Fast Sampling-Based Inference in Balanced Neuronal Networks. - Guillaume Hennequin; Laurence Aitchison; Máté Lengyel (NIPS, 2014)

Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making. - Laurence Aitchison; Dan Bang; Bahador Bahrami; Peter E. Latham (PLoS Comput. Biol., 2015)

Zipf's Law Arises Naturally When There Are Underlying, Unobserved Variables. - Laurence Aitchison; Nicola Corradi; Peter E. Latham (PLoS Comput. Biol., 2016)

The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics. - Laurence Aitchison; Máté Lengyel (PLoS Comput. Biol., 2016)

Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit. - Laurence Aitchison; Lloyd Russell; Adam M. Packer; Jinyao Yan; Philippe Castonguay; Michael Häusser; Srinivas C. Turaga (NIPS, 2017)

Discrete flow posteriors for variational inference in discrete dynamical systems. - Laurence Aitchison; Vincent Adam; Srinivas C. Turaga (CoRR, 2018)

A unified theory of adaptive stochastic gradient descent as Bayesian filtering. - Laurence Aitchison (CoRR, 2018)

Deep Convolutional Networks as shallow Gaussian Processes. - Adrià Garriga-Alonso; Carl Edward Rasmussen; Laurence Aitchison (ICLR (Poster), 2019)

Tensor Monte Carlo: Particle Methods for the GPU era. - Laurence Aitchison (NeurIPS, 2019)

Why bigger is not always better: on finite and infinite neural networks. - Laurence Aitchison (ICML, 2020)

Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods. - Laurence Aitchison (NeurIPS, 2020)

A statistical theory of semi-supervised learning. - Laurence Aitchison (CoRR, 2020)

Legally grounded fairness objectives. - Dylan Holden-Sim; Gavin Leech; Laurence Aitchison (CoRR, 2020)

Gradient Regularisation as Approximate Variational Inference. - Ali Unlu; Laurence Aitchison (CoRR, 2020)

Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear. - Anupam K. Gupta; Laurence Aitchison; Nathan F. Lepora (CoRL, 2021)

A statistical theory of cold posteriors in deep neural networks. - Laurence Aitchison (ICLR, 2021)

Deep Kernel Processes. - Laurence Aitchison; Adam X. Yang; Sebastian W. Ober (ICML, 2021)

Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes. - Sebastian W. Ober; Laurence Aitchison (ICML, 2021)

A variational approximate posterior for the deep Wishart process. - Sebastian W. Ober; Laurence Aitchison (NeurIPS, 2021)

Bayesian Neural Network Priors Revisited. - Vincent Fortuin; Adrià Garriga-Alonso; Florian Wenzel; Gunnar Rätsch; Richard E. Turner; Mark van der Wilk; Laurence Aitchison (CoRR, 2021)

A statistical theory of out-of-distribution detection. - Xi Wang; Laurence Aitchison (CoRR, 2021)

BNNpriors: A library for Bayesian neural network inference with different prior distributions. - Vincent Fortuin; Adrià Garriga-Alonso; Mark van der Wilk; Laurence Aitchison (CoRR, 2021)

InfoNCE is a variational autoencoder. - Laurence Aitchison (CoRR, 2021)

A fast point solver for deep nonlinear function approximators. - Laurence Aitchison (CoRR, 2021)

Gradient Regularization as Approximate Variational Inference. - Ali Unlu; Laurence Aitchison (Entropy, 2021)

BNNpriors: A library for Bayesian neural network inference with different prior distributions. - Vincent Fortuin; Adrià Garriga-Alonso; Mark van der Wilk; Laurence Aitchison (Softw. Impacts, 2021)

Bayesian Neural Network Priors Revisited. - Vincent Fortuin; Adrià Garriga-Alonso; Sebastian W. Ober; Florian Wenzel; Gunnar Rätsch; Richard E. Turner; Mark van der Wilk; Laurence Aitchison (ICLR, 2022)

Active Inference for Artificial Touch: A Biologically-Plausible Tactile Control Method. - Pernilla Craig; Laurence Aitchison; Nathan F. Lepora (Living Machines, 2022)

Data augmentation in Bayesian neural networks and the cold posterior effect. - Seth Nabarro; Stoil Ganev; Adrià Garriga-Alonso; Vincent Fortuin; Mark van der Wilk; Laurence Aitchison (UAI, 2022)

Out of distribution robustness with pre-trained Bayesian neural networks. - Xi Wang; Laurence Aitchison (CoRR, 2022)

What deep reinforcement learning tells us about human motor learning and vice-versa. - Michele Garibbo; Casimir J. H. Ludwig; Nathan F. Lepora; Laurence Aitchison (CoRR, 2022)

Random initialisations performing above chance and how to find them. - Frederik Benzing; Simon Schug; Robert Meier; Johannes von Oswald; Yassir Akram; Nicolas Zucchet; Laurence Aitchison; Angelika Steger (CoRR, 2022)

Machine learning emulation of a local-scale UK climate model. - Henry Addison; Elizabeth Kendon; Suman V. Ravuri; Laurence Aitchison; Peter A. G. Watson (CoRR, 2022)

Semi-supervised learning with a principled likelihood from a generative model of data curation. - Stoil Ganev; Laurence Aitchison (ICLR, 2023)

Robustness to corruption in pre-trained Bayesian neural networks. - Xi Wang; Laurence Aitchison (ICLR, 2023)

A theory of representation learning gives a deep generalisation of kernel methods. - Adam X. Yang; Maxime Robeyns; Edward Milsom; Ben Anson; Nandi Schoots; Laurence Aitchison (ICML, 2023)

Taylor TD-learning. - Michele Garibbo; Maxime Robeyns; Laurence Aitchison (NeurIPS, 2023)

Massively parallel reweighted wake-sleep. - Thomas Heap; Gavin Leech; Laurence Aitchison (UAI, 2023)

An improved variational approximate posterior for the deep Wishart process. - Sebastian W. Ober; Ben Anson; Edward Milsom; Laurence Aitchison (UAI, 2023)

Imitating careful experts to avoid catastrophic events. - Jack R. P. Hanslope; Laurence Aitchison (CoRR, 2023)

Decision trees compensate for model misspecification. - Hugh Panton; Gavin Leech; Laurence Aitchison (CoRR, 2023)

MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning. - Adam X. Yang; Laurence Aitchison; Henry B. Moss (CoRR, 2023)

LoRA ensembles for large language model fine-tuning. - Xi Wang; Laurence Aitchison; Maja Rudolph (CoRR, 2023)

Convolutional Deep Kernel Machines. - Edward Milsom; Ben Anson; Laurence Aitchison (ICLR, 2024)

Bayesian Low-rank Adaptation for Large Language Models. - Adam X. Yang; Maxime Robeyns; Xi Wang; Laurence Aitchison (ICLR, 2024)

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (ICML, 2024)

Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines. - Edward Milsom; Ben Anson; Laurence Aitchison (NeurIPS, 2024)

Instruction Tuning With Loss Over Instructions. - Zhengxiang Shi; Adam X. Yang; Bin Wu 025; Laurence Aitchison; Emine Yilmaz; Aldo Lipani (NeurIPS, 2024)

Using Autodiff to Estimate Posterior Moments, Marginals and Samples. - Sam Bowyer; Thomas Heap; Laurence Aitchison (UAI, 2024)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (CoRR, 2024)

Flexible infinite-width graph convolutional networks and the importance of representation learning. - Ben Anson; Edward Milsom; Laurence Aitchison (CoRR, 2024)

Bayesian Reward Models for LLM Alignment. - Adam X. Yang; Maxime Robeyns; Thomas Coste; Jun Wang 012; Haitham Bou-Ammar; Laurence Aitchison (CoRR, 2024)

Batch size invariant Adam. - Xi Wang; Laurence Aitchison (CoRR, 2024)

How to set AdamW's weight decay as you scale model and dataset size. - Xi Wang; Laurence Aitchison (CoRR, 2024)

Instruction Tuning With Loss Over Instructions. - Zhengyan Shi; Adam X. Yang; Bin Wu 025; Laurence Aitchison; Emine Yilmaz; Aldo Lipani (CoRR, 2024)

Using Neural Networks for Data Cleaning in Weather Datasets. - Jack R. P. Hanslope; Laurence Aitchison (CoRR, 2024)

Questionable practices in machine learning. - Gavin Leech; Juan J. Vazquez; Misha Yagudin; Niclas Kupper; Laurence Aitchison (CoRR, 2024)

Machine learning emulation of precipitation from km-scale regional climate simulations using a diffusion model. - Henry Addison; Elizabeth Kendon; Suman V. Ravuri; Laurence Aitchison; Peter A. G. Watson (CoRR, 2024)

Human-inspired Perspectives: A Survey on AI Long-term Memory. - Zihong He; Weizhe Lin; Hao Zheng; Fan Zhang; Matt W. Jones; Laurence Aitchison; Xuhai Xu; Miao Liu; Per Ola Kristensson; Junxiao Shen (CoRR, 2024)

Why you don't overfit, and don't need Bayes if you only train for one epoch. - Laurence Aitchison (CoRR, 2024)

Relating Human Error-Based Learning to Modern Deep RL Algorithms. - Michele Garibbo; Casimir J. H. Ludwig; Nathan F. Lepora; Laurence Aitchison (Neural Comput., 2024)

InfoNCE is variational inference in a recognition parameterised model. - Laurence Aitchison; Stoil Ganev (Trans. Mach. Learn. Res., 2024)

TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction Using Vision-Based Tactile Sensing. - Mauro Comi; Yijiong Lin; Alex Church; Alessio Tonioni; Laurence Aitchison; Nathan F. Lepora (IEEE Robotics Autom. Lett., 2024)

Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces. - Mauro Comi; Alessio Tonioni; Jonathan Tremblay; Max Yang; Valts Blukis; Yijiong Lin; Nathan F. Lepora; Laurence Aitchison (3DV, 2025)

Massively Parallel Expectation Maximization For Approximate Posteriors. - Thomas Heap; Sam Bowyer; Laurence Aitchison (AABI, 2025)

Residual Stream Analysis with Multi-Layer SAEs. - Tim Lawson; Lucy Farnik; Conor Houghton; Laurence Aitchison (ICLR, 2025)

Flexible Infinite-Width Graph Convolutional Neural Networks. - Ben Anson; Edward Milsom; Laurence Aitchison (Trans. Mach. Learn. Res., 2025)

Sparse Autoencoders Can Interpret Randomly Initialized Transformers. - Thomas Heap; Tim Lawson; Lucy Farnik; Laurence Aitchison (CoRR, 2025)

Function-Space Learning Rates. - Edward Milsom; Ben Anson; Laurence Aitchison (CoRR, 2025)

Jacobian Sparse Autoencoders: Sparsify Computations, Not Just Activations. - Lucy Farnik; Tim Lawson; Conor Houghton; Laurence Aitchison (CoRR, 2025)

Position: Don't use the CLT in LLM evals with fewer than a few hundred datapoints. - Sam Bowyer; Laurence Aitchison; Desi R. Ivanova (CoRR, 2025)

A Self-Improving Coding Agent. - Maxime Robeyns; Martin Szummer; Laurence Aitchison (CoRR, 2025)

Scale-invariant Attention. - Ben Anson; Xi Wang; Laurence Aitchison (CoRR, 2025)

Improving LLM-Generated Code Quality with GRPO. - Maxime Robeyns; Laurence Aitchison (CoRR, 2025)

Learning to Skip the Middle Layers of Transformers. - Tim Lawson; Laurence Aitchison (CoRR, 2025)