Publications (14)
Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory
Haoran Li, Wei Fan, Yulin Chen +5
Privacy research has attracted wide attention as individuals worry that their private data can be easily leaked during interactions with smart devices, social platforms, and AI app…
Eliminating Contextual Prior Bias for Semantic Image Editing via Dual-Cycle Diffusion
Zuopeng Yang, Tianshu Chu, Xin Lin +4
The recent success of text-to-image generation diffusion models has also revolutionized semantic image editing, enabling the manipulation of images based on query/target texts. Des…
Multi-agent Reinforcement Learning for Networked System Control
Tianshu Chu, Sandeep Chinchali, Sachin Katti
This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specifically, each agent learns a decentralized control policy based on local observatio…
MCIP: Protecting MCP Safety via Model Contextual Integrity Protocol
Huihao Jing, Haoran Li, Wenbin Hu +5
As Model Context Protocol (MCP) introduces an easy-to-use ecosystem for users and developers, it also brings underexplored safety risks. Its decentralized architecture, which separ…
A Provably Convergent Plug-and-Play Framework for Stochastic Bilevel Optimization
Tianshu Chu, Dachuan Xu, Wei Yao +2
Bilevel optimization has recently attracted significant attention in machine learning due to its wide range of applications and advanced hierarchical optimization capabilities. In…
Energy-Efficient Power Control for Multiple-Task Split Inference in UAVs: A Tiny Learning-Based Approach
Chenxi Zhao, Min Sheng, Junyu Liu +2
The limited energy and computing resources of unmanned aerial vehicles (UAVs) hinder the application of aerial artificial intelligence. The utilization of split inference in UAVs g…
Cloud Resource Allocation for Cloud-Based Automotive Applications
Zhaojian Li, Tianshu Chu, Ilya V. Kolmanovsky +2
There is a rapidly growing interest in the use of cloud computing for automotive vehicles to facilitate computation and data intensive tasks. Efficient utilization of on-demand clo…
PrivaCI-Bench: Evaluating Privacy with Contextual Integrity and Legal Compliance
Haoran Li, Wenbin Hu, Huihao Jing +6
Recent advancements in generative large language models (LLMs) have enabled wider applicability, accessibility, and flexibility. However, their reliability and trustworthiness are…
Mixed-Precision Quantized Neural Network with Progressively Decreasing Bitwidth For Image Classification and Object Detection
Tianshu Chu, Qin Luo, Jie Yang +1
Efficient model inference is an important and practical issue in the deployment of deep neural network on resource constraint platforms. Network quantization addresses this problem…
PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control
Dong Chen, Kaian Chen. Zhaojian Li, Tianshu Chu +3
This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-base…
SPABA: A Single-Loop and Probabilistic Stochastic Bilevel Algorithm Achieving Optimal Sample Complexity
Tianshu Chu, Dachuan Xu, Wei Yao +1
While stochastic bilevel optimization methods have been extensively studied for addressing large-scale nested optimization problems in machine learning, it remains an open question…
Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control
Tianshu Chu, Jie Wang, Lara Codecà +1
Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhan…
Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning
Wenbin Hu, Haoran Li, Huihao Jing +7
While Large Language Models (LLMs) exhibit remarkable capabilities, they also introduce significant safety and privacy risks. Current mitigation strategies often fail to preserve c…
Predicting Depression and Anxiety: A Multi-Layer Perceptron for Analyzing the Mental Health Impact of COVID-19
David Fong, Tianshu Chu, Matthew Heflin +2
We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends, particularly anxiety and depression, durin…