2 citations · 5 across the 6 of their papers we have counts for
7 papers
The Pervasive Blind Spot: Benchmarking VLM Inference Risks on Everyday Personal Videos
Shuning Zhang, Zhaoxin Li, Changxi Wen +8
The proliferation of Vision-Language Models (VLMs) introduces profound privacy risks from personal videos. This paper addresses the critical yet unexplored inferential privacy thre…
Improvement of Optimization using Learning Based Models in Mixed Integer Linear Programming Tasks
Xiaoke Wang, Batuhan Altundas, Zhaoxin Li +2
Mixed Integer Linear Programs (MILPs) are essential tools for solving planning and scheduling problems across critical industries such as construction, manufacturing, and logistics…
Towards Automated Semantic Interpretability in Reinforcement Learning via Vision-Language Models
Zhaoxin Li, Zhang Xi-Jia, Batuhan Altundas +3
Semantic interpretability in Reinforcement Learning (RL) enables transparency and verifiability of decision-making. Achieving semantic interpretability in reinforcement learning re…
Exploring Personality-Driven Personalization in XAI: Enhancing User Trust in Gameplay
Zhaoxin Li, Sophie Yang, Shijie Wang
Tailoring XAI methods to individual needs is crucial for intuitive Human-AI interactions. While context and task goals are vital, factors like user personality traits could also in…
Faster Model Predictive Control via Self-Supervised Initialization Learning
Zhaoxin Li, Xiaoke Wang, Letian Chen +3
Model Predictive Control (MPC) is widely used in robot control by optimizing a sequence of control outputs over a finite-horizon. Computational approaches for MPC include determini…
A Design Trajectory Map of Human-AI Collaborative Reinforcement Learning Systems: Survey and Taxonomy
Zhaoxing Li
Driven by the algorithmic advancements in reinforcement learning and the increasing number of implementations of human-AI collaboration, Collaborative Reinforcement Learning (CRL)…
Interpretable Reinforcement Learning for Robotics and Continuous Control
Rohan Paleja, Letian Chen, Yaru Niu +10
Interpretability in machine learning is critical for the safe deployment of learned policies across legally-regulated and safety-critical domains. While gradient-based approaches i…