10 citations · 15 across the 6 of their papers we have counts for
6 papers
Learning Models of Adversarial Agent Behavior under Partial Observability
Sean Ye, Manisha Natarajan, Zixuan Wu +3
The need for opponent modeling and tracking arises in several real-world scenarios, such as professional sports, video game design, and drug-trafficking interdiction. In this work,…
Diffusion Models for Multi-target Adversarial Tracking
Sean Ye, Manisha Natarajan, Zixuan Wu +1
Target tracking plays a crucial role in real-world scenarios, particularly in drug-trafficking interdiction, where the knowledge of an adversarial target's location is often limite…
Adversarial Search and Tracking with Multiagent Reinforcement Learning in Sparsely Observable Environment
Zixuan Wu, Sean Ye, Manisha Natarajan +3
We study a search and tracking (S&T) problem where a team of dynamic search agents must collaborate to track an adversarial, evasive agent. The heterogeneous search team may only h…
Image-Based Trajectory Tracking through Unknown Environments without Absolute Positioning
Shiyu Feng, Zixuan Wu, Yipu Zhao +1
This paper describes a stereo image-based visual servoing system for trajectory tracking by a non-holonomic robot without externally derived pose information nor a known visual map…
Revisiting SGD with Increasingly Weighted Averaging: Optimization and Generalization Perspectives
Zhishuai Guo, Yan Yan, Tianbao Yang
Stochastic gradient descent (SGD) has been widely studied in the literature from different angles, and is commonly employed for solving many big data machine learning problems. How…
A Simple and Effective Framework for Pairwise Deep Metric Learning
Qi Qi, Yan Yan, Xiaoyu Wang +1
Deep metric learning (DML) has received much attention in deep learning due to its wide applications in computer vision. Previous studies have focused on designing complicated loss…