| Lipschitz recurrent neural networks NB Erichson, O Azencot, A Queiruga, L Hodgkinson, MW Mahoney arXiv preprint arXiv:2006.12070, 2020 | 62 | 2020 |
| Multiplicative noise and heavy tails in stochastic optimization L Hodgkinson, M Mahoney International Conference on Machine Learning, 4262-4274, 2021 | 44 | 2021 |
| The reproducing Stein kernel approach for post-hoc corrected sampling L Hodgkinson, R Salomone, F Roosta arXiv preprint arXiv:2001.09266, 2020 | 26 | 2020 |
| Noisy recurrent neural networks SH Lim, NB Erichson, L Hodgkinson, MW Mahoney Advances in Neural Information Processing Systems 34, 5124-5137, 2021 | 25 | 2021 |
| Stochastic normalizing flows L Hodgkinson, C van der Heide, F Roosta, MW Mahoney arXiv preprint arXiv:2002.09547, 2020 | 21 | 2020 |
| Stateful ode-nets using basis function expansions A Queiruga, NB Erichson, L Hodgkinson, MW Mahoney Advances in Neural Information Processing Systems 34, 21770-21781, 2021 | 9 | 2021 |
| 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 |
| Shadow manifold hamiltonian monte carlo C van der Heide, F Roosta, L Hodgkinson, D Kroese International Conference on Artificial Intelligence and Statistics, 1477-1485, 2021 | 9 | 2021 |
| Implicit Langevin algorithms for sampling from log-concave densities L Hodgkinson, R Salomone, F Roosta The Journal of Machine Learning Research 22 (1), 6055-6084, 2021 | 8 | 2021 |
| Generalization properties of stochastic optimizers via trajectory analysis L Hodgkinson, U Şimşekli, R Khanna, MW Mahoney arXiv preprint arXiv:2108.00781, 2021 | 6 | 2021 |
| Normal approximations for discrete-time occupancy processes L Hodgkinson, R McVinish, PK Pollett Stochastic Processes and their Applications 130 (10), 6414-6444, 2020 | 6 | 2020 |
| Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers L Hodgkinson, U Simsekli, R Khanna, M Mahoney International Conference on Machine Learning, 8774-8795, 2022 | 5 | 2022 |
| 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 |
| Fast approximate simulation of finite long-range spin systems R McVinish, L Hodgkinson The Annals of Applied Probability 31 (3), 1443-1473, 2021 | 3 | 2021 |
| Stochastic continuous normalizing flows: training SDEs as ODEs L Hodgkinson, C van der Heide, F Roosta, MW Mahoney Uncertainty in Artificial Intelligence, 1130-1140, 2021 | 2 | 2021 |
| Fat–Tailed Variational Inference with Anisotropic Tail Adaptive Flows F Liang, M Mahoney, L Hodgkinson International Conference on Machine Learning, 13257-13270, 2022 | 1 | 2022 |
| Compressing Deep ODE-Nets using Basis Function Expansions. A Queiruga, NB Erichson, L Hodgkinson, MW Mahoney arXiv preprint arXiv:2106.10820, 2021 | 1 | 2021 |
| When are ensembles really effective? R Theisen, H Kim, Y Yang, L Hodgkinson, MW Mahoney arXiv preprint arXiv:2305.12313, 2023 | | 2023 |
| Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes L Hodgkinson, C van der Heide, F Roosta, MW Mahoney arXiv preprint arXiv:2210.07612, 2022 | | 2022 |
| Geometric rates of convergence for kernel-based sampling algorithms R Khanna, L Hodgkinson, MW Mahoney Uncertainty in Artificial Intelligence, 2156-2164, 2021 | | 2021 |
Michael MahoneyProfessor of Statistics, UC BerkeleyVerified email at stat.berkeley.edu
