Image de couverture de livre

Bandit Allocational Instability

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Chen, Yilun, Lu, Jiaqi
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Optimization and Control Probability
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Model Predictive Control is Almost Optimal for Restless Bandit
par: Gast, Nicolas, et autres
Publié: (2024)

Representative Action Selection for Large Action Space Bandit Families
par: Zhou, Quan, et autres
Publié: (2025)

Representative Action Selection for Large Action Space: From Bandits to MDPs
par: Zhou, Quan, et autres
Publié: (2025)

Model Predictive Control is almost Optimal for Heterogeneous Restless Multi-armed Bandits
par: Narasimha, Dheeraj, et autres
Publié: (2025)

Online Resource Allocation with Average Budget Constraints
par: Ao, Ruicheng, et autres
Publié: (2024)

Beyond Non-Degeneracy: Revisiting Certainty Equivalent Heuristic for Online Linear Programming
par: Chen, Yilun, et autres
Publié: (2025)

Early Stopping in Contextual Bandits and Inferences
par: Cui, Zihan
Publié: (2025)

Lagrangian Index Policy for Restless Bandits with Average Reward
par: Avrachenkov, Konstantin, et autres
Publié: (2024)

When Machine Learning Meets Importance Sampling: A More Efficient Rare Event Estimation Approach
par: Zhao, Ruoning, et autres
Publié: (2025)

Online Learning and Optimization for Queues with Unknown Demand Curve and Service Distribution
par: Chen, Xinyun, et autres
Publié: (2023)

Prelimit Coupling and Steady-State Convergence of Constant-stepsize Nonsmooth Contractive SA
par: Zhang, Yixuan, et autres
Publié: (2024)

On propagation of chaos for the Fisher-Rao gradient flow in entropic mean-field optimization
par: Lazić, Petra, et autres
Publié: (2026)

Value Mirror Descent for Reinforcement Learning
par: Jia, Zhichao, et autres
Publié: (2026)

Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once
par: Piening, Moritz, et autres
Publié: (2026)

Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise
par: Agrawal, Shubhada, et autres
Publié: (2026)

Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations
par: Huynh, Phuoc-Toan, et autres
Publié: (2026)

Wasserstein Formulation of Reinforcement Learning. An Optimal Transport Perspective on Policy Optimization
par: Dus, Mathias
Publié: (2026)

A Generalization Result for Convergence in Learning-to-Optimize
par: Sucker, Michael, et autres
Publié: (2024)

Properties of Discrete Sliced Wasserstein Losses
par: Tanguy, Eloi, et autres
Publié: (2023)

Non-convex entropic mean-field optimization via Best Response flow
par: Lascu, Razvan-Andrei, et autres
Publié: (2025)

Decoupled Functional Central Limit Theorems for Two-Time-Scale Stochastic Approximation
par: Han, Yuze, et autres
Publié: (2024)

Improved sampling via learned diffusions
par: Richter, Lorenz, et autres
Publié: (2023)

Function approximation by neural nets in the mean-field regime: Entropic regularization and controlled McKean-Vlasov dynamics
par: Tzen, Belinda, et autres
Publié: (2020)

Asymptotic regularity of a generalised stochastic Halpern scheme
par: Pischke, Nicholas, et autres
Publié: (2024)

ODE approximation for the Adam algorithm: General and overparametrized setting
par: Dereich, Steffen, et autres
Publié: (2025)

Flatness-Aware Stochastic Gradient Langevin Dynamics
par: Bruno, Stefano, et autres
Publié: (2025)

Admission Control of Quasi-Reversible Queueing Systems: Optimization and Reinforcement Learning
par: Comte, Céline, et autres
Publié: (2025)

Approximation and interpolation of deep neural networks
par: Constantinescu, Vlad-Raul, et autres
Publié: (2023)

Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization
par: Kassing, Sebastian, et autres
Publié: (2025)

Hilbert's projective metric for functions of bounded growth and exponential convergence of Sinkhorn's algorithm
par: Eckstein, Stephan
Publié: (2023)

Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel
par: Hagemann, Paul, et autres
Publié: (2023)

Designing Algorithms for Entropic Optimal Transport from an Optimisation Perspective
par: Srinivasan, Vishwak, et autres
Publié: (2025)

Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
par: Hu, Jie, et autres
Publié: (2024)

Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses
par: Tanguy, Eloi
Publié: (2023)

Stochastic Inverse Problem: stability, regularization and Wasserstein gradient flow
par: Li, Qin, et autres
Publié: (2024)

Benchmarking Diffusion Annealing-Based Bayesian Inverse Problem Solvers
par: Crafts, Evan Scope, et autres
Publié: (2025)

A stochastic gradient descent algorithm with random search directions
par: Gbaguidi, Eméric
Publié: (2025)

Weak Convergence Analysis of Online Neural Actor-Critic Algorithms
par: Lam, Samuel Chun-Hei, et autres
Publié: (2024)

On diffusion-based generative models and their error bounds: The log-concave case with full convergence estimates
par: Bruno, Stefano, et autres
Publié: (2023)

Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting
par: Ustimenko, Aleksei, et autres
Publié: (2023)