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Liam Hodgkinson
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Year
Lipschitz recurrent neural networks
NB Erichson, O Azencot, A Queiruga, L Hodgkinson, MW Mahoney
arXiv preprint arXiv:2006.12070, 2020
942020
Multiplicative noise and heavy tails in stochastic optimization
L Hodgkinson, M Mahoney
International Conference on Machine Learning, 4262-4274, 2021
702021
Noisy recurrent neural networks
SH Lim, NB Erichson, L Hodgkinson, MW Mahoney
Advances in Neural Information Processing Systems 34, 5124-5137, 2021
422021
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
31*2021
The reproducing Stein kernel approach for post-hoc corrected sampling
L Hodgkinson, R Salomone, F Roosta
arXiv preprint arXiv:2001.09266, 2020
292020
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
23*2022
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
192021
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
122021
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
112021
Test accuracy vs. generalization gap: Model selection in nlp without accessing training or testing data
Y Yang, R Theisen, L Hodgkinson, JE Gonzalez, K Ramchandran, ...
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery andá…, 2023
10*2023
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
92021
Fat–Tailed Variational Inference with Anisotropic Tail Adaptive Flows
F Liang, M Mahoney, L Hodgkinson
International Conference on Machine Learning, 13257-13270, 2022
62022
Normal approximations for discrete-time occupancy processes
L Hodgkinson, R McVinish, PK Pollett
Stochastic Processes and their Applications 130 (10), 6414-6444, 2020
62020
Monotonicity and double descent in uncertainty estimation with gaussian processes
L Hodgkinson, C Van Der Heide, F Roosta, MW Mahoney
International Conference on Machine Learning, 13085-13117, 2023
32023
Fast approximate simulation of finite long-range spin systems
R McVinish, L Hodgkinson
The Annals of Applied Probability 31 (3), 1443-1473, 2021
32021
The Interpolating Information Criterion for Overparameterized Models
L Hodgkinson, C van der Heide, R Salomone, F Roosta, MW Mahoney
arXiv preprint arXiv:2307.07785, 2023
22023
Compressing Deep ODE-Nets using Basis Function Expansions.
A Queiruga, NB Erichson, L Hodgkinson, MW Mahoney
arXiv preprint arXiv:2106.10820, 2021
22021
When are ensembles really effective?
R Theisen, H Kim, Y Yang, L Hodgkinson, MW Mahoney
Advances in Neural Information Processing Systems 36, 2024
12024
Generalization Guarantees via Algorithm-dependent Rademacher Complexity
S Sachs, T van Erven, L Hodgkinson, R Khanna, U ¬im║ekli
The Thirty Sixth Annual Conference on Learning Theory, 4863-4880, 2023
12023
A Heavy-Tailed Algebra for Probabilistic Programming
FT Liang, L Hodgkinson, MW Mahoney
Advances in Neural Information Processing Systems 36, 2024
2024
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