Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. - Jeremiah Z. Liu; Zi Lin; Shreyas Padhy; Dustin Tran; Tania Bedrax-Weiss; Balaji Lakshminarayanan (NeurIPS, 2020)

Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift. - Zachary Nado; Shreyas Padhy; D. Sculley; Alexander D'Amour; Balaji Lakshminarayanan; Jasper Snoek (CoRR, 2020)

Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks. - Shreyas Padhy; Zachary Nado; Jie Ren 006; Jeremiah Z. Liu; Jasper Snoek; Balaji Lakshminarayanan (CoRR, 2020)

Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. - 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; et al. (CoRR, 2021)

A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. - Jie Ren 006; Stanislav Fort; Jeremiah Z. Liu; Abhijit Guha Roy; Shreyas Padhy; Balaji Lakshminarayanan (CoRR, 2021)

Sampling-based inference for large linear models, with application to linearised Laplace. - Javier Antorán; Shreyas Padhy; Riccardo Barbano; Eric T. Nalisnick; David Janz; José Miguel Hernández-Lobato (ICLR, 2023)

Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. - Jihao Andreas Lin; Javier Antorán; Shreyas Padhy; David Janz; José Miguel Hernández-Lobato; Alexander Terenin (NeurIPS, 2023)

Kernel Regression with Infinite-Width Neural Networks on Millions of Examples. - Ben Adlam; Jaehoon Lee 001; Shreyas Padhy; Zachary Nado; Jasper Snoek (CoRR, 2023)

A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. - Jeremiah Zhe Liu; Shreyas Padhy; Jie Ren 006; Zi Lin; Yeming Wen; Ghassen Jerfel; Zachary Nado; Jasper Snoek; Dustin Tran; Balaji Lakshminarayanan (J. Mach. Learn. Res., 2023)

Transport meets Variational Inference: Controlled Monte Carlo Diffusions. - Francisco Vargas 001; Shreyas Padhy; Denis Blessing; Nikolas Nüsken (ICLR, 2024)

Stochastic Gradient Descent for Gaussian Processes Done Right. - Jihao Andreas Lin; Shreyas Padhy; Javier Antorán; Austin Tripp; Alexander Terenin; Csaba Szepesvári; José Miguel Hernández-Lobato; David Janz (ICLR, 2024)

A Generative Model of Symmetry Transformations. - James Urquhart Allingham; Bruno Mlodozeniec; Shreyas Padhy; Javier Antorán; David Krueger 001; Richard E. Turner; Eric T. Nalisnick; José Miguel Hernández-Lobato (NeurIPS, 2024)

DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform. - Alexander Denker; Francisco Vargas 001; Shreyas Padhy; Kieran Didi; Simon V. Mathis; Riccardo Barbano; Vincent Dutordoir; Emile Mathieu; Urszula Julia Komorowska; Pietro Lió (NeurIPS, 2024)

Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. - Jihao Andreas Lin; Shreyas Padhy; Bruno Mlodozeniec; Javier Antorán; José Miguel Hernández-Lobato (NeurIPS, 2024)

Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes. - Jihao Andreas Lin; Shreyas Padhy; Bruno Mlodozeniec; José Miguel Hernández-Lobato (CoRR, 2024)

DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform. - Alexander Denker; Francisco Vargas 001; Shreyas Padhy; Kieran Didi; Simon V. Mathis; Vincent Dutordoir; Riccardo Barbano; Emile Mathieu; Urszula Julia Komorowska; Pietro Lio (CoRR, 2024)

Iterative Importance Fine-tuning of Diffusion Models. - Alexander Denker; Shreyas Padhy; Francisco Vargas 001; Johannes Hertrich (CoRR, 2025)

No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers. - Jiajun He 003; Yuanqi Du; Francisco Vargas 001; Dinghuai Zhang; Shreyas Padhy; Ruikang Ouyang; Carla P. Gomes; José Miguel Hernández-Lobato (CoRR, 2025)