Laurence Aitchison
30 papers · 2014–2025 · 5 conferences · across top CS/AI conferences
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Conferences
ICML (9)
ICLR (8)
NIPS (8)
UAI (4)
CORL (1)
Top co-authors
Keywords
variational inference
(7)
bayesian inference
(5)
gaussian process
(4)
deep gaussian process
(3)
representation learning
(3)
bayesian neural network
(3)
deep wishart process
(2)
importance sampling
(2)
deep kernel machine
(2)
deep kernel process
(2)
latent variable model
(2)
variational autoencoder
(2)
kernel methods
(2)
supervised learning
(1)
text generation
(1)
approximate inference
(1)
data augmentation
(1)
wishart distribution
(1)
multi-task learning
(1)
image classification
(1)
Papers
Residual Stream Analysis with Multi-Layer SAEs
ICLR 2025
Position: Donβt Use the CLT in LLM Evals With Fewer Than a Few Hundred Datapoints
ICML 2025
How to set AdamWβs weight decay as you scale model and dataset size
ICML 2025
Function-Space Learning Rates
ICML 2025
Jacobian Sparse Autoencoders: Sparsify Computations, Not Just Activations
ICML 2025
Using Autodiff to Estimate Posterior Moments, Marginals and Samples
UAI 2024
Instruction Tuning With Loss Over Instructions
NIPS 2024
Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines
NIPS 2024
Convolutional Deep Kernel Machines
ICLR 2024
Bayesian Low-rank Adaptation for Large Language Models
ICLR 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
ICML 2024
A theory of representation learning gives a deep generalisation of kernel methods
ICML 2023
Semi-supervised learning with a principled likelihood from a generative model of data curation
ICLR 2023
Robustness to corruption in pre-trained Bayesian neural networks
ICLR 2023
Massively parallel reweighted wake-sleep
UAI 2023
An improved variational approximate posterior for the deep Wishart process
UAI 2023
Taylor TD-learning
NIPS 2023
Bayesian Neural Network Priors Revisited
ICLR 2022
Data augmentation in Bayesian neural networks and the cold posterior effect
UAI 2022
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes
ICML 2021
A variational approximate posterior for the deep Wishart process
NIPS 2021
A statistical theory of cold posteriors in deep neural networks
ICLR 2021
Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear
CORL 2021
Deep kernel processes
ICML 2021
Why bigger is not always better: on finite and infinite neural networks
ICML 2020
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods
NIPS 2020
Tensor Monte Carlo: Particle Methods for the GPU era
NIPS 2019
Deep Convolutional Networks as shallow Gaussian Processes
ICLR 2019
Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit
NIPS 2017
Fast Sampling-Based Inference in Balanced Neuronal Networks
NIPS 2014