| Cycling cancer persister cells arise from lineages with distinct programs Y Oren, M Tsabar, MS Cuoco, L Amir-Zilberstein, HF Cabanos, JC Hütter, ... Nature 596 (7873), 576-582, 2021 | 346 | 2021 |
| Lecture notes in high dimensional statistics P Rigollet, JC Hütter Zentralblatt MATH 1372, 2015 | 324* | 2015 |
| Skin-resident innate lymphoid cells converge on a pathogenic effector state P Bielecki, SJ Riesenfeld, JC Hütter, E Torlai Triglia, MS Kowalczyk, ... Nature, 1-5, 2021 | 157 | 2021 |
| Minimax estimation of smooth optimal transport maps JC Hütter, P Rigollet | 139 | 2021 |
| Optimal rates for total variation denoising JC Hütter, P Rigollet Conference on Learning Theory, 1115-1146, 2016 | 113 | 2016 |
| Statistical optimal transport via factored couplings A Forrow, JC Hütter, M Nitzan, P Rigollet, G Schiebinger, J Weed The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 87 | 2019 |
| Stepwise-edited, human melanoma models reveal mutations’ effect on tumor and microenvironment E Hodis, ET Triglia, JYH Kwon, T Biancalani, LR Zakka, S Parkar, ... Science 376 (6592), eabi8175, 2022 | 46 | 2022 |
| Large-scale differentiable causal discovery of factor graphs R Lopez, JC Hütter, J Pritchard, A Regev Advances in Neural Information Processing Systems 35, 19290-19303, 2022 | 39 | 2022 |
| Biological cartography: Building and benchmarking representations of life S Celik, JC Huetter, S Melo, N Lazar, R Mohan, C Tillinghast, T Biancalani, ... NeurIPS 2022 Workshop on Learning Meaningful Representations of Life, 2022 | 17 | 2022 |
| Sequential Optimal Experimental Design of Perturbation Screens Guided by Multi-modal Priors K Huang, R Lopez, JC Hütter, T Kudo, A Rios, A Regev International Conference on Research in Computational Molecular Biology, 17-37, 2024 | 13 | 2024 |
| NODAGS-Flow: Nonlinear cyclic causal structure learning MG Sethuraman, R Lopez, R Mohan, F Fekri, T Biancalani, JC Hütter International Conference on Artificial Intelligence and Statistics, 6371-6387, 2023 | 13 | 2023 |
| Disentangling shared and group-specific variations in single-cell transcriptomics data with multiGroupVI E Weinberger, R Lopez, JC Hütter, A Regev Machine Learning in Computational Biology, 16-32, 2022 | 13 | 2022 |
| Estimation of Monge matrices JC Hütter, C Mao, P Rigollet, E Robeva | 11 | 2020 |
| Consistency of probability measure quantization by means of power repulsion–attraction potentials M Fornasier, JC Hütter Journal of Fourier Analysis and Applications 22 (3), 694-749, 2016 | 9 | 2016 |
| Maximum likelihood estimation for Brownian motion tree models based on one sample M Truell, JC Hütter, C Squires, P Zwiernik, C Uhler arXiv preprint arXiv:2112.00816, 2021 | 8 | 2021 |
| Building, benchmarking, and exploring perturbative maps of transcriptional and morphological data S Celik, JC Hütter, SM Carlos, NH Lazar, R Mohan, C Tillinghast, ... PLOS Computational Biology 20 (10), e1012463, 2024 | 7 | 2024 |
| A supervised contrastive framework for learning disentangled representations of cell perturbation data X Tu, JC Hütter, ZJ Wang, T Kudo, A Regev, R Lopez BioRxiv, 2024.01. 05.574421, 2024 | 4 | 2024 |
| Estimation rates for sparse linear cyclic causal models JC Huetter, P Rigollet Conference on Uncertainty in Artificial Intelligence, 1169-1178, 2020 | 3 | 2020 |
| Optimal rates for estimation of two-dimensional totally positive distributions JC Hütter, C Mao, P Rigollet, E Robeva Electronic Journal of Statistics 14 (2), 2600-2652, 2020 | 3 | 2020 |
| Toward the Identifiability of Comparative Deep Generative Models R Lopez, JC Huetter, E Hajiramezanali, JK Pritchard, A Regev Causal Learning and Reasoning, 868-912, 2024 | 2 | 2024 |
