| Efficient dataset distillation using random feature approximation N Loo, R Hasani, A Amini, D Rus Advances in Neural Information Processing Systems 35, 13877-13891, 2022 | 50 | 2022 |
| Generalized variational continual learning N Loo, S Swaroop, RE Turner arXiv preprint arXiv:2011.12328, 2020 | 49 | 2020 |
| Dataset distillation with convexified implicit gradients N Loo, R Hasani, M Lechner, D Rus International Conference on Machine Learning, 22649-22674, 2023 | 24 | 2023 |
| Evolution of neural tangent kernels under benign and adversarial training N Loo, R Hasani, A Amini, D Rus Advances in Neural Information Processing Systems 35, 11642-11657, 2022 | 7 | 2022 |
| Dataset distillation fixes dataset reconstruction attacks N Loo, R Hasani, M Lechner, D Rus arXiv preprint arXiv:2302.01428 1, 2023 | 4 | 2023 |
| A real‐world evaluation of the implementation of NLP technology in abstract screening of a systematic review S Perlman‐Arrow, N Loo, N Bobrovitz, T Yan, RK Arora Research Synthesis Methods 14 (4), 608-621, 2023 | 1 | 2023 |
| Combining Variational Continual Learning with FiLM Layers N Loo, S Swaroop, RE Turner 4th Lifelong Machine Learning Workshop at ICML 2020, 2020 | 1 | 2020 |
| On the Size and Approximation Error of Distilled Datasets A Maalouf, M Tukan, N Loo, R Hasani, M Lechner, D Rus Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control N Tumma, M Lechner, N Loo, R Hasani, D Rus Causal Representation Learning Workshop at NeurIPS 2023, 2023 | | 2023 |
| On the Size and Approximation Error of Distilled Sets A Maalouf, M Tukan, N Loo, R Hasani, M Lechner, D Rus arXiv preprint arXiv:2305.14113, 2023 | | 2023 |
| Understanding Reconstruction Attacks with the Neural Tangent Kernel and Dataset Distillation N Loo, R Hasani, M Lechner, A Amini, D Rus arXiv preprint arXiv:2302.01428, 2023 | | 2023 |
