Shreyas Padhy
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Simple and principled uncertainty estimation with deterministic deep learning via distance awareness
J Liu, Z Lin, S Padhy, D Tran, T Bedrax Weiss, B Lakshminarayanan
Advances in neural information processing systems 33, 7498-7512, 2020
Evaluating prediction-time batch normalization for robustness under covariate shift
Z Nado, S Padhy, D Sculley, A D'Amour, B Lakshminarayanan, J Snoek
arXiv preprint arXiv:2006.10963, 2020
A simple fix to mahalanobis distance for improving near-ood detection
J Ren, S Fort, J Liu, AG Roy, S Padhy, B Lakshminarayanan
arXiv preprint arXiv:2106.09022, 2021
Uncertainty baselines: Benchmarks for uncertainty & robustness in deep learning
Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ...
arXiv preprint arXiv:2106.04015, 2021
Using deep Siamese neural networks for detection of brain asymmetries associated with Alzheimer's disease and mild cognitive impairment
CF Liu, S Padhy, S Ramachandran, VX Wang, A Efimov, A Bernal, L Shi, ...
Magnetic resonance imaging 64, 190-199, 2019
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks
S Padhy, Z Nado, J Ren, J Liu, J Snoek, B Lakshminarayanan
arXiv preprint arXiv:2007.05134, 2020
A simple approach to improve single-model deep uncertainty via distance-awareness
JZ Liu, S Padhy, J Ren, Z Lin, Y Wen, G Jerfel, Z Nado, J Snoek, D Tran, ...
Journal of Machine Learning Research 24 (42), 1-63, 2023
Stochastic solutions to rough surface scattering using the finite element method
UK Khankhoje, S Padhy
IEEE Transactions on Antennas and Propagation 65 (8), 4170-4180, 2017
Sampling-based inference for large linear models, with application to linearised Laplace
J Antorán, S Padhy, R Barbano, E Nalisnick, D Janz, ...
arXiv preprint arXiv:2210.04994, 2022
Sampling from Gaussian process posteriors using stochastic gradient descent
JA Lin, J Antorán, S Padhy, D Janz, JM Hernández-Lobato, A Terenin
Advances in Neural Information Processing Systems 36, 36886-36912, 2023
Transport meets Variational Inference: Controlled Monte Carlo Diffusions
F Vargas, S Padhy, B Denis, N Nüsken
arXiv preprint arXiv:2307.01050, 2023
Kernel regression with infinite-width neural networks on millions of examples
B Adlam, J Lee, S Padhy, Z Nado, J Snoek
arXiv preprint arXiv:2303.05420, 2023
Stochastic Gradient Descent for Gaussian Processes Done Right
JA Lin, S Padhy, J Antorán, A Tripp, A Terenin, C Szepesvári, ...
arXiv preprint arXiv:2310.20581, 2023
Learning generative models with invariance to symmetries
JU Allingham, J Antoran, S Padhy, E Nalisnick, JM Hernández-Lobato
NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022
DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised -transform
A Denker, F Vargas, S Padhy, K Didi, S Mathis, V Dutordoir, R Barbano, ...
arXiv preprint arXiv:2406.01781, 2024
Analyzing shape and residual pose of subcortical structures in brains of subjects with schizophrenia
S Padhy
Johns Hopkins University, 2019
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
JA Lin, S Padhy, B Mlodozeniec, J Antorán, JM Hernández-Lobato
arXiv preprint arXiv:2405.18457, 2024
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes
JA Lin, S Padhy, B Mlodozeniec, JM Hernández-Lobato
arXiv preprint arXiv:2405.18328, 2024
A Generative Model of Symmetry Transformations
JU Allingham, BK Mlodozeniec, S Padhy, J Antorán, D Krueger, ...
arXiv preprint arXiv:2403.01946, 2024
A Generative Model of Symmetry Transformations
J Urquhart Allingham, B Kacper Mlodozeniec, S Padhy, J Antorán, ...
arXiv e-prints, arXiv: 2403.01946, 2024
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