no code implementations • 24 Mar 2024 • Dongqi Fu, Zhigang Hua, Yan Xie, Jin Fang, Si Zhang, Kaan Sancak, Hao Wu, Andrey Malevich, Jingrui He, Bo Long
Therefore, mini-batch training for graph transformers is a promising direction, but limited samples in each mini-batch can not support effective dense attention to encode informative representations.
no code implementations • 19 Mar 2024 • Baoyu Jing, Yansen Wang, Guoxin Sui, Jing Hong, Jingrui He, Yuqing Yang, Dongsheng Li, Kan Ren
In recent years, Contrastive Learning (CL) has become a predominant representation learning paradigm for time series.
no code implementations • 17 Mar 2024 • Lihui Liu, ZiHao Wang, Ruizhong Qiu, Yikun Ban, Eunice Chan, Yangqiu Song, Jingrui He, Hanghang Tong
Through the utilization of both knowledge graph reasoning and LLMs, it successfully derives answers for each subquestion.
no code implementations • 15 Mar 2024 • Tianxin Wei, Bowen Jin, Ruirui Li, Hansi Zeng, Zhengyang Wang, Jianhui Sun, Qingyu Yin, Hanqing Lu, Suhang Wang, Jingrui He, Xianfeng Tang
Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration.
no code implementations • 4 Feb 2024 • Lecheng Zheng, Zhengzhang Chen, Jingrui He, Haifeng Chen
Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses, and ensuring the smooth operation and management of complex systems.
no code implementations • 21 Dec 2023 • Yikun Ban, Yunzhe Qi, Jingrui He
Different from existing related tutorials, (1) we focus on the exploration perspective of contextual bandits to alleviate the ``Matthew Effect'' in the recommender systems, i. e., the rich get richer and the poor get poorer, concerning the popularity of items; (2) in addition to the conventional linear contextual bandits, we will also dedicated to neural contextual bandits which have emerged as an important branch in recent years, to investigate how neural networks benefit contextual bandits for personalized recommendation both empirically and theoretically; (3) we will cover the latest topic, collaborative neural contextual bandits, to incorporate both user heterogeneity and user correlations customized for recommender system; (4) we will provide and discuss the new emerging challenges and open questions for neural contextual bandits with applications in the personalized recommendation, especially for large neural models.
no code implementations • 12 Dec 2023 • Rohan Deb, Yikun Ban, Shiliang Zuo, Jingrui He, Arindam Banerjee
Based on such a perturbed prediction, we show a ${\mathcal{O}}(\log T)$ regret for online regression with both squared loss and KL loss, and subsequently convert these respectively to $\tilde{\mathcal{O}}(\sqrt{KT})$ and $\tilde{\mathcal{O}}(\sqrt{KL^*} + K)$ regret for NeuCB, where $L^*$ is the loss of the best policy.
no code implementations • 30 Nov 2023 • Haoyang Liu, Yijiang Li, Tiancheng Xing, Vibhu Dalal, Luwei Li, Jingrui He, Haohan Wang
Dataset Distillation (DD) emerges as a powerful strategy to encapsulate the expansive information of large datasets into significantly smaller, synthetic equivalents, thereby preserving model performance with reduced computational overhead.
1 code implementation • 27 Nov 2023 • Xinrui He, Tianxin Wei, Jingrui He
Next, to further inhibit the within-behavior noise of the user and basket interactions, we propose to exploit invariant properties of the recommenders w. r. t augmentations through within-behavior contrastive learning.
1 code implementation • NeurIPS 2023 • Wenxuan Bao, Tianxin Wei, Haohan Wang, Jingrui He
To tackle this challenge, we propose a novel algorithm called ATP to adaptively learns the adaptation rates for each module in the model from distribution shifts among source domains.
no code implementations • 27 Aug 2023 • Zhining Liu, Zhichen Zeng, Ruizhong Qiu, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, Hanghang Tong
Class imbalance is prevalent in real-world node classification tasks and often biases graph learning models toward majority classes.
no code implementations • 21 Aug 2023 • Yunzhe Qi, Yikun Ban, Jingrui He
Contextual bandits algorithms aim to choose the optimal arm with the highest reward out of a set of candidates based on the contextual information.
1 code implementation • 18 Jul 2023 • Tianxin Wei, Zeming Guo, Yifan Chen, Jingrui He
Fine-tuning a pre-trained language model (PLM) emerges as the predominant strategy in many natural language processing applications.
no code implementations • 10 Jul 2023 • Dongqi Fu, Wenxuan Bao, Ross Maciejewski, Hanghang Tong, Jingrui He
We systematically review related works from the data to the computational aspects.
1 code implementation • 10 Jun 2023 • Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He
In federated learning (FL), multiple clients collaborate to train machine learning models together while keeping their data decentralized.
1 code implementation • 5 May 2023 • Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He
In recent literature, a series of neural bandit algorithms have been proposed to adapt to the non-linear reward function, combined with TS or UCB strategies for exploration.
no code implementations • 30 Mar 2023 • Lecheng Zheng, Dawei Zhou, Hanghang Tong, Jiejun Xu, Yada Zhu, Jingrui He
In addition, we propose a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability.
1 code implementation • 11 Feb 2023 • Lecheng Zheng, Yada Zhu, Jingrui He
We also derive insights regarding the relative performance of the proposed regularizers in various scenarios.
1 code implementation • 15 Dec 2022 • Jun Wu, Jingrui He, Elizabeth Ainsworth
To bridge the gap, in this paper, we propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph.
1 code implementation • 7 Oct 2022 • Tianxin Wei, Yuning You, Tianlong Chen, Yang shen, Jingrui He, Zhangyang Wang
This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (we refer to it as HyperGCL).
1 code implementation • 2 Oct 2022 • Yikun Ban, Yuheng Zhang, Hanghang Tong, Arindam Banerjee, Jingrui He
We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting.
1 code implementation • 27 Aug 2022 • Wenxuan Bao, Jun Wu, Jingrui He
In federated learning, most existing robust aggregation rules (AGRs) combat Byzantine attacks in the IID setting, where client data is assumed to be independent and identically distributed.
1 code implementation • 21 Aug 2022 • Dawei Zhou, Lecheng Zheng, Dongqi Fu, Jiawei Han, Jingrui He
To comprehend heterogeneous graph signals at different granularities, we propose a curriculum learning paradigm that automatically re-weighs graph signals in order to ensure a good generalization in the target domain.
1 code implementation • 5 Jul 2022 • Jun Wu, Jingrui He
Transfer learning refers to the transfer of knowledge or information from a relevant source task to a target task.
no code implementations • 30 Jun 2022 • Dongqi Fu, Jingrui He, Hanghang Tong, Ross Maciejewski
Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial capacity to represent rich attributes and relationships.
no code implementations • 19 Jun 2022 • Ziwei Wu, Jingrui He
Our framework is proven to be statistically consistent in terms of both the classification error and the fairness metric.
1 code implementation • 9 Jun 2022 • Tianxin Wei, Jingrui He
The core idea is to learn global shared meta-initialization parameters for all users and rapidly adapt them into local parameters for each user respectively.
no code implementations • 8 Jun 2022 • Yunzhe Qi, Yikun Ban, Jingrui He
Contextual bandits aim to identify among a set of arms the optimal one with the highest reward based on their contextual information.
1 code implementation • 9 Mar 2022 • Dongqi Fu, Yikun Ban, Hanghang Tong, Ross Maciejewski, Jingrui He
Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed in numerous issues, such as political agendas and manipulating financial markets.
no code implementations • 31 Jan 2022 • Yikun Ban, Yunzhe Qi, Tianxin Wei, Jingrui He
Contextual multi-armed bandits provide powerful tools to solve the exploitation-exploration dilemma in decision making, with direct applications in the personalized recommendation.
no code implementations • 15 Jan 2022 • Haonan Wang, Ziwei Wu, Jingrui He
Most fair machine learning methods either highly rely on the sensitive information of the training samples or require a large modification on the target models, which hinders their practical application.
no code implementations • 14 Jan 2022 • Jun Wu, Elizabeth A. Ainsworth, Sheng Wang, Kaiyu Guan, Jingrui He
Plant phenotyping (Guo et al. 2021; Pieruschka et al. 2019) focuses on studying the diverse traits of plants related to the plants' growth.
no code implementations • 28 Oct 2021 • Yao Zhou, Haonan Wang, Jingrui He, Haixun Wang
With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications.
no code implementations • 26 Oct 2021 • Lecheng Zheng, Dongqi Fu, Ross Maciejewski, Jingrui He
However, two major problems hinder the deeper GNNs to obtain satisfactory performance, i. e., vanishing gradient and over-smoothing.
Ranked #15 on Node Classification on Reddit
no code implementations • 16 Oct 2021 • Haonan Wang, Wei Huang, Ziwei Wu, Andrew Margenot, Hanghang Tong, Jingrui He
Active learning theories and methods have been extensively studied in classical statistical learning settings.
1 code implementation • ICLR 2022 • Yikun Ban, Yuchen Yan, Arindam Banerjee, Jingrui He
To overcome this challenge, a series of neural bandit algorithms have been proposed, where a neural network is used to learn the underlying reward function and TS or UCB are adapted for exploration.
no code implementations • 29 Sep 2021 • Dongqi Fu, Liri Fang, Ross Maciejewski, Vetle I Torvik, Jingrui He
Graph metric learning methods aim to learn the distance metric over graphs such that similar graphs are closer and dissimilar graphs are farther apart.
1 code implementation • 5 Jul 2021 • Dongqi Fu, Jingrui He
In the big data era, the relationship between entries becomes more and more complex.
no code implementations • 2 Jul 2021 • Yikun Ban, Jingrui He
Online recommendation/advertising is ubiquitous in web business.
1 code implementation • 6 Jun 2021 • Yikun Ban, Jingrui He, Curtiss B. Cook
In this paper, we study a novel problem of multi-facet bandits involving a group of bandits, each characterizing the users' needs from one unique aspect.
1 code implementation • 31 May 2021 • Haonan Wang, Chang Zhou, Carl Yang, Hongxia Yang, Jingrui He
A better way is to present a sequence of products with increasingly floral attributes based on the white dress, and allow the customer to select the most satisfactory one from the sequence.
1 code implementation • 19 May 2021 • Lecheng Zheng, JinJun Xiong, Yada Zhu, Jingrui He
We first provide a theoretical analysis showing that the vanilla contrastive learning loss easily leads to the sub-optimal solution in the presence of false negative pairs, whereas the proposed weighted loss could automatically adjust the weight based on the similarity of the learned representations to mitigate this issue.
no code implementations • 26 Feb 2021 • Yikun Ban, Jingrui He
We study identifying user clusters in contextual multi-armed bandits (MAB).
1 code implementation • 15 Feb 2021 • Lecheng Zheng, Yu Cheng, Hongxia Yang, Nan Cao, Jingrui He
For example, given the diagnostic result that a model provided based on the X-ray images of a patient at different poses, the doctor needs to know why the model made such a prediction.
no code implementations • 1 Jan 2021 • Yao Zhou, Jun Wu, Jingrui He
In federated learning, data is distributed among local clients which collaboratively train a prediction model using secure aggregation.
no code implementations • 1 Jan 2021 • Jun Wu, Jingrui He
One major challenge associated with continuous transfer learning is the time evolving relatedness of the source domain and the current target domain as the target domain evolves over time.
no code implementations • 12 Dec 2020 • Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi Nasrabadi, Evren Korpeoglu, Kannan Achan, Jingrui He
Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products.
1 code implementation • 18 Sep 2020 • Yao Zhou, Jun Wu, Haixun Wang, Jingrui He
In this work, we show that this paradigm might inherit the adversarial vulnerability of the centralized neural network, i. e., it has deteriorated performance on adversarial examples when the model is deployed.
1 code implementation • 15 Sep 2020 • Yuxin Ma, Arlen Fan, Jingrui He, Arun Reddy Nelakurthi, Ross Maciejewski
Transfer Learning is intended to relax this assumption by modeling relationships between domains, and is often applied in deep learning applications to reduce the demand for labeled data and training time.
no code implementations • 14 Jul 2020 • Yikun Ban, Jingrui He
In this paper, we study the problem of outlier arm detection in multi-armed bandit settings, which finds plenty of applications in many high-impact domains such as finance, healthcare, and online advertising.
no code implementations • 5 Jun 2020 • Jun Wu, Jingrui He
To bridge this gap, in this paper, we study a novel continuous transfer learning setting with a time evolving target domain.
1 code implementation • 5 Jun 2019 • Jun Wu, Jingrui He, Jiejun Xu
Graph data widely exist in many high-impact applications.
Ranked #1 on Node Classification on BlogCatalog
no code implementations • 14 May 2019 • Yang Shi, Yuyin Liu, Hanghang Tong, Jingrui He, Gang Yan, Nan Cao
The increasing accessibility of data provides substantial opportunities for understanding user behaviors.
1 code implementation • 25 Jan 2019 • Lecheng Zheng, Yu Cheng, Jingrui He
However, there is no existing deep learning algorithm that jointly models task and view dual heterogeneity, particularly for a data set with multiple modalities (text and image mixed data set or text and video mixed data set, etc.).
no code implementations • 23 Jun 2018 • Yao Zhou, Jingrui He
The unprecedented demand for large amount of data has catalyzed the trend of combining human insights with machine learning techniques, which facilitate the use of crowdsourcing to enlist label information both effectively and efficiently.
1 code implementation • 24 Apr 2018 • Jun Wu, Jingrui He, Yongming Liu
Then, based on VDRW, we propose a semi-supervised network representation learning framework named ImVerde for imbalanced networks, in which context sampling uses VDRW and the label information to create node-context pairs, and balanced-batch sampling adopts a simple under-sampling method to balance these pairs in different classes.
Social and Information Networks
1 code implementation • 17 Apr 2018 • Yao Zhou, Arun Reddy Nelakurthi, Jingrui He
With the increasing demand for large amount of labeled data, crowdsourcing has been used in many large-scale data mining applications.
no code implementations • NeurIPS 2012 • Jingrui He, Hanghang Tong, Qiaozhu Mei, Boleslaw Szymanski
In this paper, we consider a generic setting where we aim to diversify the top-k ranking list based on an arbitrary relevance function and an arbitrary similarity function among all the examples.