Shreyas Padhy

Computer Science · Physical Sciences

9h-index622citations26works0.82yr avg
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Research Topics

Adversarial Robustness in Machine Learning(9), Gaussian Processes and Bayesian Inference(6), Machine Learning and Data Classification(5), Fault Detection and Control Systems(4), Anomaly Detection Techniques and Applications(3)

Publications26 total

No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers
arXiv (Cornell University)·2025· 2 citedOpen Access
Iterative Importance Fine-tuning of Diffusion Models
arXiv (Cornell University)·2025Open Access
DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised $h$-transform
arXiv (Cornell University)·2024· 2 citedOpen Access
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
arXiv (Cornell University)·2024Open Access
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes
arXiv (Cornell University)·2024Open Access
A Generative Model of Symmetry Transformations
arXiv (Cornell University)·2024· 3 citedOpen Access
DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform
2024
A Generative Model of Symmetry Transformations
2024
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
2024
Stochastic Gradient Descent for Gaussian Processes Done Right
arXiv (Cornell University)·2023· 1 citedOpen Access
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent
arXiv (Cornell University)·2023· 2 citedOpen Access
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples
arXiv (Cornell University)·2023· 1 citedOpen Access
Sampling-based inference for large linear models, with application to linearised Laplace
arXiv (Cornell University)·2022· 1 citedOpen Access
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
arXiv (Cornell University)·2022· 36 citedOpen Access
A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection
arXiv (Cornell University)·2021· 71 citedOpen Access
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
arXiv (Cornell University)·2021· 5 citedOpen Access
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
arXiv (Cornell University)·2021· 31 citedOpen Access
Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks
arXiv (Cornell University)·2020· 9 citedOpen Access
Revisiting One-vs-All Classifiers for Predictive Uncertainty and\n Out-of-Distribution Detection in Neural Networks
arXiv (Cornell University)·2020· 20 citedOpen Access
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift
arXiv (Cornell University)·2020· 95 citedOpen Access
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Frequent Co-authors

Balaji Lakshminarayanan(9), José Miguel Hernández-Lobato(9), Jeremiah Zhe Liu(7), Jasper Snoek(7), Javier Antorán(7), Zachary Nado(6), Dustin Tran(5), Bruno Mlodozeniec(5), Jihao Andreas Lin(5), Jie Ren(4), Francisco Vargas(4), Yeming Wen(3), Ghassen Jerfel(3), Eric Nalisnick(3), Alexander Denker(3), Riccardo Barbano(3), David M. Janz(3), Lin Zi(2), Tania Bedrax-Weiss(2), D. Sculley(2)