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← Authors

Liam Hodgkinson

19 papers · 2021–2025 · 7 conferences · across top CS/AI conferences

Achievements

Jump to papers ↓
+9 more ↓ 🌍 Conference Polyglot (7) 🐣 Hot Topic Early Bird 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge 🐝 Cross-Pollinator (13)
🧭 Keyword Pioneer 🀝 Dynamic Duo (12) πŸ‘‘ Triple Crown πŸ† Keyword Champion (2) πŸ’Ž Century Club (19) ⚑ Prolific Year (9) ❓ The Questioner (2) πŸ—ƒοΈ Keyword Collector (84) πŸ”₯ Unstoppable (5)

Conferences

ICML (6) NIPS (6) UAI (3) AISTATS (1) COLT (1) ICLR (1) JMLR (1)

Top co-authors

Michael W. Mahoney (12) Chris van der Heide (5) Fred Roosta (5) Rajiv Khanna (3) Michael Mahoney (3) Ryan Theisen (3) N. Benjamin Erichson (3) Yaoqing Yang (2) Hyunsuk Kim (2) Alejandro Queiruga (2)

Keywords

neural network (3) stochastic differential equation (3) stochastic optimization (2) error rate (2) double descent (2) neural network optimization (2) heavy-tailed distribution (2) markov chain monte carlo (2) variational inference (2) ensemble learning (2) non-convex optimization (2) generalization bound (2) majority vote (2) batch normalization (1) stochastic gradient descent (1) uncertainty quantification (1) density estimation (1) langevin dynamics (1) gaussian process (1) image classification (1)

Papers

Models of Heavy-Tailed Mechanistic Universality ICML 2025 Temperature Optimization for Bayesian Deep Learning UAI 2025 Determinant Estimation under Memory Constraints and Neural Scaling Laws ICML 2025 How many classifiers do we need? NIPS 2024 When are ensembles really effective? NIPS 2023 Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes ICML 2023 Generalization Guarantees via Algorithm-dependent Rademacher Complexity COLT 2023 A Heavy-Tailed Algebra for Probabilistic Programming NIPS 2023 Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers ICML 2022 Fat–Tailed Variational Inference with Anisotropic Tail Adaptive Flows ICML 2022 Multiplicative Noise and Heavy Tails in Stochastic Optimization ICML 2021 Taxonomizing local versus global structure in neural network loss landscapes NIPS 2021 Stateful ODE-Nets using Basis Function Expansions NIPS 2021 Shadow Manifold Hamiltonian Monte Carlo AISTATS 2021 Lipschitz Recurrent Neural Networks ICLR 2021 Noisy Recurrent Neural Networks NIPS 2021 Implicit Langevin Algorithms for Sampling From Log-concave Densities JMLR 2021 Stochastic continuous normalizing flows: training SDEs as ODEs UAI 2021 Geometric rates of convergence for kernel-based sampling algorithms UAI 2021