Laurence Aitchison

According to our database, Laurence Aitchison authored at least 51 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces.

Mauro Comi, Alessio Tonioni, Max Yang, Jonathan Tremblay, Valts Blukis, Yijiong Lin, Nathan F. Lepora, Laurence Aitchison

CoRR, 2024

Batch size invariant Adam.

Xi Wang, Laurence Aitchison

CoRR, 2024

Bayesian Reward Models for LLM Alignment.

Adam X. Yang, Maxime Robeyns, Thomas Coste, Jun Wang, Haitham Bou-Ammar, Laurence Aitchison

CoRR, 2024

Flexible infinite-width graph convolutional networks and the importance of representation learning.

Ben Anson, Edward Milsom, Laurence Aitchison

CoRR, 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, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

CoRR, 2024

2023

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

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

CoRR, 2023

Bayesian low-rank adaptation for large language models.

Adam X. Yang, Maxime Robeyns, Xi Wang, Laurence Aitchison

CoRR, 2023

MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning.

Adam X. Yang, Laurence Aitchison, Henry B. Moss

CoRR, 2023

Decision trees compensate for model misspecification.

Hugh Panton, Gavin Leech, Laurence Aitchison

CoRR, 2023

Imitating careful experts to avoid catastrophic events.

Jack R. P. Hanslope, Laurence Aitchison

CoRR, 2023

An improved variational approximate posterior for the deep Wishart process.

Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Massively parallel reweighted wake-sleep.

Thomas Heap, Gavin Leech, Laurence Aitchison

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Taylor TD-learning.

Michele Garibbo, Maxime Robeyns, Laurence Aitchison

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the International Conference on Machine Learning, 2023

Robustness to corruption in pre-trained Bayesian neural networks.

Xi Wang, Laurence Aitchison

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Semi-supervised learning with a principled likelihood from a generative model of data curation.

Stoil Ganev, Laurence Aitchison

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

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

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

Out of distribution robustness with pre-trained Bayesian neural networks.

Xi Wang, Laurence Aitchison

CoRR, 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

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Active Inference for Artificial Touch: A Biologically-Plausible Tactile Control Method.

Pernilla Craig, Laurence Aitchison, Nathan F. Lepora

Proceedings of the Biomimetic and Biohybrid Systems - 11th International Conference, 2022

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

<i>BNNpriors</i>: 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

Gradient Regularization as Approximate Variational Inference.

Ali Unlu, Laurence Aitchison

Entropy, 2021

A fast point solver for deep nonlinear function approximators.

Laurence Aitchison

CoRR, 2021

InfoNCE is a variational autoencoder.

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

A statistical theory of out-of-distribution detection.

Xi Wang, Laurence Aitchison

CoRR, 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 variational approximate posterior for the deep Wishart process.

Sebastian W. Ober, Laurence Aitchison

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes.

Sebastian W. Ober, Laurence Aitchison

Proceedings of the 38th International Conference on Machine Learning, 2021

Deep Kernel Processes.

Laurence Aitchison, Adam X. Yang, Sebastian W. Ober

Proceedings of the 38th International Conference on Machine Learning, 2021

A statistical theory of cold posteriors in deep neural networks.

Laurence Aitchison

Proceedings of the 9th International Conference on Learning Representations, 2021

Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear.

Anupam K. Gupta, Laurence Aitchison, Nathan F. Lepora

Proceedings of the Conference on Robot Learning, 8-11 November 2021, London, UK., 2021

2020

Gradient Regularisation as Approximate Variational Inference.

Ali Unlu, Laurence Aitchison

CoRR, 2020

Legally grounded fairness objectives.

Dylan Holden-Sim, Gavin Leech, Laurence Aitchison

CoRR, 2020

A statistical theory of semi-supervised learning.

Laurence Aitchison

CoRR, 2020

Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods.

Laurence Aitchison

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Why bigger is not always better: on finite and infinite neural networks.

Laurence Aitchison

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Tensor Monte Carlo: Particle Methods for the GPU era.

Laurence Aitchison

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Deep Convolutional Networks as shallow Gaussian Processes.

Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

A unified theory of adaptive stochastic gradient descent as Bayesian filtering.

Laurence Aitchison

CoRR, 2018

Discrete flow posteriors for variational inference in discrete dynamical systems.

Laurence Aitchison, Vincent Adam, Srinivas C. Turaga

CoRR, 2018

2017

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

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics.

Laurence Aitchison, Máté Lengyel

PLoS Comput. Biol., 2016

Zipf's Law Arises Naturally When There Are Underlying, Unobserved Variables.

Laurence Aitchison, Nicola Corradi, Peter E. Latham

PLoS Comput. Biol., 2016

2015

Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making.

Laurence Aitchison, Dan Bang, Bahador Bahrami, Peter E. Latham

PLoS Comput. Biol., 2015

2014

Fast Sampling-Based Inference in Balanced Neuronal Networks.

Guillaume Hennequin, Laurence Aitchison, Máté Lengyel

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014