Liam Hodgkinson
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
13
Venues
6
Active years
2021–2025
Best venue rank
A*
Where they publish
Papers
13 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2025 | ICML | Determinant Estimation under Memory Constraints and Neural Scaling Laws. | Siavash Ameli, Chris van der Heide, Liam Hodgkinson, Fred Roosta, Michael W. Mahoney |
| 2025 | ICML | Models of Heavy-Tailed Mechanistic Universality. | Liam Hodgkinson, Zhichao Wang, Michael W. Mahoney |
| 2025 | UAI | Temperature Optimization for Bayesian Deep Learning. | Kenyon Ng, Chris van der Heide, Liam Hodgkinson, Susan Wei |
| 2023 | COLT | Generalization Guarantees via Algorithm-dependent Rademacher Complexity. | Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli |
| 2023 | ICML | Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. | Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney |
| 2023 | KDD | Test Accuracy vs. Generalization Gap: Model Selection in NLP without Accessing Training or Testing Data. | Yaoqing Yang, Ryan Theisen, Liam Hodgkinson, Joseph E. Gonzalez, Kannan Ramchandran, Charles H. Martin, Michael W. Mahoney |
| 2022 | ICML | Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. | Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney |
| 2022 | ICML | Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. | Feynman T. Liang, Michael W. Mahoney, Liam Hodgkinson |
| 2021 | AISTATS | Shadow Manifold Hamiltonian Monte Carlo. | Christopher van der Heide, Fred Roosta, Liam Hodgkinson, Dirk P. Kroese |
| 2021 | ICLR | Lipschitz Recurrent Neural Networks. | N. Benjamin Erichson, Omri Azencot, Alejandro F. Queiruga, Liam Hodgkinson, Michael W. Mahoney |
| 2021 | ICML | Multiplicative Noise and Heavy Tails in Stochastic Optimization. | Liam Hodgkinson, Michael W. Mahoney |
| 2021 | UAI | Stochastic continuous normalizing flows: training SDEs as ODEs. | Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney |
| 2021 | UAI | Geometric rates of convergence for kernel-based sampling algorithms. | Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney |