| On the challenges and opportunities in generative ai L Manduchi, K Pandey, C Meister, R Bamler, R Cotterell, S Däubener, ... TMLR, 2024 | 77 | 2024 |
| Scalable Generalized Dynamic Topic Models P Jähnichen, F Wenzel, M Kloft, S Mandt AISTATS 2018, 2018 | 58 | 2018 |
| Bayesian Nonlinear Support Vector Machines for Big Data F Wenzel, T Galy-Fajou, M Deutsch, M Kloft ECML 2017, 2017 | 48 | 2017 |
| Efficient Gaussian process classification using Polya-Gamma data augmentation F Wenzel, T Galy-Fajou, C Donner, M Kloft, M Opper AAAI Conference on Artificial Intelligence 2019, 2019 | 44 | 2019 |
| On stein variational neural network ensembles F D'Angelo, V Fortuin, F Wenzel arXiv preprint arXiv:2106.10760, 2021 | 40 | 2021 |
| Leveraging sparse and shared feature activations for disentangled representation learning M Fumero, F Wenzel, L Zancato, A Achille, E Rodolà, S Soatto, ... NeurIPS 2023, 2023 | 38 | 2023 |
| Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation T Galy-Fajou, F Wenzel, C Donner, M Opper UAI 2019, 2019 | 33 | 2019 |
| Sparse moes meet efficient ensembles JU Allingham, F Wenzel, ZE Mariet, B Mustafa, J Puigcerver, N Houlsby, ... TMLR, 2022 | 30 | 2022 |
| A data augmentation perspective on diffusion models and retrieval MF Burg, F Wenzel, D Zietlow, M Horn, O Makansi, F Locatello, C Russell TMLR, 2023 | 28 | 2023 |
| Are Multimodal Models Robust to Image and Text Perturbations? J Qiu, Y Zhu, X Shi, F Wenzel, Z Tang, D Zhao, B Li, M Li Journal of Data-centric Machine Learning Research, 2024 | 24 | 2024 |
| Evaluating the fairness of discriminative foundation models in computer vision J Ali, M Kleindessner, F Wenzel, K Budhathoki, V Cevher, C Russell AIES 2023: AAAI/ACM Conference on AI, Ethics, and Society, 2023 | 21 | 2023 |
| Benchmarking robustness of multimodal image-text models under distribution shift J Qiu, Y Zhu, X Shi, F Wenzel, Z Tang, D Zhao, B Li, M Li arXiv preprint arXiv:2212.08044, 2022 | 20 | 2022 |
| Sparse probit linear mixed model S Mandt, F Wenzel, S Nakajima, J Cunningham, C Lippert, M Kloft Machine Learning 106, 1621-1642, 2017 | 16 | 2017 |
| Deep classifiers with label noise modeling and distance awareness V Fortuin, M Collier, F Wenzel, J Allingham, J Liu, D Tran, ... TMLR, 2022 | 14 | 2022 |
Stephan MandtAssociate Professor, University of California, IrvineVerified email at uci.edu
