"posterior sampling" Papers
28 papers found
Conference
$\Psi$-Sampler: Initial Particle Sampling for SMC-Based Inference-Time Reward Alignment in Score Models
Taehoon Yoon, Yunhong Min, Kyeongmin Yeo et al.
A Data-Driven Prism: Multi-View Source Separation with Diffusion Model Priors
Sebastian Wagner-Carena, Aizhan Akhmetzhanova, Sydney Erickson
Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data
Asad Aali, Giannis Daras, Brett Levac et al.
Constrained Posterior Sampling: Time Series Generation with Hard Constraints
Sai Shankar Narasimhan, Shubhankar Agarwal, Litu Rout et al.
Geometry Meets Incentives: Sample-Efficient Incentivized Exploration with Linear Contexts
Ben Schiffer, Mark Sellke
Guided Diffusion Sampling on Function Spaces with Applications to PDEs
Jiachen Yao, Abbas Mammadov, Julius Berner et al.
Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing
Bingliang Zhang, Wenda Chu, Julius Berner et al.
Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian Neural Networks
Emanuel Sommer, Jakob Robnik, Giorgi Nozadze et al.
Multitask Learning with Stochastic Interpolants
Hugo Negrel, Florentin Coeurdoux, Michael Albergo et al.
PostEdit: Posterior Sampling for Efficient Zero-Shot Image Editing
Feng Tian, Yixuan Li, Yichao Yan et al.
Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior
Tongda Xu, Xiyan Cai, Xinjie Zhang et al.
Self-diffusion for Solving Inverse Problems
Guanxiong Luo, Shoujin Huang
Split Gibbs Discrete Diffusion Posterior Sampling
Wenda Chu, Zihui Wu, Yifan Chen et al.
Diffusion Posterior Sampling is Computationally Intractable
Shivam Gupta, Ajil Jalal, Aditya Parulekar et al.
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse Problems
Sojin Lee, Dogyun Park, Inho Kong et al.
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior Sampling
Danil Provodin, Maurits Kaptein, Mykola Pechenizkiy
Embarrassingly Parallel GFlowNets
Tiago Silva, Luiz Carvalho, Amauri Souza et al.
Exact Inference for Continuous-Time Gaussian Process Dynamics
Katharina Ensinger, Nicholas Tagliapietra, Sebastian Ziesche et al.
Feel-Good Thompson Sampling for Contextual Dueling Bandits
Xuheng Li, Heyang Zhao, Quanquan Gu
Listening to the noise: Blind Denoising with Gibbs Diffusion
David Heurtel-Depeiges, Charles Margossian, Ruben Ohana et al.
Meta-Reinforcement Learning Robust to Distributional Shift Via Performing Lifelong In-Context Learning
TengYe Xu, Zihao Li, Qinyuan Ren
Osmosis: RGBD Diffusion Prior for Underwater Image Restoration
Opher Bar Nathan, Deborah Steinberger-Levy, Tali Treibitz et al.
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
Shion Takeno, Yu Inatsu, Masayuki Karasuyama et al.
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent
Yingru Li, Jiawei Xu, Lei Han et al.
Stochastic Localization via Iterative Posterior Sampling
Louis Grenioux, Maxence Noble, Marylou Gabrié et al.
Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Prediction
Jeffrey Wen, Rizwan Ahmad, Phillip Schniter
The Perception-Robustness Tradeoff in Deterministic Image Restoration
Guy Ohayon, Tomer Michaeli, Michael Elad
Zero-Shot Adaptation for Approximate Posterior Sampling of Diffusion Models in Inverse Problems
Yasar Utku Alcalar, Mehmet Akcakaya