Liam Hodgkinson

Publication Activity (10 Years)

Years Active: 2019-2023
Publications (10 Years): 31

Top Topics

Generalization Bounds

Gaussian Process

Information Criterion

Top Venues

Publications

Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. CoRR (2023)
Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney
The Interpolating Information Criterion for Overparameterized Models. CoRR (2023)
Liam Hodgkinson, Christopher van der Heide, Robert Salomone, Fred Roosta, Michael W. Mahoney
A PAC-Bayesian Perspective on the Interpolating Information Criterion. CoRR (2023)
Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. ICML (2023)
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)
Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney
A Heavy-Tailed Algebra for Probabilistic Programming. NeurIPS (2023)
Sarah Sachs, Tim van Erven, Liam Hodgkinson, Rajiv Khanna, Umut Simsekli
Generalization Guarantees via Algorithm-dependent Rademacher Complexity. COLT (2023)
Ryan Theisen, Hyunsuk Kim, Yaoqing Yang, Liam Hodgkinson, Michael W. Mahoney
When are ensembles really effective? CoRR (2023)
Ryan Theisen, Hyunsuk Kim, Yaoqing Yang, Liam Hodgkinson, Michael W. Mahoney
When are ensembles really effective? NeurIPS (2023)
Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney
A Heavy-Tailed Algebra for Probabilistic Programming. CoRR (2023)
Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. ICML (2022)
Feynman T. Liang, Liam Hodgkinson, Michael W. Mahoney
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. CoRR (2022)
Feynman T. Liang, Michael W. Mahoney, Liam Hodgkinson
Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. ICML (2022)
Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. CoRR (2022)
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 (2022)
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 (2021)
Soon Hoe Lim, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney
Noisy Recurrent Neural Networks. CoRR (2021)
Liam Hodgkinson, Robert Salomone, Fred Roosta
Implicit Langevin Algorithms for Sampling From Log-concave Densities. J. Mach. Learn. Res. 22 (2021)
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)
Alejandro Queiruga, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney
Compressing Deep ODE-Nets using Basis Function Expansions. CoRR (2021)
Christopher van der Heide, Fred Roosta, Liam Hodgkinson, Dirk Kroese
Shadow Manifold Hamiltonian Monte Carlo. AISTATS (2021)
Rajiv Khanna, Liam Hodgkinson, Michael W. Mahoney
Geometric rates of convergence for kernel-based sampling algorithms. UAI (2021)
Liam Hodgkinson, Umut Simsekli, Rajiv Khanna, Michael W. Mahoney
Generalization Properties of Stochastic Optimizers via Trajectory Analysis. CoRR (2021)
Liam Hodgkinson, Michael W. Mahoney
Multiplicative Noise and Heavy Tails in Stochastic Optimization. ICML (2021)
Soon Hoe Lim, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney
Noisy Recurrent Neural Networks. NeurIPS (2021)
Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
Stochastic continuous normalizing flows: training SDEs as ODEs. UAI (2021)
Alejandro Queiruga, N. Benjamin Erichson, Liam Hodgkinson, Michael W. Mahoney
Stateful ODE-Nets using Basis Function Expansions. NeurIPS (2021)
N. Benjamin Erichson, Omri Azencot, Alejandro Queiruga, Liam Hodgkinson, Michael W. Mahoney
Lipschitz Recurrent Neural Networks. ICLR (2021)
Liam Hodgkinson, Christopher van der Heide, Fred Roosta, Michael W. Mahoney
Stochastic Normalizing Flows. CoRR (2020)
Liam Hodgkinson, Michael W. Mahoney
Multiplicative noise and heavy tails in stochastic optimization. CoRR (2020)
Liam Hodgkinson, Robert Salomone, Fred (Farbod) Roosta
Implicit Langevin Algorithms for Sampling From Log-concave Densities. CoRR (2019)