Cover Image

Robust Control with Gradient Uncertainty

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Qi, Qian
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Universal Approximation Theorem for Deep Q-Learning via FBSDE System
by: Qi, Qian
Published: (2025)

Adaptively Robust LLM Inference Optimization under Prediction Uncertainty
by: Chen, Zixi, et al.
Published: (2025)

Robust Reinforcement Learning in Finance: Modeling Market Impact with Elliptic Uncertainty Sets
by: Ma, Shaocong, et al.
Published: (2025)

DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
by: Cui, Mingxuan, et al.
Published: (2025)

Neural Brownian Motion
by: Qi, Qian
Published: (2025)

Revisiting LQR Control from the Perspective of Receding-Horizon Policy Gradient
by: Zhang, Xiangyuan, et al.
Published: (2023)

Optimal Control for Transformer Architectures: Enhancing Generalization, Robustness and Efficiency
by: Kan, Kelvin, et al.
Published: (2025)

Delightful Policy Gradient
by: Osband, Ian
Published: (2026)

Robust $Q$-learning Algorithm for Markov Decision Processes under Wasserstein Uncertainty
by: Neufeld, Ariel, et al.
Published: (2022)

Delightful Distributed Policy Gradient
by: Osband, Ian
Published: (2026)

Gradient Descent Efficiency Index
by: Dhingra, Aviral
Published: (2024)

Unbiased Gradient Low-Rank Projection
by: Pan, Rui, et al.
Published: (2025)

Neural Hamiltonian Operator
by: Qi, Qian
Published: (2025)

Performative Policy Gradient: Optimality in Performative Reinforcement Learning
by: Basu, Debabrota, et al.
Published: (2025)

Almost Bayesian: The Fractal Dynamics of Stochastic Gradient Descent
by: Hennick, Max, et al.
Published: (2025)

Boosting Gradient Ascent for Continuous DR-submodular Maximization
by: Zhang, Qixin, et al.
Published: (2024)

Riemann Sum Optimization for Accurate Integrated Gradients Computation
by: Swain, Swadesh, et al.
Published: (2024)

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

On the Convergence of (Stochastic) Gradient Descent for Kolmogorov--Arnold Networks
by: Gao, Yihang, et al.
Published: (2024)

On the Optimal Construction of Unbiased Gradient Estimators for Zeroth-Order Optimization
by: Ma, Shaocong, et al.
Published: (2025)

Deterministic Policy Gradient for Reinforcement Learning with Continuous Time and State
by: Cheng, Ziheng, et al.
Published: (2025)

A Unified Framework for Gradient Aggregation in Multi-Objective Optimization
by: Hu, Zeou, et al.
Published: (2026)

Implicit Regularization of Gradient Flow on One-Layer Softmax Attention
by: Sheen, Heejune, et al.
Published: (2024)

Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries
by: Ganesh, Swetha, et al.
Published: (2024)

Weighted Low-rank Approximation via Stochastic Gradient Descent on Manifolds
by: Xu, Conglong, et al.
Published: (2025)

On a Gradient Approach to Chebyshev Center Problems with Applications to Function Learning
by: Raghuvanshi, Abhinav, et al.
Published: (2026)

Towards Efficient Risk-Sensitive Policy Gradient: An Iteration Complexity Analysis
by: Liu, Rui, et al.
Published: (2024)

Isotropic Curvature Model for Understanding Deep Learning Optimization: Is Gradient Orthogonalization Optimal?
by: Su, Weijie
Published: (2025)

Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence
by: Xiao, Minheng, et al.
Published: (2024)

On Finding Small Hyper-Gradients in Bilevel Optimization: Hardness Results and Improved Analysis
by: Chen, Lesi, et al.
Published: (2023)

Policy Optimization in Hybrid Discrete-Continuous Action Spaces via Mixed Gradients
by: Alvo, Matias, et al.
Published: (2026)

Rod Flow: A Continuous-Time Model for Gradient Descent at the Edge of Stability
by: Regis, Eric, et al.
Published: (2026)

A Methodology Establishing Linear Convergence of Adaptive Gradient Methods under PL Inequality
by: Chakrabarti, Kushal, et al.
Published: (2024)

Enhancing Stochastic Gradient Descent: A Unified Framework and Novel Acceleration Methods for Faster Convergence
by: Deng, Yichuan, et al.
Published: (2024)

Neural-Rendezvous: Provably Robust Guidance and Control to Encounter Interstellar Objects
by: Tsukamoto, Hiroyasu, et al.
Published: (2022)

Deep Learning for Sequential Decision Making under Uncertainty: Foundations, Frameworks, and Frontiers
by: Buyuktahtakin, I. Esra
Published: (2026)

Provable Acceleration of Nesterov's Accelerated Gradient Method over Heavy Ball Method in Training Over-Parameterized Neural Networks
by: Liu, Xin, et al.
Published: (2022)

AI2STOW: End-to-End Deep Reinforcement Learning to Construct Master Stowage Plans under Demand Uncertainty
by: Van Twiller, Jaike, et al.
Published: (2025)

Deep Learning for Two-Stage Robust Integer Optimization
by: Dumouchelle, Justin, et al.
Published: (2023)

Stability of Primal-Dual Gradient Flow Dynamics for Multi-Block Convex Optimization Problems
by: Ozaslan, Ibrahim K., et al.
Published: (2024)