Stochastic Normalizing Flows. - Liam Hodgkinson; Christopher van der Heide; Fred Roosta; Michael W. Mahoney (CoRR, 2020)

Shadow Manifold Hamiltonian Monte Carlo. - Christopher van der Heide; Fred Roosta; Liam Hodgkinson; Dirk P. Kroese (AISTATS, 2021)

Lipschitz Recurrent Neural Networks. - N. Benjamin Erichson; Omri Azencot; Alejandro F. Queiruga; Liam Hodgkinson; Michael W. Mahoney (ICLR, 2021)

Multiplicative Noise and Heavy Tails in Stochastic Optimization. - Liam Hodgkinson; Michael W. Mahoney (ICML, 2021)

Noisy Recurrent Neural Networks. - Soon Hoe Lim; N. Benjamin Erichson; Liam Hodgkinson; Michael W. Mahoney (NeurIPS, 2021)

Stateful ODE-Nets using Basis Function Expansions. - Alejandro F. Queiruga; N. Benjamin Erichson; Liam Hodgkinson; Michael W. Mahoney (NeurIPS, 2021)

Taxonomizing local versus global structure in neural network loss landscapes. - Yaoqing Yang; Liam Hodgkinson; Ryan Theisen; Joe Zou; Joseph E. Gonzalez; Kannan Ramchandran; Michael W. Mahoney (NeurIPS, 2021)

Stochastic continuous normalizing flows: training SDEs as ODEs. - Liam Hodgkinson; Christopher van der Heide; Fred Roosta; Michael W. Mahoney (UAI, 2021)

Geometric rates of convergence for kernel-based sampling algorithms. - Rajiv Khanna; Liam Hodgkinson; Michael W. Mahoney (UAI, 2021)

Compressing Deep ODE-Nets using Basis Function Expansions. - Alejandro F. Queiruga; N. Benjamin Erichson; Liam Hodgkinson; Michael W. Mahoney (CoRR, 2021)

Generalization Properties of Stochastic Optimizers via Trajectory Analysis. - Liam Hodgkinson; Umut Simsekli; Rajiv Khanna; Michael W. Mahoney (CoRR, 2021)

Implicit Langevin Algorithms for Sampling From Log-concave Densities. - Liam Hodgkinson; Robert Salomone; Fred Roosta (J. Mach. Learn. Res., 2021)

Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers. - Liam Hodgkinson; Umut Simsekli; Rajiv Khanna; Michael W. Mahoney (ICML, 2022)

Fat-Tailed Variational Inference with Anisotropic Tail Adaptive Flows. - Feynman T. Liang; Michael W. Mahoney; Liam Hodgkinson (ICML, 2022)

Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data. - Yaoqing Yang; Ryan Theisen; Liam Hodgkinson; Joseph E. Gonzalez; Kannan Ramchandran; Charles H. Martin; Michael W. Mahoney (CoRR, 2022)

Generalization Guarantees via Algorithm-dependent Rademacher Complexity. - Sarah Sachs; Tim van Erven; Liam Hodgkinson; Rajiv Khanna; Umut Simsekli (COLT, 2023)

Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes. - Liam Hodgkinson; Christopher van der Heide; Fred Roosta; Michael W. Mahoney (ICML, 2023)

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 (KDD, 2023)

A Heavy-Tailed Algebra for Probabilistic Programming. - Feynman T. Liang; Liam Hodgkinson; Michael W. Mahoney (NeurIPS, 2023)

When are ensembles really effective? - Ryan Theisen; Hyunsuk Kim; Yaoqing Yang; Liam Hodgkinson; Michael W. Mahoney (NeurIPS, 2023)

The Interpolating Information Criterion for Overparameterized Models. - Liam Hodgkinson; Christopher van der Heide; Robert Salomone; Fred Roosta; Michael W. Mahoney (CoRR, 2023)

A PAC-Bayesian Perspective on the Interpolating Information Criterion. - Liam Hodgkinson; Christopher van der Heide; Robert Salomone; Fred Roosta; Michael W. Mahoney (CoRR, 2023)

How many classifiers do we need? - Hyunsuk Kim; Liam Hodgkinson; Ryan Theisen; Michael W. Mahoney (NeurIPS, 2024)

Faithful Label-free Knowledge Distillation. - Evelyn J. Mannix; Liam Hodgkinson; Howard D. Bondell (CoRR, 2024)

Temperature Optimization for Bayesian Deep Learning. - Kenyon Ng; Chris van der Heide; Liam Hodgkinson; Susan Wei (UAI, 2025)

Uncertainty Quantification with the Empirical Neural Tangent Kernel. - Joseph Wilson; Chris van der Heide; Liam Hodgkinson; Fred Roosta (CoRR, 2025)

Determinant Estimation under Memory Constraints and Neural Scaling Laws. - Siavash Ameli; Chris van der Heide; Liam Hodgkinson; Fred Roosta; Michael W. Mahoney (CoRR, 2025)

Models of Heavy-Tailed Mechanistic Universality. - Liam Hodgkinson; Zhichao Wang; Michael W. Mahoney (CoRR, 2025)

Spectral Estimation with Free Decompression. - Siavash Ameli; Chris van der Heide; Liam Hodgkinson; Michael W. Mahoney (CoRR, 2025)