| Inverse problems and data assimilation D Sanz-Alonso, AM Stuart, A Taeb Cambridge University Press, 2023 | 45* | 2023 |
| Visual stylometry using background selection and wavelet-HMT-based Fisher information distances for attribution and dating of impressionist paintings H Qi, A Taeb, SM Hughes Signal Processing 93 (3), 541-553, 2013 | 24 | 2013 |
| Interpreting latent variables in factor models via convex optimization A Taeb, V Chandrasekaran Mathematical programming 167, 129-154, 2018 | 15 | 2018 |
| A statistical graphical model of the California reservoir system A Taeb, JT Reager, M Turmon, V Chandrasekaran Water Resources Research 53 (11), 9721-9739, 2017 | 15 | 2017 |
| A fast non-parametric approach for local causal structure learning M Azadkia, A Taeb, P Bühlmann arXiv preprint arXiv:2111.14969, 2022 | 9* | 2022 |
| A Look at Robustness and Stability of 1-versus 0-Regularization: Discussion of Papers by Bertsimas et al. and Hastie et al. Y Chen, A Taeb, P Bühlmann Statistical Science 35 (4), 614-622, 2020 | 9* | 2020 |
| False discovery and its control in low rank estimation A Taeb, P Shah, V Chandrasekaran Journal of Royal Statistical Society, Series B, 2020 | 8 | 2020 |
| Maximin analysis of message passing algorithms for recovering block sparse signals A Taeb, A Maleki, C Studer, R Baraniuk Signal Processing with Adaptive Sparse Structured Representations (SPARS), 2013 | 8 | 2013 |
| Provable concept learning for interpretable predictions using variational inference A Taeb, N Ruggeri, C Schnuck, F Yang ICML workshop on AI4Science, 2022 | 7* | 2022 |
| Learning and scoring Gaussian latent causal models with unknown additive interventions A Taeb, JL Gamella, C Heinze-Deml, P Bühlmann arXiv preprint arXiv:2101.06950, 2023 | 5* | 2023 |
| Learning exponential family graphical models with latent variables using regularized conditional likelihood A Taeb, P Shah, V Chandrasekaran arXiv preprint arXiv:2010.09386, 2020 | 3 | 2020 |
| Model Selection over Partially Ordered Sets A Taeb, P Bühlmann, V Chandrasekaran Proceedings of National Academy of Sciences, 2023 | 2 | 2023 |
| Causality-oriented robustness: exploiting general additive interventions X Shen, P Bühlmann, A Taeb arXiv preprint arXiv:2307.10299, 2023 | 2 | 2023 |
| Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions JL Gamella, A Taeb, C Heinze-Deml, P Bühlmann arXiv preprint arXiv:2211.14897, 2022 | 1 | 2022 |
| Extremal graphical modeling with latent variables S Engelke, A Taeb arXiv preprint arXiv:2403.09604, 2024 | | 2024 |
| Latent-Variable Modeling: Algorithms, Inference, and Applications A Taeb California Institute of Technology, 2020 | | 2020 |
