:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Abolhassani, Bahman, Tadrous, John, Eryilmaz, Atilla, Yüksel, Serdar
Format:	Preprint
Published:	2024
Subjects:	Optimization and Control
Online Access:	https://arxiv.org/abs/2401.03613
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SwiftCache: Model-Based Learning for Dynamic Content Caching in CDNs
by: Abolhassani, Bahman, et al.
Published: (2024)

Another Look at Partially Observed Optimal Stochastic Control: Existence, Ergodicity, and Approximations without Belief-Reduction
by: Yüksel, Serdar
Published: (2023)

Convergence of Gradient Descent for Recurrent Neural Networks: A Nonasymptotic Analysis
by: Cayci, Semih, et al.
Published: (2024)

Recurrent Natural Policy Gradient for POMDPs
by: Cayci, Semih, et al.
Published: (2024)

Partially Observed Optimal Stochastic Control: Regularity, Optimality, Approximations, and Learning
by: Kara, Ali Devran, et al.
Published: (2024)

Decentralized Learning for Optimality in Stochastic Dynamic Teams and Games with Local Control and Global State Information
by: Yongacoglu, Bora, et al.
Published: (2019)

Decentralized Exchangeable Stochastic Dynamic Teams in Continuous-time, their Mean-Field Limits and Optimality of Symmetric Policies
by: Sanjari, Sina, et al.
Published: (2024)

Quantum Markov Decision Processes: Dynamic and Semi-Definite Programs for Optimal Solutions
by: Saldi, Naci, et al.
Published: (2024)

On Borkar and Young Relaxed Control Topologies and Continuous Dependence of Invariant Measures on Control Policy
by: Yüksel, Serdar
Published: (2023)

Finite-Time Analysis of Gradient Descent for Shallow Transformers
by: Arda, Enes, et al.
Published: (2026)

Near Optimal Approximations and Finite Memory Policies for POMPDs with Continuous Spaces
by: Kara, Ali Devran, et al.
Published: (2024)

Data-Driven Non-Parametric Model Learning and Adaptive Control of MDPs with Borel spaces: Identifiability and Near Optimal Design
by: Mrani-Zentar, Omar, et al.
Published: (2025)

Reinforcement Learning for Jointly Optimal Coding and Control Policies for a Controlled Markovian System over a Communication Channel
by: Hubbard, Evelyn, et al.
Published: (2024)

An Optimal Control Approach To Transformer Training
by: Akman, Kağan, et al.
Published: (2026)

SLAM as a Stochastic Control Problem with Partial Information: Optimal Solutions and Rigorous Approximations
by: Gusija, Ilir, et al.
Published: (2026)

Optimality of Symmetric Independent Policies under Decentralized Mean-Field Information Sharing for Stochastic Teams and Equivalence with McKean-Vlasov Control of a Representative Agent
by: Sanjari, Sina, et al.
Published: (2024)

Subjective Equilibria under Beliefs of Exogenous Uncertainty: Linear Quadratic Case
by: Arslan, Gürdal, et al.
Published: (2024)

Decentralized Detection with Many Sensors: Optimality of Exchangeable and Identical Encoding Policies
by: Sanjari, Sina, et al.
Published: (2025)

Reinforcement Learning for Near-Optimal Design of Zero-Delay Codes for Markov Sources
by: Cregg, Liam, et al.
Published: (2023)

Sliding Window Codes: Near-Optimality and Q-Learning for Zero-Delay Coding
by: Cregg, Liam, et al.
Published: (2023)

Refined Bounds on Near Optimality Finite Window Policies in POMDPs and Their Reinforcement Learning
by: Demirci, Yunus Emre, et al.
Published: (2024)

Robust Decentralized Control of Coupled Systems via Risk Sensitive Control of Decoupled or Simple Models with Measure Change
by: Selk, Zachary, et al.
Published: (2024)

Stochastic Push-Pull for Decentralized Nonconvex Optimization
by: You, Runze, et al.
Published: (2025)

Robustness to Model Approximation, Model Learning From Data, and Sample Complexity in Wasserstein Regular MDPs
by: Zhou, Yichen, et al.
Published: (2024)

Q-Learning for Stochastic Control under General Information Structures and Non-Markovian Environments
by: Kara, Ali Devran, et al.
Published: (2023)

Kernel Mean Embedding Topology: Weak and Strong Forms for Stochastic Kernels and Implications for Model Learning
by: Saldi, Naci, et al.
Published: (2025)

Quantum Markov Decision Processes: General Theory, Approximations, and Classes of Policies
by: Saldi, Naci, et al.
Published: (2024)

Near Optimality of Lipschitz and Smooth Policies in Controlled Diffusions
by: Pradhan, Somnath, et al.
Published: (2024)

Non-Sequential Decentralized Stochastic Control Revisited: Causality and Static Reducibility
by: Mrani-Zentar, Omar, et al.
Published: (2022)

A Robust Compressed Push-Pull Method for Decentralized Nonconvex Optimization
by: Liao, Yiwei, et al.
Published: (2024)

On the Linear Speedup of the Push-Pull Method for Decentralized Optimization over Digraphs
by: Liang, Liyuan, et al.
Published: (2025)

Mean-Field Systems with Heterogeneous Subteams: Optimality of Cluster-Symmetric Independent Policies and Equivalence with Decentralized McKean-Vlasov Control of Cluster-Representative Agents
by: Braun, Connor S., et al.
Published: (2026)

Sensitivity of Filter Kernels and Robustness Bounds to Transition and Measurement Kernel Perturbations in Partially Observable Stochastic Control
by: Demirci, Yunus Emre, et al.
Published: (2025)

Stochastic Momentum Tracking Push-Pull for Decentralized Optimization over Directed Graphs
by: Fan, Wenqi, et al.
Published: (2026)

Approximations and Learning for Decentralized Stochastic Control and Near Optimal Finite Window Policies
by: Mrani-Zentar, Omar, et al.
Published: (2026)

Discrete-Time Approximations of Controlled Diffusions with Infinite Horizon Discounted and Average Cost
by: Pradhan, Somnath, et al.
Published: (2025)

Near Optimality of Discrete-Time Approximations for Controlled McKean-Vlasov Diffusions and Interacting Particle Systems
by: Pradhan, Somnath, et al.
Published: (2025)

Reinforcement Learning for Discounted and Ergodic Control of Diffusion Processes
by: Bayraktar, Erhan, et al.
Published: (2026)

Quantizer Design for Finite Model Approximations, Model Learning, and Quantized Q-Learning for MDPs with Unbounded Spaces
by: Bicer, Osman, et al.
Published: (2025)

B-ary Tree Push-Pull Method is Provably Efficient for Distributed Learning on Heterogeneous Data
by: You, Runze, et al.
Published: (2024)