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ORCIDLaurence Aitchison
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- affiliation: University of Bristol, UK
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2020 – today
- 2024
[j7]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): 5719-5726 (2024)- [j6]Laurence Aitchison, Stoil Ganev:
InfoNCE is variational inference in a recognition parameterised model. Trans. Mach. Learn. Res. 2024 (2024) - [c23]Edward Milsom, Ben Anson, Laurence Aitchison:
Convolutional Deep Kernel Machines. ICLR 2024 - [c22]Adam X. Yang, Maxime Robeyns, Xi Wang, Laurence Aitchison:
Bayesian Low-rank Adaptation for Large Language Models. ICLR 2024 - [c21]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: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [i46]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 abs/2402.00809 (2024) - [i45]Ben Anson, Edward Milsom, Laurence Aitchison:
Flexible infinite-width graph convolutional networks and the importance of representation learning. CoRR abs/2402.06525 (2024) - [i44]Adam X. Yang, Maxime Robeyns, Thomas Coste, Jun Wang, Haitham Bou-Ammar, Laurence Aitchison:
Bayesian Reward Models for LLM Alignment. CoRR abs/2402.13210 (2024) - [i43]Xi Wang, Laurence Aitchison:
Batch size invariant Adam. CoRR abs/2402.18824 (2024) - [i42]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 abs/2403.20275 (2024) - [i41]Xi Wang, Laurence Aitchison:
How to set AdamW's weight decay as you scale model and dataset size. CoRR abs/2405.13698 (2024) - [i40]Zhengyan Shi, Adam X. Yang, Bin Wu, Laurence Aitchison, Emine Yilmaz, Aldo Lipani:
Instruction Tuning With Loss Over Instructions. CoRR abs/2405.14394 (2024) - [i39]Jack R. P. Hanslope, Laurence Aitchison:
Using Neural Networks for Data Cleaning in Weather Datasets. CoRR abs/2406.15027 (2024) - [i38]Gavin Leech, Juan J. Vazquez, Misha Yagudin, Niclas Kupper, Laurence Aitchison:
Questionable practices in machine learning. CoRR abs/2407.12220 (2024) - [i37]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 abs/2407.14158 (2024) - [i36]Tim Lawson, Lucy Farnik, Conor Houghton, Laurence Aitchison:
Residual Stream Analysis with Multi-Layer SAEs. CoRR abs/2409.04185 (2024) - [i35]Edward Milsom, Ben Anson, Laurence Aitchison:
Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines. CoRR abs/2410.06171 (2024) - [i34]Zihong He, Weizhe Lin, Hao Zheng, Fan Zhang, Matt Jones, Laurence Aitchison, Xuhai Xu, Miao Liu, Per Ola Kristensson, Junxiao Shen:
Human-inspired Perspectives: A Survey on AI Long-term Memory. CoRR abs/2411.00489 (2024) - 2023
- [c20]Stoil Ganev, Laurence Aitchison:
Semi-supervised learning with a principled likelihood from a generative model of data curation. ICLR 2023 - [c19]Xi Wang, Laurence Aitchison:
Robustness to corruption in pre-trained Bayesian neural networks. ICLR 2023 - [c18]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: 39380-39415 - [c17]Michele Garibbo, Maxime Robeyns, Laurence Aitchison:
Taylor TD-learning. NeurIPS 2023 - [c16]Thomas Heap, Gavin Leech, Laurence Aitchison:
Massively parallel reweighted wake-sleep. UAI 2023: 870-878 - [c15]Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison:
An improved variational approximate posterior for the deep Wishart process. UAI 2023: 1555-1563 - [i33]Jack R. P. Hanslope, Laurence Aitchison:
Imitating careful experts to avoid catastrophic events. CoRR abs/2302.01193 (2023) - [i32]Hugh Panton, Gavin Leech, Laurence Aitchison:
Decision trees compensate for model misspecification. CoRR abs/2302.04081 (2023) - [i31]Adam X. Yang, Laurence Aitchison, Henry B. Moss:
MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning. CoRR abs/2302.11533 (2023) - [i30]Michele Garibbo, Maxime Robeyns, Laurence Aitchison:
Taylor TD-learning. CoRR abs/2302.14182 (2023) - [i29]Thomas Heap, Gavin Leech, Laurence Aitchison:
Massively Parallel Reweighted Wake-Sleep. CoRR abs/2305.11022 (2023) - [i28]Sebastian W. Ober, Ben Anson, Edward Milsom, Laurence Aitchison:
An Improved Variational Approximate Posterior for the Deep Wishart Process. CoRR abs/2305.14454 (2023) - [i27]Adam X. Yang, Maxime Robeyns, Xi Wang, Laurence Aitchison:
Bayesian low-rank adaptation for large language models. CoRR abs/2308.13111 (2023) - [i26]Edward Milsom, Ben Anson, Laurence Aitchison:
Convolutional Deep Kernel Machines. CoRR abs/2309.09814 (2023) - [i25]Xi Wang, Laurence Aitchison, Maja Rudolph:
LoRA ensembles for large language model fine-tuning. CoRR abs/2310.00035 (2023) - [i24]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 abs/2311.12602 (2023) - 2022
- [c14]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 - [c13]Pernilla Craig, Laurence Aitchison, Nathan F. Lepora:
Active Inference for Artificial Touch: A Biologically-Plausible Tactile Control Method. Living Machines 2022: 169-181 - [c12]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: 1434-1444 - [i23]Xi Wang, Laurence Aitchison:
Out of distribution robustness with pre-trained Bayesian neural networks. CoRR abs/2206.12361 (2022) - [i22]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 abs/2208.10892 (2022) - [i21]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 abs/2209.07509 (2022) - [i20]Henry Addison, Elizabeth Kendon, Suman V. Ravuri, Laurence Aitchison, Peter A. G. Watson:
Machine learning emulation of a local-scale UK climate model. CoRR abs/2211.16116 (2022) - 2021
- [j5]Ali Unlu, Laurence Aitchison:
Gradient Regularization as Approximate Variational Inference. Entropy 23(12): 1629 (2021) - [j4]Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison:
BNNpriors: A library for Bayesian neural network inference with different prior distributions. Softw. Impacts 9: 100079 (2021) - [c11]Anupam K. Gupta, Laurence Aitchison, Nathan F. Lepora:
Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear. CoRL 2021: 14-23 - [c10]Laurence Aitchison:
A statistical theory of cold posteriors in deep neural networks. ICLR 2021 - [c9]Laurence Aitchison, Adam X. Yang, Sebastian W. Ober:
Deep Kernel Processes. ICML 2021: 130-140 - [c8]Sebastian W. Ober, Laurence Aitchison:
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes. ICML 2021: 8248-8259 - [c7]Sebastian W. Ober, Laurence Aitchison:
A variational approximate posterior for the deep Wishart process. NeurIPS 2021: 6567-6579 - [i19]Vincent Fortuin, Adrià Garriga-Alonso, Florian Wenzel, Gunnar Rätsch, Richard E. Turner, Mark van der Wilk, Laurence Aitchison:
Bayesian Neural Network Priors Revisited. CoRR abs/2102.06571 (2021) - [i18]Xi Wang, Laurence Aitchison:
A statistical theory of out-of-distribution detection. CoRR abs/2102.12959 (2021) - [i17]Vincent Fortuin, Adrià Garriga-Alonso, Mark van der Wilk, Laurence Aitchison:
BNNpriors: A library for Bayesian neural network inference with different prior distributions. CoRR abs/2105.06964 (2021) - [i16]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 abs/2106.05586 (2021) - [i15]Laurence Aitchison:
InfoNCE is a variational autoencoder. CoRR abs/2107.02495 (2021) - [i14]Sebastian W. Ober, Laurence Aitchison:
A variational approximate posterior for the deep Wishart process. CoRR abs/2107.10125 (2021) - [i13]Laurence Aitchison:
A fast point solver for deep nonlinear function approximators. CoRR abs/2108.13097 (2021) - [i12]Anupam K. Gupta, Laurence Aitchison, Nathan F. Lepora:
Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear. CoRR abs/2109.03615 (2021) - 2020
- [c6]Laurence Aitchison:
Why bigger is not always better: on finite and infinite neural networks. ICML 2020: 156-164 - [c5]Laurence Aitchison:
Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods. NeurIPS 2020 - [i11]Sebastian W. Ober, Laurence Aitchison:
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes. CoRR abs/2005.08140 (2020) - [i10]Laurence Aitchison:
A statistical theory of cold posteriors in deep neural networks. CoRR abs/2008.05912 (2020) - [i9]Laurence Aitchison:
A statistical theory of semi-supervised learning. CoRR abs/2008.05913 (2020) - [i8]Dylan Holden-Sim, Gavin Leech, Laurence Aitchison:
Legally grounded fairness objectives. CoRR abs/2009.11677 (2020) - [i7]Laurence Aitchison, Adam X. Yang, Sebastian W. Ober:
Deep kernel processes. CoRR abs/2010.01590 (2020) - [i6]Ali Unlu, Laurence Aitchison:
Gradient Regularisation as Approximate Variational Inference. CoRR abs/2011.10443 (2020)
2010 – 2019
- 2019
- [c4]Adrià Garriga-Alonso, Carl Edward Rasmussen, Laurence Aitchison:
Deep Convolutional Networks as shallow Gaussian Processes. ICLR (Poster) 2019 - [c3]Laurence Aitchison:
Tensor Monte Carlo: Particle Methods for the GPU era. NeurIPS 2019: 7146-7155 - [i5]Laurence Aitchison:
Why bigger is not always better: on finite and infinite neural networks. CoRR abs/1910.08013 (2019) - 2018
- [i4]Laurence Aitchison, Vincent Adam, Srinivas C. Turaga:
Discrete flow posteriors for variational inference in discrete dynamical systems. CoRR abs/1805.10958 (2018) - [i3]Laurence Aitchison:
Tensor Monte Carlo: particle methods for the GPU era. CoRR abs/1806.08593 (2018) - [i2]Laurence Aitchison:
A unified theory of adaptive stochastic gradient descent as Bayesian filtering. CoRR abs/1807.07540 (2018) - [i1]Adrià Garriga-Alonso, Laurence Aitchison, Carl Edward Rasmussen:
Deep Convolutional Networks as shallow Gaussian Processes. CoRR abs/1808.05587 (2018) - 2017
- [c2]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: 3486-3495 - 2016
- [j3]Laurence Aitchison, Nicola Corradi, Peter E. Latham:
Zipf's Law Arises Naturally When There Are Underlying, Unobserved Variables. PLoS Comput. Biol. 12(12) (2016) - [j2]Laurence Aitchison, Máté Lengyel:
The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics. PLoS Comput. Biol. 12(12) (2016) - 2015
- [j1]Laurence Aitchison, Dan Bang, Bahador Bahrami, Peter E. Latham:
Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making. PLoS Comput. Biol. 11(10) (2015) - 2014
- [c1]Guillaume Hennequin, Laurence Aitchison, Máté Lengyel:
Fast Sampling-Based Inference in Balanced Neuronal Networks. NIPS 2014: 2240-2248
Coauthor Index
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last updated on 2024-12-11 20:46 CET by the dblp team
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