Liam Hodgkinson
19 papers · 2021–2025 · 7 venues · across top CS/AI venues
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🐣 Hot Topic Early Bird 🌉 Interdisciplinary Bridge · Bronze 🌍 Conference Polyglot · Silver 🗺️ Taxonomy Completionist (34) 🤝 Dynamic Duo (12)
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Dynamic Duo
(12)
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Prolific Year · Productive
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Century Club · Bronze
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(2)
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Research topics
Machine Learning
(16)
Optimization & Theory · Machine Learning
(15)
Deep Learning
(10)
Mathematics & Optimization
(6)
Artificial Intelligence
(5)
Architectures
(5)
Optimization
(5)
Bayesian & Probabilistic · Artificial Intelligence
(4)
Models
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Core Methods
(3)
Optimization & Theory · Deep Learning
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Bayesian & Probabilistic · Machine Learning
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Learning Types · Machine Learning
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Learning Types · Deep Learning
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Mathematics
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Keywords
neural network
(3)
stochastic differential equation
(3)
stochastic optimization
(2)
heavy-tailed distribution
(2)
ensemble learning
(2)
neural network optimization
(2)
generalization bound
(2)
majority vote
(2)
markov chain monte carlo
(2)
variational inference
(2)
recurrent neural network
(2)
error rate
(2)
double descent
(2)
non-convex optimization
(2)
stochastic gradient descent
(2)
uncertainty quantification
(1)
density estimation
(1)
model misspecification
(1)
neural tangent kernel
(1)
langevin dynamics
(1)
Papers
Determinant Estimation under Memory Constraints and Neural Scaling Laws
ICML 2025
Models of Heavy-Tailed Mechanistic Universality
ICML 2025
Temperature Optimization for Bayesian Deep Learning
UAI 2025
How many classifiers do we need?
NeurIPS 2024
A Heavy-Tailed Algebra for Probabilistic Programming
NeurIPS 2023
Generalization Guarantees via Algorithm-dependent Rademacher Complexity
COLT 2023
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
ICML 2023
When are ensembles really effective?
NeurIPS 2023
Fat–Tailed Variational Inference with Anisotropic Tail Adaptive Flows
ICML 2022
Generalization Bounds using Lower Tail Exponents in Stochastic Optimizers
ICML 2022
Geometric rates of convergence for kernel-based sampling algorithms
UAI 2021
Implicit Langevin Algorithms for Sampling From Log-concave Densities
JMLR 2021
Lipschitz Recurrent Neural Networks
ICLR 2021
Multiplicative Noise and Heavy Tails in Stochastic Optimization
ICML 2021
Noisy Recurrent Neural Networks
NeurIPS 2021
Shadow Manifold Hamiltonian Monte Carlo
AISTATS 2021
Stateful ODE-Nets using Basis Function Expansions
NeurIPS 2021
Stochastic continuous normalizing flows: training SDEs as ODEs
UAI 2021
Taxonomizing local versus global structure in neural network loss landscapes
NeurIPS 2021