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20232026
most citedA Deep Learning Analysis of Climate Change, Innovation, and Uncertainty

1 citations · 1 across the 12 of their papers we have counts for

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10 papers · 1 filter

math.OC2026

The Price of Covertness: Dual-Control Navigation in Uncertain Flows under Adversarial Sensing

Ruimeng Hu, Botao Jin, Xu Yang

Covert navigation in an uncertain flow couples motion planning with information acquisition. A vehicle must estimate the local flow to navigate, while estimation error induces corr…

math.OC2026

Strategic Inference of Adversarial Navigation Objectives for Unmanned Underwater Vehicles

Ruimeng Hu, Xu Yang

We study destination inference for adversarial unmanned underwater navigation in a spatially varying current field. The red vehicle is modeled as approximately following a Hamilton…

math.OC2026

Optimal Design of Stealthy Attacks in Partially Observed Linear Systems: A Likelihood-Based Approach

Haosheng Zhou, Ruimeng Hu

We study the optimal design of stealthy attacks against partially observed linear control systems. We first propose a novel likelihood-based detection mechanism derived from the in…

math.OC2026

Deception in Linear-Quadratic Control

Yerin Kim, Haosheng Zhou, Alexander Benvenuti +2

Systems operating in adversarial environments may inadvertently leak sensitive information to adversaries. To address this challenge, we revisit the linear-quadratic control framew…

math.OC2026

Information Revelation and Alignment Faking in Stochastic Differential Games

Daniel Ralston, Xu Yang, Ruimeng Hu

In competitive games with private objectives, actions can reveal information about hidden parameters. However, quantifying such information revelation is nontrivial, since it depen…

math.OC2025

Learning Mean-Field Games through Mean-Field Actor-Critic Flow

Mo Zhou, Haosheng Zhou, Ruimeng Hu

We propose the Mean-Field Actor-Critic (MFAC) flow, a continuous-time learning dynamics for solving mean-field games (MFGs), combining techniques from reinforcement learning and op…

math.OC2025

Strategic Inference in Stackelberg Games: Optimal Control for Revealing Adversary Intent

Ruimeng Hu, Daniel Ralston, Xu Yang +1

We study a continuous-time stochastic Stackelberg game in which a leader seeks to accomplish a primary objective while inferring a hidden parameter of a rational follower. The foll…

math.OC2025

Adversarial Decision-Making in Partially Observable Multi-Agent Systems: A Sequential Hypothesis Testing Approach

Haosheng Zhou, Daniel Ralston, Xu Yang +1

Adversarial decision-making in partially observable multi-agent systems requires sophisticated strategies for both deception and counter-deception. This paper presents a sequential…

math.OC2025

Integrating Sequential Hypothesis Testing into Adversarial Games: A Sun Zi-Inspired Framework

Haosheng Zhou, Daniel Ralston, Xu Yang +1

This paper investigates the interplay between sequential hypothesis testing (SHT) and adversarial decision-making in partially observable games, focusing on the deceptive strategie…

math.OC2024

Finite-Agent Stochastic Differential Games on Large Graphs: I. The Linear-Quadratic Case

Ruimeng Hu, Jihao Long, Haosheng Zhou

In this paper, we study finite-agent linear-quadratic games on graphs. Specifically, we propose a comprehensive framework that extends the existing literature by incorporating hete…