Shreyas Padhy - RankMe

25 papers A* 8Journal 17

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Iterative Importance Fine-tuning of Diffusion Models. CoRR Alexander Denker, Shreyas Padhy, Francisco Vargas, Johannes Hertrich
2025	J	`jnl`	No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers. CoRR Jiajun He, Yuanqi Du, Francisco Vargas, Dinghuai Zhang, Shreyas Padhy, Ruikang Ouyang, Carla P. Gomes, José Miguel Hernández-Lobato
2024	A*	`conf`	A Generative Model of Symmetry Transformations. NeurIPS James Urquhart Allingham, Bruno Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato
2024	J	`jnl`	A Generative Model of Symmetry Transformations. CoRR James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato
2024	A*	`conf`	DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform. NeurIPS Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Riccardo Barbano, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lió
2024	J	`jnl`	DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform. CoRR Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon V. Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio
2024	A*	`conf`	Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. NeurIPS Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato
2024	J	`jnl`	Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes. CoRR Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, Javier Antorán, José Miguel Hernández-Lobato
2024	A*	`conf`	Stochastic Gradient Descent for Gaussian Processes Done Right. ICLR Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
2024	A*	`conf`	Transport meets Variational Inference: Controlled Monte Carlo Diffusions. ICLR Francisco Vargas, Shreyas Padhy, Denis Blessing, Nikolas Nüsken
2024	J	`jnl`	Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes. CoRR Jihao Andreas Lin, Shreyas Padhy, Bruno Mlodozeniec, José Miguel Hernández-Lobato
2023	J	`jnl`	A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. J. Mach. Learn. Res. Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan
2023	J	`jnl`	Kernel Regression with Infinite-Width Neural Networks on Millions of Examples. CoRR Ben Adlam, Jaehoon Lee, Shreyas Padhy, Zachary Nado, Jasper Snoek
2023	A*	`conf`	Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. NeurIPS Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
2023	J	`jnl`	Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent. CoRR Jihao Andreas Lin, Javier Antorán, Shreyas Padhy, David Janz, José Miguel Hernández-Lobato, Alexander Terenin
2023	A*	`conf`	Sampling-based inference for large linear models, with application to linearised Laplace. ICLR Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
2023	J	`jnl`	Stochastic Gradient Descent for Gaussian Processes Done Right. CoRR Jihao Andreas Lin, Shreyas Padhy, Javier Antorán, Austin Tripp, Alexander Terenin, Csaba Szepesvári, José Miguel Hernández-Lobato, David Janz
2022	J	`jnl`	A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness. CoRR Jeremiah Zhe Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zack Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan
2022	J	`jnl`	Sampling-based inference for large linear models, with application to linearised Laplace. CoRR Javier Antorán, Shreyas Padhy, Riccardo Barbano, Eric T. Nalisnick, David Janz, José Miguel Hernández-Lobato
2021	J	`jnl`	A Simple Fix to Mahalanobis Distance for Improving Near-OOD Detection. CoRR Jie Ren, Stanislav Fort, Jeremiah Z. Liu, Abhijit Guha Roy, Shreyas Padhy, Balaji Lakshminarayanan
2021	J	`jnl`	Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning. CoRR 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
2020	J	`jnl`	Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift. CoRR Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek
2020	J	`jnl`	Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks. CoRR Shreyas Padhy, Zachary Nado, Jie Ren, Jeremiah Z. Liu, Jasper Snoek, Balaji Lakshminarayanan
2020	A*	`conf`	Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. NeurIPS Jeremiah Z. Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan
2020	J	`jnl`	Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness. CoRR Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan