| Competitive gradient descent F Schäfer, A Anandkumar Advances in Neural Information Processing Systems, 7625-7635, 2019 | 118 | 2019 |
| Sparse Cholesky Factorization by Kullback--Leibler Minimization F Schäfer, M Katzfuss, H Owhadi SIAM Journal on Scientific Computing 43 (3), A2019-A2046, 2021 | 94 | 2021 |
| Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity F Schäfer, TJ Sullivan, H Owhadi Multiscale Modeling & Simulation 19 (2), 688-730, 2021 | 85* | 2021 |
| Implicit competitive regularization in GANs F Schäfer, H Zheng, A Anandkumar arXiv preprint arXiv:1910.05852, 2019 | 34 | 2019 |
| Competitive physics informed networks Q Zeng, Y Kothari, SH Bryngelson, F Schäfer arXiv preprint arXiv:2204.11144, 2022 | 25 | 2022 |
| Zero initialization: Initializing neural networks with only zeros and ones J Zhao, F Schäfer, A Anandkumar arXiv preprint arXiv:2110.12661, 2021 | 23* | 2021 |
| Sparse recovery of elliptic solvers from matrix-vector products F Schäfer, H Owhadi SIAM Journal on Scientific Computing 46 (2), A998-A1025, 2024 | 22 | 2024 |
| Statistical numerical approximation H Owhadi, C Scovel, F Schäfer Notices of the AMS 66 (10), 2019 | 21 | 2019 |
| Multiscale cholesky preconditioning for ill-conditioned problems J Chen, F Schäfer, J Huang, M Desbrun ACM Transactions on Graphics (TOG) 40 (4), 1-13, 2021 | 20 | 2021 |
| Robust Reinforcement Learning: A Constrained Game-theoretic Approach J Yu, C Gehring, F Schäfer, A Anandkumar Learning for Dynamics and Control, 1242-1254, 2021 | 16 | 2021 |
| Sparse Cholesky factorization for solving nonlinear PDEs via Gaussian processes Y Chen, H Owhadi, F Schäfer Mathematics of Computation, 2024 | 14 | 2024 |
| Competitive Mirror Descent F Schäfer, A Anandkumar, H Owhadi arXiv preprint arXiv:2006.10179, 2020 | 10 | 2020 |
| Scalable Bayesian transport maps for high-dimensional non-Gaussian spatial fields M Katzfuss, F Schäfer Journal of the American Statistical Association, 1-15, 2023 | 9 | 2023 |
| Image extrapolation for the time discrete metamorphosis model: Existence and applications A Effland, M Rumpf, F Schäfer SIAM Journal on Imaging Sciences 11 (1), 834-862, 2018 | 8 | 2018 |
| Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization J Cao, M Kang, F Jimenez, H Sang, FT Schaefer, M Katzfuss International Conference on Machine Learning, 3559-3576, 2023 | 7 | 2023 |
| Multiresolution operator decomposition for flow simulation in fractured porous media Q Zhang, H Owhadi, J Yao, F Schäfer, Z Huang, Y Li Journal of Computational Physics 391, 381-396, 2019 | 6 | 2019 |
| Incremental Low-Rank Learning J Zhao, Y Zhang, B Chen, FT Schaefer, A Anandkumar Workshop on Efficient Systems for Foundation Models@ ICML2023, 0 | 6* | |
| A Lagrangian Method for Inverse Problems in Reinforcement Learning PL Bacon, F Schäfer, C Gehring, A Anandkumar, E Brunskill Optimization in RL workshop at NeurIPS 2019, 2019 | 5 | 2019 |
| Time discrete extrapolation in a Riemannian space of images A Effland, M Rumpf, F Schäfer Scale Space and Variational Methods in Computer Vision: 6th International …, 2017 | 2 | 2017 |
| Fast macroscopic forcing method SH Bryngelson, F Schäfer, J Liu, A Mani Journal of Computational Physics 499, 112721, 2024 | 1 | 2024 |
Anima AnandkumarCalifornia Institute of Technology and NVIDIA在 caltech.edu 的电子邮件经过验证
