Saved in:
| Main Authors: | Zhang, Haochen, Zheng, Zhong, Xue, Lingzhou |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.02859 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition
by: Zheng, Zhong, et al.
Published: (2024)
by: Zheng, Zhong, et al.
Published: (2024)
Q-Learning with Fine-Grained Gap-Dependent Regret
by: Zhang, Haochen, et al.
Published: (2025)
by: Zhang, Haochen, et al.
Published: (2025)
Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
by: Zhang, Haochen, et al.
Published: (2026)
by: Zhang, Haochen, et al.
Published: (2026)
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
by: Zhang, Haochen, et al.
Published: (2025)
by: Zhang, Haochen, et al.
Published: (2025)
Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost
by: Zheng, Zhong, et al.
Published: (2024)
by: Zheng, Zhong, et al.
Published: (2024)
Federated Q-Learning: Linear Regret Speedup with Low Communication Cost
by: Zheng, Zhong, et al.
Published: (2023)
by: Zheng, Zhong, et al.
Published: (2023)
Smoothed Robust Phase Retrieval
by: Zheng, Zhong, et al.
Published: (2024)
by: Zheng, Zhong, et al.
Published: (2024)
A New Inexact Proximal Linear Algorithm with Adaptive Stopping Criteria for Robust Phase Retrieval
by: Zheng, Zhong, et al.
Published: (2023)
by: Zheng, Zhong, et al.
Published: (2023)
A Unified Combination Framework for Dependent Tests with Applications to Microbiome Association Studies
by: Yu, Xiufan, et al.
Published: (2024)
by: Yu, Xiufan, et al.
Published: (2024)
A Copula Graphical Model for Multi-Attribute Data using Optimal Transport
by: Zhang, Qi, et al.
Published: (2024)
by: Zhang, Qi, et al.
Published: (2024)
EXACT: Explicit Attribute-Guided Decoding-Time Personalization
by: Yu, Xin, et al.
Published: (2026)
by: Yu, Xin, et al.
Published: (2026)
AltLoRA: Towards Better Gradient Approximation in Low-Rank Adaptation with Alternating Projections
by: Yu, Xin, et al.
Published: (2025)
by: Yu, Xin, et al.
Published: (2025)
Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees
by: Yu, Xin, et al.
Published: (2024)
by: Yu, Xin, et al.
Published: (2024)
Sharp Gap-Dependent Variance-Aware Regret Bounds for Tabular MDPs
by: Chen, Shulun, et al.
Published: (2025)
by: Chen, Shulun, et al.
Published: (2025)
Distributed Networked Multi-task Learning
by: Hong, Lingzhou, et al.
Published: (2024)
by: Hong, Lingzhou, et al.
Published: (2024)
Strongly Consistent Community Detection in Popularity Adjusted Block Models
by: Yuan, Quan, et al.
Published: (2025)
by: Yuan, Quan, et al.
Published: (2025)
Hypothesis Testing for High-Dimensional Matrix-Valued Data
by: Cui, Shijie, et al.
Published: (2024)
by: Cui, Shijie, et al.
Published: (2024)
Structure-Preserving Nonlinear Sufficient Dimension Reduction for Tensors
by: Lin, Dianjun, et al.
Published: (2025)
by: Lin, Dianjun, et al.
Published: (2025)
Doubly robust estimation of causal effects for random object outcomes with continuous treatments
by: Bhattacharjee, Satarupa, et al.
Published: (2025)
by: Bhattacharjee, Satarupa, et al.
Published: (2025)
Support Basis: Fast Attention Beyond Bounded Entries
by: Aliakbarpour, Maryam, et al.
Published: (2025)
by: Aliakbarpour, Maryam, et al.
Published: (2025)
Statistical Convergence Rates of Optimal Transport Map Estimation between General Distributions
by: Ding, Yizhe, et al.
Published: (2024)
by: Ding, Yizhe, et al.
Published: (2024)
PrunedLoRA: Robust Gradient-Based structured pruning for Low-rank Adaptation in Fine-tuning
by: Yu, Xin, et al.
Published: (2025)
by: Yu, Xin, et al.
Published: (2025)
Preference-Based Self-Distillation: Beyond KL Matching via Reward Regularization
by: Yu, Xin, et al.
Published: (2026)
by: Yu, Xin, et al.
Published: (2026)
A Finite Sample Complexity Bound for Distributionally Robust Q-learning
by: Wang, Shengbo, et al.
Published: (2023)
by: Wang, Shengbo, et al.
Published: (2023)
Q-Distribution guided Q-learning for offline reinforcement learning: Uncertainty penalized Q-value via consistency model
by: Zhang, Jing, et al.
Published: (2024)
by: Zhang, Jing, et al.
Published: (2024)
HAVER: Instance-Dependent Error Bounds for Maximum Mean Estimation and Applications to Q-Learning and Monte Carlo Tree Search
by: Nguyen, Tuan Ngo, et al.
Published: (2024)
by: Nguyen, Tuan Ngo, et al.
Published: (2024)
Regularized Q-learning
by: Lim, Han-Dong, et al.
Published: (2022)
by: Lim, Han-Dong, et al.
Published: (2022)
HyperQ-Opt: Q-learning for Hyperparameter Optimization
by: Hasan, Md. Tarek
Published: (2024)
by: Hasan, Md. Tarek
Published: (2024)
The Bounds of Algorithmic Collusion; $Q$-learning, Gradient Learning, and the Folk Theorem
by: Askenazi-Golan, Galit, et al.
Published: (2024)
by: Askenazi-Golan, Galit, et al.
Published: (2024)
DualOptim: Enhancing Efficacy and Stability in Machine Unlearning with Dual Optimizers
by: Zhong, Xuyang, et al.
Published: (2025)
by: Zhong, Xuyang, et al.
Published: (2025)
Transfer Q-learning
by: Chen, Elynn, et al.
Published: (2022)
by: Chen, Elynn, et al.
Published: (2022)
Q-learning with Posterior Sampling
by: Agrawal, Priyank, et al.
Published: (2025)
by: Agrawal, Priyank, et al.
Published: (2025)
An Efficient Gradient-Aware Error-Bounded Lossy Compressor for Federated Learning
by: Ye, Zhijing, et al.
Published: (2025)
by: Ye, Zhijing, et al.
Published: (2025)
Plug-and-Play Parameter-Efficient Tuning of Embeddings for Federated Recommendation
by: Yuan, Haochen, et al.
Published: (2025)
by: Yuan, Haochen, et al.
Published: (2025)
Generalisation in Multitask Fitted Q-Iteration and Offline Q-learning
by: Manda, Kausthubh, et al.
Published: (2025)
by: Manda, Kausthubh, et al.
Published: (2025)
Boosting Soft Q-Learning by Bounding
by: Adamczyk, Jacob, et al.
Published: (2024)
by: Adamczyk, Jacob, et al.
Published: (2024)
Closing the Gap between TD Learning and Supervised Learning with $Q$-Conditioned Maximization
by: Lei, Xing, et al.
Published: (2025)
by: Lei, Xing, et al.
Published: (2025)
Tight Bounds for Jensen's Gap with Applications to Variational Inference
by: Mazur, Marcin, et al.
Published: (2025)
by: Mazur, Marcin, et al.
Published: (2025)
Review of deep learning models for crypto price prediction: implementation and evaluation
by: Wu, Jingyang, et al.
Published: (2024)
by: Wu, Jingyang, et al.
Published: (2024)
Q-learning as a monotone scheme
by: Yang, Lingyi
Published: (2024)
by: Yang, Lingyi
Published: (2024)
Similar Items
-
Gap-Dependent Bounds for Q-Learning using Reference-Advantage Decomposition
by: Zheng, Zhong, et al.
Published: (2024) -
Q-Learning with Fine-Grained Gap-Dependent Regret
by: Zhang, Haochen, et al.
Published: (2025) -
Gap-Dependent Bounds for Nearly Minimax Optimal Reinforcement Learning with Linear Function Approximation
by: Zhang, Haochen, et al.
Published: (2026) -
Regret-Optimal Q-Learning with Low Cost for Single-Agent and Federated Reinforcement Learning
by: Zhang, Haochen, et al.
Published: (2025) -
Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost
by: Zheng, Zhong, et al.
Published: (2024)