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ORCIDLiam Hodgkinson
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2020 – today
- 2024
[i16]Kenyon Ng, Chris van der Heide, Liam Hodgkinson, Susan Wei:
Temperature Optimization for Bayesian Deep Learning. CoRR abs/2410.05757 (2024)- 2023
- [c15]Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli:
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. COLT 2023: 4863-4880 - [c14]Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. ICML 2023: 13085-13117 - [c13]Yaoqing Yang
, Ryan Theisen, Liam Hodgkinson, Joseph E. Gonzalez, Kannan Ramchandran, Charles H. Martin, Michael W. Mahoney:
Test Accuracy vs. Generalization Gap: Model Selection in NLP without Accessing Training or Testing Data. KDD 2023: 3011-3021 - [c12]Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney:
A Heavy-Tailed Algebra for Probabilistic Programming. NeurIPS 2023 - [c11]Ryan Theisen, Hyunsuk Kim, Yaoqing Yang, Liam Hodgkinson, Michael W. Mahoney:
When are ensembles really effective? NeurIPS 2023 - [i15]Ryan Theisen, Hyunsuk Kim, Yaoqing Yang, Liam Hodgkinson, Michael W. Mahoney:
When are ensembles really effective? CoRR abs/2305.12313 (2023) - [i14]Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney:
A Heavy-Tailed Algebra for Probabilistic Programming. CoRR abs/2306.09262 (2023) - [i13]Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli:
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. CoRR abs/2307.02501 (2023) - [i12]Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney:
The Interpolating Information Criterion for Overparameterized Models. CoRR abs/2307.07785 (2023) - [i11]Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney:
A PAC-Bayesian Perspective on the Interpolating Information Criterion. CoRR abs/2311.07013 (2023) - 2022
- [c10]Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney:
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. ICML 2022: 8774-8795 - [c9]Feynman T. Liang, Michael W. Mahoney, Liam Hodgkinson:
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. ICML 2022: 13257-13270 - [i10]Yaoqing Yang, Ryan Theisen, Liam Hodgkinson, Joseph E. Gonzalez, Kannan Ramchandran, Charles H. Martin, Michael W. Mahoney:
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data. CoRR abs/2202.02842 (2022) - [i9]Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney:
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. CoRR abs/2205.07918 (2022) - [i8]Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. CoRR abs/2210.07612 (2022) - 2021
- [j1]Liam Hodgkinson, Robert Salomone, Fred Roosta:
Implicit Langevin Algorithms for Sampling From Log-concave Densities. J. Mach. Learn. Res. 22: 136:1-136:30 (2021) - [c8]Christopher van der Heide, Fred Roosta, Liam Hodgkinson, Dirk P. Kroese:
Shadow Manifold Hamiltonian Monte Carlo. AISTATS 2021: 1477-1485 - [c7]N. Benjamin Erichson, Omri Azencot, Alejandro F. Queiruga, Liam Hodgkinson, Michael W. Mahoney:
Lipschitz Recurrent Neural Networks. ICLR 2021 - [c6]Liam Hodgkinson, Michael W. Mahoney:
Multiplicative Noise and Heavy Tails in Stochastic Optimization. ICML 2021: 4262-4274 - [c5]Soon Hoe Lim, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney:
Noisy Recurrent Neural Networks. NeurIPS 2021: 5124-5137 - [c4]Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Taxonomizing local versus global structure in neural network loss landscapes. NeurIPS 2021: 18722-18733 - [c3]Alejandro F. Queiruga, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney:
Stateful ODE-Nets using Basis Function Expansions. NeurIPS 2021: 21770-21781 - [c2]Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Stochastic continuous normalizing flows: training SDEs as ODEs. UAI 2021: 1130-1140 - [c1]Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney:
Geometric rates of convergence for kernel-based sampling algorithms. UAI 2021: 2156-2164 - [i7]Soon Hoe Lim, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney:
Noisy Recurrent Neural Networks. CoRR abs/2102.04877 (2021) - [i6]Alejandro F. Queiruga, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney:
Compressing Deep ODE-Nets using Basis Function Expansions. CoRR abs/2106.10820 (2021) - [i5]Yaoqing Yang, Liam Hodgkinson, Ryan Theisen, Joe Zou, Joseph E. Gonzalez, Kannan Ramchandran, Michael W. Mahoney:
Taxonomizing local versus global structure in neural network loss landscapes. CoRR abs/2107.11228 (2021) - [i4]Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney:
Generalization Properties of Stochastic Optimizers via Trajectory Analysis. CoRR abs/2108.00781 (2021) - 2020
- [i3]Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney:
Stochastic Normalizing Flows. CoRR abs/2002.09547 (2020) - [i2]Liam Hodgkinson, Michael W. Mahoney:
Multiplicative noise and heavy tails in stochastic optimization. CoRR abs/2006.06293 (2020)
2010 – 2019
- 2019
- [i1]Liam Hodgkinson, Robert Salomone, Fred (Farbod) Roosta:
Implicit Langevin Algorithms for Sampling From Log-concave Densities. CoRR abs/1903.12322 (2019)
Coauthor Index
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last updated on 2024-12-01 01:16 CET by the dblp team
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