:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Polikar, Dor, Cohen, Alon
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2511.12534
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Eluder-based Regret for Stochastic Contextual MDPs
by: Levy, Orin, et al.
Published: (2022)

Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path
by: Di, Qiwei, et al.
Published: (2024)

Regret Bounds for Adversarial Contextual Bandits with General Function Approximation and Delayed Feedback
by: Levy, Orin, et al.
Published: (2025)

Near-Optimal Regret for Policy Optimization in Contextual MDPs with General Offline Function Approximation
by: Levy, Orin, et al.
Published: (2026)

Regret Lower Bounds for Decentralized Multi-Agent Stochastic Shortest Path Problems
by: Chavan, Utkarsh U., et al.
Published: (2025)

Stochastic Shortest Path with Sparse Adversarial Costs
by: Johnson, Emmeran, et al.
Published: (2025)

Spectral Journey: How Transformers Predict the Shortest Path
by: Cohen, Andrew, et al.
Published: (2025)

Convergent Reinforcement Learning Algorithms for Stochastic Shortest Path Problem
by: Guin, Soumyajit, et al.
Published: (2025)

On the Optimal Regret of Locally Private Linear Contextual Bandit
by: Li, Jiachun, et al.
Published: (2024)

Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model
by: Tarbouriech, Jean, et al.
Published: (2026)

Variance-Aware Regret Bounds for Stochastic Contextual Dueling Bandits
by: Di, Qiwei, et al.
Published: (2023)

Any-Time Regret-Guaranteed Algorithm for Control of Linear Quadratic Systems
by: Chekan, Jafar Abbaszadeh, et al.
Published: (2024)

Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit
by: Kim, Seok-Jin, et al.
Published: (2024)

Tangential Randomization in Linear Bandits (TRAiL): Guaranteed Inference and Regret Bounds
by: Güçlü, Arda, et al.
Published: (2024)

Rate-Optimal Policy Optimization for Linear Markov Decision Processes
by: Sherman, Uri, et al.
Published: (2023)

Stochastic Regret Guarantees for Online Zeroth- and First-Order Bilevel Optimization
by: Nazari, Parvin, et al.
Published: (2025)

Active Learning for Stochastic Contextual Linear Bandits
by: Brunskill, Emma, et al.
Published: (2026)

Unsupervised Learning for the Elementary Shortest Path Problem
by: Chen, Jingyi, et al.
Published: (2025)

Learning Shortest Paths When Data is Scarce
by: Matsypura, Dmytro, et al.
Published: (2026)

Fast Best-in-Class Regret for Contextual Bandits
by: Girard, Samuel, et al.
Published: (2025)

Optimal Regret for Policy Optimization in Contextual Bandits
by: Levy, Orin, et al.
Published: (2026)

The Sample Complexity of Multiclass and Sparse Contextual Bandits
by: Erez, Liad, et al.
Published: (2026)

Improved Bounds for Reward-Agnostic and Reward-Free Exploration
by: Ridel, Oran, et al.
Published: (2026)

Skeleton-Guided Learning for Shortest Path Search
by: Liu, Tiantian, et al.
Published: (2025)

Improved Regret and Contextual Linear Extension for Pandora's Box and Prophet Inequality
by: Liu, Junyan, et al.
Published: (2025)

Learning Shortest Paths with Generative Flow Networks
by: Morozov, Nikita, et al.
Published: (2026)

Redefining the Shortest Path Problem Formulation of the Linear Non-Gaussian Acyclic Model: Pairwise Likelihood Ratios, Prior Knowledge, and Path Enumeration
by: Ong, Hans Jarett J., et al.
Published: (2024)

Queue Length Regret Bounds for Contextual Queueing Bandits
by: Bae, Seoungbin, et al.
Published: (2026)

How Does Variance Shape the Regret in Contextual Bandits?
by: Jia, Zeyu, et al.
Published: (2024)

Optimal-Point Variance Reduction For Bayesian Optimization With Regret Guarantee
by: Takeno, Shion
Published: (2026)

Data-Driven Online Model Selection With Regret Guarantees
by: Pacchiano, Aldo, et al.
Published: (2023)

Neural Shortest Path for Surface Reconstruction from Point Clouds
by: Park, Yesom, et al.
Published: (2025)

Generalization in LLM Problem Solving: The Case of the Shortest Path
by: Tong, Yao, et al.
Published: (2026)

Tighter Regret Bounds for Contextual Action-Set Reinforcement Learning
by: Chen, Zijun, et al.
Published: (2026)

Active Context Selection Improves Simple Regret in Contextual Bandits
by: Shahverdikondori, Mohammad, et al.
Published: (2026)

Stronger Neyman Regret Guarantees for Adaptive Experimental Design
by: Noarov, Georgy, et al.
Published: (2025)

Fairness and Privacy Guarantees in Federated Contextual Bandits
by: Solanki, Sambhav, et al.
Published: (2024)

Variance-Dependent Regret Lower Bounds for Contextual Bandits
by: He, Jiafan, et al.
Published: (2025)

Improved Regret of Linear Ensemble Sampling
by: Lee, Harin, et al.
Published: (2024)

Incremental Approximate Single-Source Shortest Paths with Predictions
by: McCauley, Samuel, et al.
Published: (2025)