462 citations · 1.9k across the 32 of their papers we have counts for
21 papers · 1 filter
CAAL: Confidence-Aware Active Learning for Heteroscedastic Atmospheric Regression
Fei Jiang, Jiyang Xia, Junjie Yu +6
Quantifying the impacts of air pollution on health and climate relies on key atmospheric particle properties such as toxicity and hygroscopicity. However, these properties typicall…
Urban Representation Learning for Fine-grained Economic Mapping: A Semi-supervised Graph-based Approach
Jinzhou Cao, Xiangxu Wang, Jiashi Chen +5
Fine-grained economic mapping through urban representation learning has emerged as a crucial tool for evidence-based economic decisions. While existing methods primarily rely on su…
Boosting Offline Reinforcement Learning with Residual Generative Modeling
Hua Wei, Deheng Ye, Zhao Liu +5
Offline reinforcement learning (RL) tries to learn the near-optimal policy with recorded offline experience without online exploration. Current offline RL research includes: 1) gen…
Learning to Route via Theory-Guided Residual Network
Chang Liu, Guanjie Zheng, Zhenhui Li
The heavy traffic and related issues have always been concerns for modern cities. With the help of deep learning and reinforcement learning, people have proposed various policies t…
Learning to Simulate on Sparse Trajectory Data
Hua Wei, Chacha Chen, Chang Liu +2
Simulation of the real-world traffic can be used to help validate the transportation policies. A good simulator means the simulated traffic is similar to real-world traffic, which…
Automatic Historical Feature Generation through Tree-based Method in Ads Prediction
Hongjian Wang, Qi Li, Lanbo Zhang +4
Historical features are important in ads click-through rate (CTR) prediction, because they account for past engagements between users and ads. In this paper, we study how to effici…
Online Structured Meta-learning
Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi +3
Learning quickly is of great importance for machine intelligence deployed in online platforms. With the capability of transferring knowledge from learned tasks, meta-learning has s…
Relation-aware Meta-learning for Market Segment Demand Prediction with Limited Records
Jiatu Shi, Huaxiu Yao, Xian Wu +4
E-commerce business is revolutionizing our shopping experiences by providing convenient and straightforward services. One of the most fundamental problems is how to balance the dem…
Improving Generalization in Meta-learning via Task Augmentation
Huaxiu Yao, Longkai Huang, Linjun Zhang +5
Meta-learning has proven to be a powerful paradigm for transferring the knowledge from previous tasks to facilitate the learning of a novel task. Current dominant algorithms train…
Automated Relational Meta-learning
Huaxiu Yao, Xian Wu, Zhiqiang Tao +4
In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones. However, a critical challenge i…
Graph Few-shot Learning via Knowledge Transfer
Huaxiu Yao, Chuxu Zhang, Ying Wei +5
Towards the challenging problem of semi-supervised node classification, there have been extensive studies. As a frontier, Graph Neural Networks (GNNs) have aroused great interest r…
Learning Phase Competition for Traffic Signal Control
Guanjie Zheng, Yuanhao Xiong, Xinshi Zang +6
Increasingly available city data and advanced learning techniques have empowered people to improve the efficiency of our city functions. Among them, improving the urban transportat…
Diagnosing Reinforcement Learning for Traffic Signal Control
Guanjie Zheng, Xinshi Zang, Nan Xu +5
With the increasing availability of traffic data and advance of deep reinforcement learning techniques, there is an emerging trend of employing reinforcement learning (RL) for traf…
Hierarchically Structured Meta-learning
Huaxiu Yao, Ying Wei, Junzhou Huang +1
In order to learn quickly with few samples, meta-learning utilizes prior knowledge learned from previous tasks. However, a critical challenge in meta-learning is task uncertainty a…
A Survey on Traffic Signal Control Methods
Hua Wei, Guanjie Zheng, Vikash Gayah +1
Traffic signal control is an important and challenging real-world problem, which aims to minimize the travel time of vehicles by coordinating their movements at the road intersecti…
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
Huaxiu Yao, Yiding Liu, Ying Wei +2
Spatial-temporal prediction is a fundamental problem for constructing smart city, which is useful for tasks such as traffic control, taxi dispatching, and environmental policy maki…
Detecting Outliers in Data with Correlated Measures
Yu-Hsuan Kuo, Zhenhui Li, Daniel Kifer
Advances in sensor technology have enabled the collection of large-scale datasets. Such datasets can be extremely noisy and often contain a significant amount of outliers that resu…
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction
Huaxiu Yao, Xianfeng Tang, Hua Wei +2
Traffic prediction has drawn increasing attention in AI research field due to the increasing availability of large-scale traffic data and its importance in the real world. For exam…
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Huaxiu Yao, Fei Wu, Jintao Ke +6
Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate re…
A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data
Hongjian Wang, Zhenhui Li, Yu-Hsuan Kuo +1
The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics. For example, New York City Taxi Limousine Comm…
Generalized Fisher Score for Feature Selection
Quanquan Gu, Zhenhui Li, Jiawei Han
Fisher score is one of the most widely used supervised feature selection methods. However, it selects each feature independently according to their scores under the Fisher criterio…