430 citations · 1.4k across the 24 of their papers we have counts for
24 papers
AutoLoss: Automated Loss Function Search in Recommendations
Xiangyu Zhao, Haochen Liu, Wenqi Fan +3
Designing an effective loss function plays a crucial role in training deep recommender systems. Most existing works often leverage a predefined and fixed loss function that could l…
Automated Self-Supervised Learning for Graphs
Wei Jin, Xiaorui Liu, Xiangyu Zhao +3
Graph self-supervised learning has gained increasing attention due to its capacity to learn expressive node representations. Many pretext tasks, or loss functions have been designe…
Data-Efficient Reinforcement Learning for Malaria Control
Lixin Zou, Long Xia, Linfang Hou +2
Sequential decision-making under cost-sensitive tasks is prohibitively daunting, especially for the problem that has a significant impact on people's daily lives, such as malaria c…
D2A U-Net: Automatic Segmentation of COVID-19 Lesions from CT Slices with Dilated Convolution and Dual Attention Mechanism
Xiangyu Zhao, Peng Zhang, Fan Song +6
Coronavirus Disease 2019 (COVID-19) has caused great casualties and becomes almost the most urgent public health events worldwide. Computed tomography (CT) is a significant screeni…
Contrastive Learning for Label-Efficient Semantic Segmentation
Xiangyun Zhao, Raviteja Vemulapalli, Philip Mansfield +4
Collecting labeled data for the task of semantic segmentation is expensive and time-consuming, as it requires dense pixel-level annotations. While recent Convolutional Neural Netwo…
Watermarking Graph Neural Networks by Random Graphs
Xiangyu Zhao, Hanzhou Wu, Xinpeng Zhang
Many learning tasks require us to deal with graph data which contains rich relational information among elements, leading increasing graph neural network (GNN) models to be deploye…
Object Detection with a Unified Label Space from Multiple Datasets
Xiangyun Zhao, Samuel Schulter, Gaurav Sharma +3
Given multiple datasets with different label spaces, the goal of this work is to train a single object detector predicting over the union of all the label spaces. The practical ben…
Neural Interactive Collaborative Filtering
Lixin Zou, Long Xia, Yulong Gu +4
In this paper, we study collaborative filtering in an interactive setting, in which the recommender agents iterate between making recommendations and updating the user profile base…
Memory-efficient Embedding for Recommendations
Xiangyu Zhao, Haochen Liu, Hui Liu +6
Practical large-scale recommender systems usually contain thousands of feature fields from users, items, contextual information, and their interactions. Most of them empirically al…
Jointly Learning to Recommend and Advertise
Xiangyu Zhao, Xudong Zheng, Xiwang Yang +2
Online recommendation and advertising are two major income channels for online recommendation platforms (e.g. e-commerce and news feed site). However, most platforms optimize recom…
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
Xiangyu Zhao, Chong Wang, Ming Chen +3
Deep learning based recommender systems (DLRSs) often have embedding layers, which are utilized to lessen the dimensionality of categorical variables (e.g. user/item identifiers) a…
Exploring Spatio-Temporal and Cross-Type Correlations for Crime Prediction
Xiangyu Zhao, Jiliang Tang
Crime prediction plays an impactful role in enhancing public security and sustainable development of urban. With recent advances in data collection and integration technologies, a…
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems
Xiangyu Zhao, Changsheng Gu, Haoshenglun Zhang +4
With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in utilizing RL for online advertising in recommendation platforms (e.g., e-commerce…
Recognizing Part Attributes with Insufficient Data
Xiangyun Zhao, Yi Yang, Feng Zhou +4
Recognizing attributes of objects and their parts is important to many computer vision applications. Although great progress has been made to apply object-level recognition, recogn…
Toward Simulating Environments in Reinforcement Learning Based Recommendations
Xiangyu Zhao, Long Xia, Lixin Zou +2
With the recent advances in Reinforcement Learning (RL), there have been tremendous interests in employing RL for recommender systems. However, directly training and evaluating a n…
Whole-Chain Recommendations
Xiangyu Zhao, Long Xia, Linxin Zou +3
With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, us…
Deep reinforcement learning for search, recommendation, and online advertising: a survey
Xiangyu Zhao, Long Xia, Jiliang Tang +1
Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web. These information seeking techniques, satisfying users' inf…
A Modulation Module for Multi-task Learning with Applications in Image Retrieval
Xiangyun Zhao, Haoxiang Li, Xiaohui Shen +2
Multi-task learning has been widely adopted in many computer vision tasks to improve overall computation efficiency or boost the performance of individual tasks, under the assumpti…
Crime in Urban Areas: A Data Mining Perspective
Xiangyu Zhao, Jiliang Tang
Urban safety and security play a crucial role in improving life quality of citizen and the sustainable development of urban. Traditional urban crime research focused on leveraging…
Deep Reinforcement Learning for Page-wise Recommendations
Xiangyu Zhao, Long Xia, Liang Zhang +3
Recommender systems can mitigate the information overload problem by suggesting users' personalized items. In real-world recommendations such as e-commerce, a typical interaction b…
Pseudo Mask Augmented Object Detection
Xiangyun Zhao, Shuang Liang, Yichen Wei
In this work, we present a novel and effective framework to facilitate object detection with the instance-level segmentation information that is only supervised by bounding box ann…
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
Xiangyu Zhao, Liang Zhang, Zhuoye Ding +3
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional reco…
Deep Reinforcement Learning for List-wise Recommendations
Xiangyu Zhao, Liang Zhang, Long Xia +3
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional reco…
Peak-Piloted Deep Network for Facial Expression Recognition
Xiangyun Zhao, Xiaodan Liang, Luoqi Liu +4
Objective functions for training of deep networks for face-related recognition tasks, such as facial expression recognition (FER), usually consider each sample independently. In th…