Search Results for author: Hengrui Zhang

Found 19 papers, 9 papers with code

DRL4Route: A Deep Reinforcement Learning Framework for Pick-up and Delivery Route Prediction

1 code implementation30 Jul 2023 Xiaowei Mao, Haomin Wen, Hengrui Zhang, Huaiyu Wan, Lixia Wu, Jianbin Zheng, Haoyuan Hu, Youfang Lin

Deep neural networks based on supervised learning have emerged as the dominant model for the task because of their powerful ability to capture workers' behavior patterns from massive historical data.

reinforcement-learning Reinforcement Learning (RL)

Simplifying and Empowering Transformers for Large-Graph Representations

no code implementations19 Jun 2023 Qitian Wu, Wentao Zhao, Chenxiao Yang, Hengrui Zhang, Fan Nie, Haitian Jiang, Yatao Bian, Junchi Yan

Learning representations on large-sized graphs is a long-standing challenge due to the inter-dependence nature involved in massive data points.

Node Property Prediction Philosophy +1

OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization

no code implementations31 Jan 2023 Hengrui Zhang, Shen Wang, Vassilis N. Ioannidis, Soji Adeshina, Jiani Zhang, Xiao Qin, Christos Faloutsos, Da Zheng, George Karypis, Philip S. Yu

Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.

Node Classification

Localized Contrastive Learning on Graphs

no code implementations8 Dec 2022 Hengrui Zhang, Qitian Wu, Yu Wang, Shaofeng Zhang, Junchi Yan, Philip S. Yu

Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data.

Contrastive Learning Data Augmentation +1

ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data

1 code implementation15 Nov 2022 Hengrui Zhang, Wei Wayne Chen, James M. Rondinelli, Wei Chen

To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems.

Active Learning Materials Screening

ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation

1 code implementation27 Aug 2022 Yu Wang, Hengrui Zhang, Zhiwei Liu, Liangwei Yang, Philip S. Yu

Then we propose Contrastive Variational AutoEncoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation.

Contrastive Learning Sequential Recommendation

Uncertainty-Aware Mixed-Variable Machine Learning for Materials Design

no code implementations11 Jul 2022 Hengrui Zhang, Wei Wayne Chen, Akshay Iyer, Daniel W. Apley, Wei Chen

Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods.

Bayesian Optimization BIG-bench Machine Learning

Handling Distribution Shifts on Graphs: An Invariance Perspective

1 code implementation ICLR 2022 Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf

There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so that research on out-of-distribution (OOD) generalization comes into the spotlight.

Conservative Distributional Reinforcement Learning with Safety Constraints

no code implementations18 Jan 2022 Hengrui Zhang, Youfang Lin, Sheng Han, Shuo Wang, Kai Lv

Then, CDMPO uses a conservative value function loss to reduce the number of violations of constraints during the exploration process.

Distributional Reinforcement Learning reinforcement-learning +1

Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective

no code implementations18 Oct 2021 Hengrui Zhang, Zhongming Yu, Guohao Dai, Guyue Huang, Yufei Ding, Yuan Xie, Yu Wang

The same data are propagated through the graph structure to perform the same neural operation multiple times in GNNs, leading to redundant computation which accounts for 92. 4% of total operators.

ESCo: Towards Provably Effective and Scalable Contrastive Representation Learning

no code implementations29 Sep 2021 Hengrui Zhang, Qitian Wu, Shaofeng Zhang, Junchi Yan, David Wipf, Philip S. Yu

In this paper, we propose ESCo (Effective and Scalable Contrastive), a new contrastive framework which is essentially an instantiation of the Information Bottleneck principle under self-supervised learning settings.

Contrastive Learning Representation Learning +1

CogDL: A Comprehensive Library for Graph Deep Learning

1 code implementation1 Mar 2021 Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Zhongming Yu, Hengrui Zhang, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Yuxiao Dong, Yang Yang, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, Jie Tang

In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.

Graph Classification Graph Embedding +5

Inductive Collaborative Filtering via Relation Graph Learning

no code implementations1 Jan 2021 Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Hongyuan Zha

In this paper, we propose an inductive collaborative filtering framework that learns a hidden relational graph among users from the rating matrix.

Collaborative Filtering Graph Learning +1

Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach

1 code implementation9 Jul 2020 Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Junchi Yan, Hongyuan Zha

The first model follows conventional matrix factorization which factorizes a group of key users' rating matrix to obtain meta latents.

Collaborative Filtering Matrix Completion +2

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