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ORCIDShreyas Padhy
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2020 – today
- 2024
[c5]Francisco Vargas, Shreyas Padhy, Denis Blessing, Nikolas Nüsken:
Transport meets Variational Inference: Controlled Monte Carlo Diffusions. ICLR 2024- [c4]Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz:
Stochastic Gradient Descent for Gaussian Processes Done Right. ICLR 2024 - [i14]James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato:
A Generative Model of Symmetry Transformations. CoRR abs/2403.01946 (2024) - [i13]Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, José Miguel Hernández-Lobato:
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes. CoRR abs/2405.18328 (2024) - [i12]Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato:
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. CoRR abs/2405.18457 (2024) - [i11]Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio:
DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform. CoRR abs/2406.01781 (2024) - 2023
- [j1]Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan:
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. J. Mach. Learn. Res. 24: 42:1-42:63 (2023) - [c3]Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato:
Sampling-based inference for large linear models, with application to linearised Laplace. ICLR 2023 - [c2]Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin:
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. NeurIPS 2023 - [i10]Ben Adlam, Jaehoon Lee, Shreyas Padhy, Zachary Nado, Jasper Snoek:
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples. CoRR abs/2303.05420 (2023) - [i9]Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin:
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. CoRR abs/2306.11589 (2023) - [i8]Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp
, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz:
Stochastic Gradient Descent for Gaussian Processes Done Right. CoRR abs/2310.20581 (2023) - 2022
- [i7]Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zack Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan:
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. CoRR abs/2205.00403 (2022) - [i6]Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato:
Sampling-based inference for large linear models, with application to linearised Laplace. CoRR abs/2210.04994 (2022) - 2021
- [i5]Zachary Nado, Neil Band, Mark Collier, Josip Djolonga, Michael W. Dusenberry, Sebastian Farquhar, Angelos Filos, Marton Havasi, Rodolphe Jenatton, Ghassen Jerfel, Jeremiah Z. Liu, Zelda Mariet, Jeremy Nixon, Shreyas Padhy, Jie Ren, Tim G. J. Rudner, Yeming Wen, Florian Wenzel, Kevin Murphy, D. Sculley, Balaji Lakshminarayanan, Jasper Snoek, Yarin Gal, Dustin Tran:
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR abs/2106.04015 (2021) - [i4]Jie Ren, Stanislav Fort, Jeremiah Z. Liu, Abhijit Guha Roy, Shreyas Padhy, Balaji Lakshminarayanan:
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. CoRR abs/2106.09022 (2021) - 2020
- [c1]Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan:
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. NeurIPS 2020 - [i3]Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan:
Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. CoRR abs/2006.10108 (2020) - [i2]Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek:
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift. CoRR abs/2006.10963 (2020) - [i1]Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Z. Liu, Jasper Snoek, Balaji Lakshminarayanan:
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks. CoRR abs/2007.05134 (2020)
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