conftrace_

posterior sampling

152 papers

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Also known as

PS

Co-occurring keywords

bayesian inference (1907) markov chain monte carlo (725) regret bound (1926) diffusion model (3751) langevin dynamics (128) inverse problem (240) generative model (2892) reinforcement learning (4352) thompson sampling (237) uncertainty quantification (1393)

Papers

Efficient Protein Optimization via Structure-aware Hamiltonian Dynamics AAAI 2026
Frequency-Guided Posterior Sampling for Diffusion-Based Image Restoration ICCV 2025
Graph-accelerated Markov Chain Monte Carlo using Approximate Samples JMLR 2025
Cache-Efficient Posterior Sampling for Reinforcement Learning with LLM-Derived Priors Across Discrete and Continuous Domains EMNLP 2025
LD-RPS: Zero-Shot Unified Image Restoration via Latent Diffusion Recurrent Posterior Sampling ICCV 2025
FlowDPS : Flow-Driven Posterior Sampling for Inverse Problems ICCV 2025
Stabilizing Holistic Semantics in Diffusion Bridge for Image Inpainting IJCAI 2025
Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing CVPR 2025
On Consistent Bayesian Inference from Synthetic Data JMLR 2025
Consistency Posterior Sampling for Diverse Image Synthesis CVPR 2025
Optimal Exploration is no harder than Thompson Sampling AISTATS 2024
Learning with Posterior Sampling for Revenue Management under Time-varying Demand IJCAI 2024
Molecule Design by Latent Prompt Transformer NIPS 2024
On Estimating the Gradient of the Expected Information Gain in Bayesian Experimental Design AAAI 2024
MDFL: Multi-Domain Diffusion-Driven Feature Learning AAAI 2024
Provable Posterior Sampling with Denoising Oracles via Tilted Transport NIPS 2024
Prior-dependent analysis of posterior sampling reinforcement learning with function approximation AISTATS 2024
Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction NIPS 2024
Learning Diffusion Priors from Observations by Expectation Maximization NIPS 2024
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint AISTATS 2024
Model-based Policy Optimization under Approximate Bayesian Inference AISTATS 2024
A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent AISTATS 2024
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry (Extended Abstract) IJCAI 2024
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo AISTATS 2024
A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors ICLR 2024