:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhu, Youheng, Lu, Yiping
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Optimization and Control
Online Access:	https://arxiv.org/abs/2603.03191
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Residuals-based Offline Reinforcement Learning
by: Zhu, Qing, et al.
Published: (2026)

Solving Truly Massive Budgeted Monotonic POMDPs with Oracle-Guided Meta-Reinforcement Learning
by: Vora, Manav, et al.
Published: (2024)

Offline Reinforcement Learning via Inverse Optimization
by: Dimanidis, Ioannis, et al.
Published: (2025)

Dual Control of Linear Systems from Bilinear Observations with Belief Space Model Predictive Control
by: Cao, Daniel, et al.
Published: (2026)

Offline Hierarchical Reinforcement Learning via Inverse Optimization
by: Schmidt, Carolin, et al.
Published: (2024)

Operator Models for Continuous-Time Offline Reinforcement Learning
by: Hoischen, Nicolas, et al.
Published: (2025)

On the Width Scaling of Neural Optimizers Under Matrix Operator Norms I: Row/Column Normalization and Hyperparameter Transfer
by: Xu, Ruihan, et al.
Published: (2026)

Offline-Online Reinforcement Learning for Linear Mixture MDPs
by: Zhang, Zhongjun, et al.
Published: (2026)

Reward-Relevance-Filtered Linear Offline Reinforcement Learning
by: Zhou, Angela
Published: (2024)

Online Residual Learning from Offline Experts for Pedestrian Tracking
by: Vlachos, Anastasios, et al.
Published: (2024)

Offline Policy Learning with Weight Clipping and Heaviside Composite Optimization
by: Liu, Jingren, et al.
Published: (2026)

Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning
by: Di, Qiwei, et al.
Published: (2023)

PAC-Bayes Meets Online Contextual Optimization
by: Xie, Zhuojun, et al.
Published: (2025)

Offline Reinforcement Learning via Linear-Programming with Error-Bound Induced Constraints
by: Ozdaglar, Asuman, et al.
Published: (2022)

No-Rank Tensor Decomposition Using Metric Learning
by: Bagherian, Maryam
Published: (2025)

SPP-SBL: Space-Power Prior Sparse Bayesian Learning for Block Sparse Recovery
by: Zhang, Yanhao, et al.
Published: (2025)

Learning to Cover: Online Learning and Optimization with Irreversible Decisions
by: Jacquillat, Alexandre, et al.
Published: (2024)

SPARKLE: A Unified Single-Loop Primal-Dual Framework for Decentralized Bilevel Optimization
by: Zhu, Shuchen, et al.
Published: (2024)

OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud
by: Zou, Ding, et al.
Published: (2023)

Towards Optimal Offline Reinforcement Learning
by: Li, Mengmeng, et al.
Published: (2025)

Wait-Less Offline Tuning and Re-solving for Online Decision Making
by: Sun, Jingruo, et al.
Published: (2024)

Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
by: Zhang, Dake, et al.
Published: (2024)

Modeling Hierarchical Spaces: A Review and Unified Framework for Surrogate-Based Architecture Design
by: Saves, Paul, et al.
Published: (2025)

Unsupervised Ground Metric Learning
by: Auffenberg, Janis, et al.
Published: (2025)

A Distance Metric for Mixed Integer Programming Instances
by: Maudet, Gwen, et al.
Published: (2025)

Formation Shape Control using the Gromov-Wasserstein Metric
by: Nakashima, Haruto, et al.
Published: (2025)

Distributionally Robust Optimization via Iterative Algorithms in Continuous Probability Spaces
by: Zhu, Linglingzhi, et al.
Published: (2024)

A Framework for Adaptive Stabilisation of Nonlinear Stochastic Systems
by: Siriya, Seth, et al.
Published: (2025)

Provable Offline Reinforcement Learning for Structured Cyclic MDPs
by: Lee, Kyungbok, et al.
Published: (2026)

Integrated Offline and Online Learning to Solve a Large Class of Scheduling Problems
by: Liu, Anbang, et al.
Published: (2025)

Provably Efficient Representation Selection in Low-rank Markov Decision Processes: From Online to Offline RL
by: Zhang, Weitong, et al.
Published: (2021)

TaskMet: Task-Driven Metric Learning for Model Learning
by: Bansal, Dishank, et al.
Published: (2023)

On the Power of (Approximate) Reward Models for Inference-Time Scaling
by: Zhu, Youheng, et al.
Published: (2026)

A Control Theoretic Framework for Adaptive Gradient Optimizers in Machine Learning
by: Chakrabarti, Kushal, et al.
Published: (2022)

Bisimulation Metrics are Optimal Transport Distances, and Can be Computed Efficiently
by: Calo, Sergio, et al.
Published: (2024)

Belief Samples Are All You Need For Social Learning
by: JafariNodeh, Mahyar, et al.
Published: (2024)

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

A Randomized Zeroth-Order Hierarchical Framework for Heterogeneous Federated Learning
by: Qiu, Yuyang, et al.
Published: (2025)

FERERO: A Flexible Framework for Preference-Guided Multi-Objective Learning
by: Chen, Lisha, et al.
Published: (2024)