| Taxonomizing local versus global structure in neural network loss landscapes Y Yang, L Hodgkinson, R Theisen, J Zou, JE Gonzalez, K Ramchandran, ... Advances in Neural Information Processing Systems 34, 18722-18733, 2021 | 9 | 2021 |
| Deterministic versus stochastic consensus dynamics on graphs D Weber, R Theisen, S Motsch Journal of Statistical Physics 176, 40-68, 2019 | 9 | 2019 |
| Asymptotic flocking for the three-zone model F Cao, S Motsch, A Reamy, R Theisen Mathematical Biosciences and Engineering 17 (6), 7692-7707, 2020 | 6 | 2020 |
| Good classifiers are abundant in the interpolating regime R Theisen, J Klusowski, M Mahoney International Conference on Artificial Intelligence and Statistics, 3376-3384, 2021 | 5* | 2021 |
| Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data Y Yang, R Theisen, L Hodgkinson, JE Gonzalez, K Ramchandran, ... arXiv preprint arXiv:2202.02842, 2022 | 4 | 2022 |
| Evaluating State-of-the-Art Classification Models Against Bayes Optimality R Theisen, H Wang, LR Varshney, C Xiong, R Socher Advances in Neural Information Processing Systems 34, 2021 | 4 | 2021 |
| Global capacity measures for deep relu networks via path sampling R Theisen, JM Klusowski, H Wang, NS Keskar, C Xiong, R Socher arXiv preprint arXiv:1910.10245, 2019 | 4 | 2019 |
| When are ensembles really effective? R Theisen, H Kim, Y Yang, L Hodgkinson, MW Mahoney arXiv preprint arXiv:2305.12313, 2023 | | 2023 |
