243 citations · 255 across the 9 of their papers we have counts for
9 papers
AppEval: A Unified Benchmark for LLM-Based Mobile Application Repair in ArkTS, Swift, and Kotlin
Bang Xie, Hao Liu, Zhenyu Shi +11
Repository-level LLM agents are typically evaluated on projects whose tests run on the build host. It remains unclear whether their repairs survive the mobile build-install-launch-…
Prediction with Incomplete Data under Agnostic Mask Distribution Shift
Yichen Zhu, Jian Yuan, Bo Jiang +4
Data with missing values is ubiquitous in many applications. Recent years have witnessed increasing attention on prediction with only incomplete data consisting of observed feature…
User-Oriented Robust Reinforcement Learning
Haoyi You, Beichen Yu, Haiming Jin +2
Recently, improving the robustness of policies across different environments attracts increasing attention in the reinforcement learning (RL) community. Existing robust RL methods…
DeCOM: Decomposed Policy for Constrained Cooperative Multi-Agent Reinforcement Learning
Zhaoxing Yang, Rong Ding, Haiming Jin +5
In recent years, multi-agent reinforcement learning (MARL) has presented impressive performance in various applications. However, physical limitations, budget restrictions, and man…
Networked Time Series Prediction with Incomplete Data via Generative Adversarial Network
Yichen Zhu, Bo Jiang, Haiming Jin +5
A networked time series (NETS) is a family of time series on a given graph, one for each node. It has a wide range of applications from intelligent transportation, environment moni…
Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems
Chang Liu, Jiahui Sun, Haiming Jin +7
Nowadays, ridesharing has become one of the most popular services offered by online ride-hailing platforms (e.g., Uber and Didi Chuxing). Existing ridesharing platforms adopt the s…
CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario
Huichu Zhang, Siyuan Feng, Chang Liu +7
Traffic signal control is an emerging application scenario for reinforcement learning. Besides being as an important problem that affects people's daily life in commuting, traffic…
Theseus: Incentivizing Truth Discovery in Mobile Crowd Sensing Systems
Haiming Jin, Lu Su, Klara Nahrstedt
The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource sensory data collection to the public crowd. In order t…
CENTURION: Incentivizing Multi-Requester Mobile Crowd Sensing
Haiming Jin, Lu Su, Klara Nahrstedt
The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of par…