Laurence Aitchison

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 71

Top Topics

Gaussian Process

Variational Inference

Top Venues

Publications

Adam X. Yang, Maxime Robeyns, Xi Wang, Laurence Aitchison
Bayesian Low-rank Adaptation for Large Language Models. ICLR (2024)
Xi Wang, Laurence Aitchison
Batch size invariant Adam. CoRR (2024)
Xi Wang, Laurence Aitchison
How to set AdamW's weight decay as you scale model and dataset size. CoRR (2024)
Mauro Comi, Alessio Tonioni, Max Yang, Jonathan Tremblay, Valts Blukis, Yijiong Lin, Nathan F. Lepora, Laurence Aitchison
Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces. CoRR (2024)
Laurence Aitchison, Stoil Ganev
InfoNCE is variational inference in a recognition parameterised model. Trans. Mach. Learn. Res. 2024 (2024)
Ben Anson, Edward Milsom, Laurence Aitchison
Flexible infinite-width graph convolutional networks and the importance of representation learning. CoRR (2024)
Zhengyan Shi, Adam X. Yang, Bin Wu, Laurence Aitchison, Emine Yilmaz, Aldo Lipani
Instruction Tuning With Loss Over Instructions. CoRR (2024)
Mauro Comi, Yijiong Lin, Alex Church, Alessio Tonioni, Laurence Aitchison, Nathan F. Lepora
TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction Using Vision-Based Tactile Sensing. IEEE Robotics Autom. Lett. 9 (6) (2024)
Jack R. P. Hanslope, Laurence Aitchison
Using Neural Networks for Data Cleaning in Weather Datasets. CoRR (2024)
Adam X. Yang, Maxime Robeyns, Thomas Coste, Jun Wang, Haitham Bou-Ammar, Laurence Aitchison
Bayesian Reward Models for LLM Alignment. CoRR (2024)
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
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR (2024)
Edward Milsom, Ben Anson, Laurence Aitchison
Convolutional Deep Kernel Machines. ICLR (2024)
Henry Addison, Elizabeth Kendon, Suman V. Ravuri, Laurence Aitchison, Peter A. G. Watson
Machine learning emulation of precipitation from km-scale regional climate simulations using a diffusion model. CoRR (2024)
Gavin Leech, Juan J. Vazquez, Misha Yagudin, Niclas Kupper, Laurence Aitchison
Questionable practices in machine learning. CoRR (2024)
Thomas Heap, Gavin Leech, Laurence Aitchison
Massively Parallel Reweighted Wake-Sleep. CoRR (2023)
Michele Garibbo, Maxime Robeyns, Laurence Aitchison
Taylor TD-learning. CoRR (2023)
Edward Milsom, Ben Anson, Laurence Aitchison
Convolutional Deep Kernel Machines. CoRR (2023)
Stoil Ganev, Laurence Aitchison
Semi-supervised learning with a principled likelihood from a generative model of data curation. ICLR (2023)
Adam X. Yang, Maxime Robeyns, Edward Milsom, Ben Anson, Nandi Schoots, Laurence Aitchison
A theory of representation learning gives a deep generalisation of kernel methods. ICML (2023)
Mauro Comi, Yijiong Lin, Alex Church, Alessio Tonioni, Laurence Aitchison, Nathan F. Lepora
TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction using Vision-Based Tactile Sensing. CoRR (2023)
Hugh Panton, Gavin Leech, Laurence Aitchison
Decision trees compensate for model misspecification. CoRR (2023)
Xi Wang, Laurence Aitchison, Maja Rudolph
LoRA ensembles for large language model fine-tuning. CoRR (2023)
Jack R. P. Hanslope, Laurence Aitchison
Imitating careful experts to avoid catastrophic events. CoRR (2023)
Adam X. Yang, Maxime Robeyns, Xi Wang, Laurence Aitchison
Bayesian low-rank adaptation for large language models. CoRR (2023)
Thomas Heap, Gavin Leech, Laurence Aitchison
Massively parallel reweighted wake-sleep. UAI (2023)
Michele Garibbo, Maxime Robeyns, Laurence Aitchison
Taylor TD-learning. NeurIPS (2023)
Adam X. Yang, Laurence Aitchison, Henry B. Moss
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning. CoRR (2023)
Xi Wang, Laurence Aitchison
Robustness to corruption in pre-trained Bayesian neural networks. ICLR (2023)
Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison
An improved variational approximate posterior for the deep Wishart process. UAI (2023)
Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison
An Improved Variational Approximate Posterior for the Deep Wishart Process. CoRR (2023)
Xi Wang, Laurence Aitchison
Out of distribution robustness with pre-trained Bayesian neural networks. CoRR (2022)
Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison
Data augmentation in Bayesian neural networks and the cold posterior effect. UAI (2022)
Vincent Fortuin, Adrià Garriga-Alonso, Sebastian W. Ober, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
Bayesian Neural Network Priors Revisited. ICLR (2022)
Henry Addison, Elizabeth Kendon, Suman V. Ravuri, Laurence Aitchison, Peter A. G. Watson
Machine learning emulation of a local-scale UK climate model. CoRR (2022)
Michele Garibbo, Casimir J. H. Ludwig, Nathan F. Lepora, Laurence Aitchison
What deep reinforcement learning tells us about human motor learning and vice-versa. CoRR (2022)
Frederik Benzing, Simon Schug, Robert Meier, Johannes von Oswald, Yassir Akram, Nicolas Zucchet, Laurence Aitchison, Angelika Steger
Random initialisations performing above chance and how to find them. CoRR (2022)
Pernilla Craig, Laurence Aitchison, Nathan F. Lepora
Active Inference for Artificial Touch: A Biologically-Plausible Tactile Control Method. Living Machines (2022)
Ali Unlu, Laurence Aitchison
Variational Laplace for Bayesian neural networks. CoRR (2021)
Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison
Bayesian Neural Network Priors Revisited. CoRR (2021)
Laurence Aitchison, Adam X. Yang, Sebastian W. Ober
Deep Kernel Processes. ICML (2021)
Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison
BNNpriors: A library for Bayesian neural network inference with different prior distributions. CoRR (2021)
Sebastian W. Ober, Laurence Aitchison
A variational approximate posterior for the deep Wishart process. CoRR (2021)
Anupam K. Gupta, Laurence Aitchison, Nathan F. Lepora
Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear. CoRL (2021)
Xi Wang, Laurence Aitchison
A statistical theory of out-of-distribution detection. CoRR (2021)
Laurence Aitchison
InfoNCE is a variational autoencoder. CoRR (2021)
Laurence Aitchison
A fast point solver for deep nonlinear function approximators. CoRR (2021)
Seth Nabarro, Stoil Ganev, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, Laurence Aitchison
Data augmentation in Bayesian neural networks and the cold posterior effect. CoRR (2021)
Laurence Aitchison
A statistical theory of cold posteriors in deep neural networks. ICLR (2021)
Sebastian W. Ober, Laurence Aitchison
A variational approximate posterior for the deep Wishart process. NeurIPS (2021)
Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison
: A library for Bayesian neural network inference with different prior distributions. Softw. Impacts 9 (2021)
Sebastian W. Ober, Laurence Aitchison
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes. ICML (2021)
Ali Unlu, Laurence Aitchison
Gradient Regularization as Approximate Variational Inference. Entropy 23 (12) (2021)
Anupam K. Gupta, Laurence Aitchison, Nathan F. Lepora
Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear. CoRR (2021)
Sebastian W. Ober, Laurence Aitchison
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes. CoRR (2020)
Dylan Holden-Sim, Gavin Leech, Laurence Aitchison
Legally grounded fairness objectives. CoRR (2020)
Laurence Aitchison
A statistical theory of semi-supervised learning. CoRR (2020)
Laurence Aitchison
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods. NeurIPS (2020)
Laurence Aitchison
A statistical theory of cold posteriors in deep neural networks. CoRR (2020)
Laurence Aitchison, Adam X. Yang, Sebastian W. Ober
Deep kernel processes. CoRR (2020)
Laurence Aitchison
Why bigger is not always better: on finite and infinite neural networks. ICML (2020)
Ali Unlu, Laurence Aitchison
Gradient Regularisation as Approximate Variational Inference. CoRR (2020)
Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison
Deep Convolutional Networks as shallow Gaussian Processes. ICLR (Poster) (2019)
Laurence Aitchison
Tensor Monte Carlo: Particle Methods for the GPU era. NeurIPS (2019)
Laurence Aitchison
Why bigger is not always better: on finite and infinite neural networks. CoRR (2019)
Laurence Aitchison
Tensor Monte Carlo: particle methods for the GPU era. CoRR (2018)
Laurence Aitchison, Vincent Adam, Srinivas C. Turaga
Discrete flow posteriors for variational inference in discrete dynamical systems. CoRR (2018)
Adrià Garriga-Alonso, Laurence Aitchison, Carl Edward Rasmussen
Deep Convolutional Networks as shallow Gaussian Processes. CoRR (2018)
Laurence Aitchison
A unified theory of adaptive stochastic gradient descent as Bayesian filtering. CoRR (2018)
Laurence Aitchison, Lloyd Russell, Adam M. Packer, Jinyao Yan, Philippe Castonguay, Michael Häusser, Srinivas C. Turaga
Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit. NIPS (2017)
Laurence Aitchison, Nicola Corradi, Peter E. Latham
Zipf's Law Arises Naturally When There Are Underlying, Unobserved Variables. PLoS Comput. Biol. 12 (12) (2016)
Laurence Aitchison, Máté Lengyel
The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics. PLoS Comput. Biol. 12 (12) (2016)
Laurence Aitchison, Dan Bang, Bahador Bahrami, Peter E. Latham
Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making. PLoS Comput. Biol. 11 (10) (2015)
Guillaume Hennequin, Laurence Aitchison, Máté Lengyel
Fast Sampling-Based Inference in Balanced Neuronal Networks. NIPS (2014)