Publications
Conferences
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
Shreyas Padhy*, Jihao Andreas Lin*, Javier Antorán*, David Janz, José
Miguel Hernández-Lobato, Alexander Terenin.
NeurIPS 2023 (Oral)
[code] [paper]
Sampling-based inference for large linear models, with application to linearised
Laplace
Shreyas Padhy*, Javier Antorán*, Riccardo Barbano, Eric Nalisnick,
David Janz, José Miguel Hernández-Lobato
ICLR 2023
[code] [paper] [slides] [poster]
Simple and principled uncertainty estimation with deterministic deep learning via
distance awareness
Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania
Bedrax-Weiss, and Balaji Lakshminarayanan.
NeurIPS 2020
[code]
[paper]
Preprints
Transport meets Variational Inference: Controlled Monte Carlo Diffusions
Francisco Vargas*, Shreyas Padhy*, Denis Blessing, Nikolas Nüsken.
[paper]
Stochastic Gradient Descent for Gaussian Processes Done Right
Shreyas Padhy*, Jihao Andreas Lin*, Javier Antorán*, Austin Tripp,
Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
[paper]
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples
Ben Adlam, Jaehoon Lee, Shreyas Padhy, Zachary Nado, Jasper Snoek.
[paper]
Journals
A Simple Approach to Improve Single-Model Deep Uncertainty via
Distance-Awareness
Jeremiah Zhe Liu*, Shreyas Padhy*, Jie Ren*, Zi Lin, Yeming Wen,
Ghassen Jerfel, Zack Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan
JMLR 2023
[code]
[paper] [poster]
Using deep Siamese neural networks for detection of brain asymmetries associated with
Alzheimer's Disease and Mild Cognitive Impairment
C.F. Liu*, Shreyas Padhy*, S. Ramachandran, V.X. Wang, A. Efimov, A.
Bernal, L. Shi, M. Vaillant, J.T Ratnanather, A.V Faria, B. Caffo, M. Albert, M.I
Miller.
Magnetic
resonance imaging 64 (2019): 190-199., 2019.
[paper]
Stochastic Solutions to Rough Surface Scattering using the finite element
method
Uday K. Khankhoje, Shreyas Padhy.
IEEE Transactions on Antennas and Propagation, (Vol 65, No 08), 2017.
[website] [paper]
Workshops
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution
Detection in Neural
Networks
Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Liu, Jasper Snoek, and
Balaji Lakshminarayanan.
ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning.
[code]
[paper]
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
Jie Ren, Stanislav Fort, Jeremiah Liu, Abhijit Guha Roy, Shreyas Padhy,
and Balaji Lakshminarayanan
ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning.
[paper]
Evaluating prediction-time batch normalization for robustness under covariate
shift
Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji
Lakshminarayanan, and Jasper Snoek.
ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning.
[code]
[paper]
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep
Learning
Zachary Nado at al.
Bayesian Deep Learning Workshop, 2021.
[code] [paper]
Graduate Thesis
Analyzing shape and residual pose of subcortical structures in brains of subjects
with schizophrenia
Shreyas Padhy
Masters Thesis, Master of Science in Engineering, Department of Biomedical
Engineering, Johns Hopkins University.
[paper]
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