Image de couverture de livre

Constrained Exploration via Reflected Replica Exchange Stochastic Gradient Langevin Dynamics

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zheng, Haoyang, Du, Hengrong, Feng, Qi, Deng, Wei, Lin, Guang
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Exploring Non-Convex Discrete Energy Landscapes: An Efficient Langevin-Like Sampler with Replica Exchange
par: Zheng, Haoyang, et autres
Publié: (2025)

Rethinking Langevin Thompson Sampling from A Stochastic Approximation Perspective
par: Wang, Weixin, et autres
Publié: (2025)

Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo
par: Zheng, Haoyang, et autres
Publié: (2024)

BLADE: Bayesian Langevin Active Discovery with Replica Exchange for Identification of Complex Systems
par: Kong, Cindy Xiangrui, et autres
Publié: (2025)

Non-Reversible Langevin Algorithms for Constrained Sampling
par: Du, Hengrong, et autres
Publié: (2025)

Reflected Schrödinger Bridge for Constrained Generative Modeling
par: Deng, Wei, et autres
Publié: (2024)

Direct Soft-Policy Sampling via Langevin Dynamics
par: Ki, Donghyeon, et autres
Publié: (2026)

Ultra-Fast Language Generation via Discrete Diffusion Divergence Instruct
par: Zheng, Haoyang, et autres
Publié: (2025)

Cascaded two-stage feature clustering and selection via separability and consistency in fuzzy decision systems
par: Chen, Yuepeng, et autres
Publié: (2024)

Stochastic Collapse: How Gradient Noise Attracts SGD Dynamics Towards Simpler Subnetworks
par: Chen, Feng, et autres
Publié: (2023)

Margin-aware Fuzzy Rough Feature Selection: Bridging Uncertainty Characterization and Pattern Classification
par: Xu, Suping, et autres
Publié: (2025)

Optimal Stochastic Trace Estimation in Generative Modeling
par: Liu, Xinyang, et autres
Publié: (2025)

Emergence of Exploration in Policy Gradient Reinforcement Learning via Retrying
par: Nishimori, Soichiro, et autres
Publié: (2026)

HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions
par: Zheng, Haoyang, et autres
Publié: (2023)

Ensuring Safety in an Uncertain Environment: Constrained MDPs via Stochastic Thresholds
par: Zuo, Qian, et autres
Publié: (2025)

Stochastic Re-weighted Gradient Descent via Distributionally Robust Optimization
par: Kumar, Ramnath, et autres
Publié: (2023)

Incentivized Exploration of Non-Stationary Stochastic Bandits
par: Chakraborty, Sourav, et autres
Publié: (2024)

Almost Bayesian: The Fractal Dynamics of Stochastic Gradient Descent
par: Hennick, Max, et autres
Publié: (2025)

Provably Efficient Exploration in Inverse Constrained Reinforcement Learning
par: Yue, Bo, et autres
Publié: (2024)

Optimistic Exploration for Risk-Averse Constrained Reinforcement Learning
par: McCarthy, James, et autres
Publié: (2025)

Fast Explanations via Policy Gradient-Optimized Explainer
par: Pan, Deng, et autres
Publié: (2024)

Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC
par: Yang, Sikun, et autres
Publié: (2024)

Unlocking Reasoning Capabilities in LLMs via Reinforcement Learning Exploration
par: Deng, Wenhao, et autres
Publié: (2025)

Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling
par: Xu, Jian, et autres
Publié: (2024)

Adaptive Heavy-Tailed Stochastic Gradient Descent
par: Gong, Bodu, et autres
Publié: (2025)

Stochastic Gradient Descent with Momentum is Algorithmically Stable
par: Lei, Yunwen, et autres
Publié: (2026)

Stochastic Penalty-Barrier Methods for Constrained Machine Learning
par: Bosák, Adam, et autres
Publié: (2026)

Diff-Instruct with Diffused Reward: Towards Principled One-step Generator RL
par: Wu, Junyi, et autres
Publié: (2026)

ASTRO: Adaptive Stitching via Dynamics-Guided Trajectory Rollouts
par: Yu, Hang, et autres
Publié: (2025)

QF: Quick Feedforward AI Model Training without Gradient Back Propagation
par: Qi, Feng
Publié: (2025)

An Effective Dynamic Gradient Calibration Method for Continual Learning
par: Lin, Weichen, et autres
Publié: (2024)

Sequential Controlled Langevin Diffusions
par: Chen, Junhua, et autres
Publié: (2024)

Tilted Quantile Gradient Updates for Quantile-Constrained Reinforcement Learning
par: Li, Chenglin, et autres
Publié: (2024)

Can LLMs predict the convergence of Stochastic Gradient Descent?
par: Zekri, Oussama, et autres
Publié: (2024)

Noise Balance and Stationary Distribution of Stochastic Gradient Descent
par: Ziyin, Liu, et autres
Publié: (2023)

Beyond Markovian: Reflective Exploration via Bayes-Adaptive RL for LLM Reasoning
par: Zhang, Shenao, et autres
Publié: (2025)

Enhancing Stochastic Gradient Descent: A Unified Framework and Novel Acceleration Methods for Faster Convergence
par: Deng, Yichuan, et autres
Publié: (2024)

Gradient Flow Drifting: Generative Modeling via Wasserstein Gradient Flows of KDE-Approximated Divergences
par: Cao, Jiarui, et autres
Publié: (2026)

Geometric Neural Operators via Lie Group-Constrained Latent Dynamics
par: Zhang, Jiaquan, et autres
Publié: (2026)

Generalized Discrete Diffusion with Self-Correction
par: Wang, Linxuan, et autres
Publié: (2026)