Padhy, Shreyas

12 publications

ICLRW 2025 Iterative Importance Fine-Tuning of Diffusion Models Alexander Denker, Shreyas Padhy, Francisco Vargas, Johannes Hertrich

ICLRW 2025 No Trick, No Treat: Pursuits and Challenges Towards Simulation-Free Training of Neural Samplers Jiajun He, Yuanqi Du, Francisco Vargas, Dinghuai Zhang, Shreyas Padhy, RuiKang OuYang, Carla P Gomes, José Miguel Hernández-Lobato

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

NeurIPS 2024 DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised $h$-Transform Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

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

ICLR 2024 Stochastic Gradient Descent for Gaussian Processes Done Right Jihao Andreas Lin, Shreyas Padhy, Javier Antoran, Austin Tripp, Alexander Terenin, Csaba Szepesvari, José Miguel Hernández-Lobato, David Janz

ICLR 2024 Transport Meets Variational Inference: Controlled Monte Carlo Diffusions Francisco Vargas, Shreyas Padhy, Denis Blessing, Nikolas Nüsken

JMLR 2023 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, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan

NeurIPS 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

ICLR 2023 Sampling-Based Inference for Large Linear Models, with Application to Linearised Laplace Javier Antoran, Shreyas Padhy, Riccardo Barbano, Eric Nalisnick, David Janz, José Miguel Hernández-Lobato

NeurIPSW 2022 Learning Generative Models with Invariance to Symmetries James Urquhart Allingham, Javier Antoran, Shreyas Padhy, Eric Nalisnick, José Miguel Hernández-Lobato

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