Cover Image

Efficient Approximate Posterior Sampling with Annealed Langevin Monte Carlo

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Parulekar, Advait, Rout, Litu, Shanmugam, Karthikeyan, Shakkottai, Sanjay
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Constrained Posterior Sampling: Time Series Generation with Hard Constraints
by: Narasimhan, Sai Shankar, et al.
Published: (2024)

In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness
by: Collins, Liam, et al.
Published: (2024)

Entropy Aware Reward Guidance for Diffusion Language Model Alignment
by: Tejaswi, Atula, et al.
Published: (2026)

Diffusion-Based Posterior Sampling: A Feynman-Kac Analysis of Bias and Stability
by: Delgadino, Matias G., et al.
Published: (2026)

Anchored Diffusion Language Model
by: Rout, Litu, et al.
Published: (2025)

Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo
by: Zheng, Haoyang, et al.
Published: (2024)

Fine-Tuning Diffusion Models via Intermediate Distribution Shaping
by: Anil, Gautham Govind, et al.
Published: (2025)

Test-Time Anchoring for Discrete Diffusion Posterior Sampling
by: Rout, Litu, et al.
Published: (2025)

Posterior Sampling by Combining Diffusion Models with Annealed Langevin Dynamics
by: Xun, Zhiyang, et al.
Published: (2025)

AnCoder: Anchored Code Generation via Discrete Diffusion Models
by: Xue, Anton, et al.
Published: (2026)

Diffusion Posterior Sampling is Computationally Intractable
by: Gupta, Shivam, et al.
Published: (2024)

Bandits with Mean Bounds
by: Sharma, Nihal, et al.
Published: (2020)

Machine Unlearning under Overparameterization
by: Block, Jacob L., et al.
Published: (2025)

Fairness under Covariate Shift: Improving Fairness-Accuracy tradeoff with few Unlabeled Test Samples
by: Havaldar, Shreyas, et al.
Published: (2023)

Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective
by: Wang, Weixin, et al.
Published: (2025)

Bandits with Stochastic Experts: Constant Regret, Empirical Experts and Episodes
by: Sharma, Nihal, et al.
Published: (2021)

Learning from Label Proportions: Bootstrapping Supervised Learners via Belief Propagation
by: Havaldar, Shreyas, et al.
Published: (2023)

On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond
by: Nguyen-Tang, Thanh, et al.
Published: (2024)

Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations
by: Rout, Litu, et al.
Published: (2024)

Interleaved Gibbs Diffusion: Generating Discrete-Continuous Data with Implicit Constraints
by: Anil, Gautham Govind, et al.
Published: (2025)

Learning Solution Operators for Partial Differential Equations via Monte Carlo-Type Approximation
by: Choutri, Salah Eddine, et al.
Published: (2025)

Direct Soft-Policy Sampling via Langevin Dynamics
by: Ki, Donghyeon, et al.
Published: (2026)

Combinatorial Multi-armed Bandits: Arm Selection via Group Testing
by: Mukherjee, Arpan, et al.
Published: (2024)

DAPS++: Rethinking Diffusion Inverse Problems with Decoupled Posterior Annealing
by: Chen, Hao, et al.
Published: (2025)

Provable Benefit of Annealed Langevin Monte Carlo for Non-log-concave Sampling
by: Guo, Wei, et al.
Published: (2024)

LASER: An LLM-based ASR Scoring and Evaluation Rubric
by: Parulekar, Amruta, et al.
Published: (2025)

Annealing Optimization for Progressive Learning with Stochastic Approximation
by: Mavridis, Christos, et al.
Published: (2022)

Solving Diffusion Inverse Problems with Restart Posterior Sampling
by: Ahmed, Bilal, et al.
Published: (2025)

Accelerating Monte-Carlo Tree Search with Optimized Posterior Policies
by: Frankston, Keith, et al.
Published: (2026)

Sampling for Quality: Training-Free Reward-Guided LLM Decoding via Sequential Monte Carlo
by: Markovic-Voronov, Jelena, et al.
Published: (2026)

RB-Modulation: Training-Free Personalization of Diffusion Models using Stochastic Optimal Control
by: Rout, Litu, et al.
Published: (2024)

Federated Ensemble-Directed Offline Reinforcement Learning
by: Rengarajan, Desik, et al.
Published: (2023)

Monte Carlo Permutation Search
by: Cazenave, Tristan
Published: (2025)

More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling
by: Ishfaq, Haque, et al.
Published: (2024)

Efficient Monte Carlo Tree Search via On-the-Fly State-Conditioned Action Abstraction
by: Kwak, Yunhyeok, et al.
Published: (2024)

Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities
by: Akhound-Sadegh, Tara, et al.
Published: (2025)

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation
by: Zhao, Runze, et al.
Published: (2025)

Epistemic Monte Carlo Tree Search
by: Oren, Yaniv, et al.
Published: (2022)

Coupled Data and Measurement Space Dynamics for Enhanced Diffusion Posterior Sampling
by: Hamidi, Shayan Mohajer, et al.
Published: (2025)

Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling
by: Agarwal, Alekh, et al.
Published: (2022)