Search Results for author: Jingrui He

Found 54 papers, 24 papers with code

Multi-modal Causal Structure Learning and Root Cause Analysis

no code implementations4 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.

Causal Discovery Contrastive Learning +2

Neural Contextual Bandits for Personalized Recommendation

no code implementations21 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.

Multi-Armed Bandits Recommendation Systems

Contextual Bandits with Online Neural Regression

no code implementations12 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.

Multi-Armed Bandits regression

Dataset Distillation via the Wasserstein Metric

no code implementations30 Nov 2023 Haoyang Liu, Tiancheng Xing, Luwei Li, Vibhu Dalal, Jingrui He, Haohan Wang

Dataset distillation (DD) offers a compelling approach in computer vision, with the goal of condensing extensive datasets into smaller synthetic versions without sacrificing much of the model performance.

Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning

1 code implementation27 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.

Contrastive Learning Recommendation Systems

Adaptive Test-Time Personalization for Federated 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.

Personalized Federated Learning Test

Topological Augmentation for Class-Imbalanced Node Classification

no code implementations27 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.

Classification Graph Learning +1

Graph Neural Bandits

no code implementations21 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.

Multi-Armed Bandits

NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning

1 code implementation18 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.

Language Modelling Natural Language Understanding +1

Optimizing the Collaboration Structure in Cross-Silo Federated Learning

1 code implementation10 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.

Federated Learning

Neural Exploitation and Exploration of Contextual Bandits

no code implementations5 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.

Multi-Armed Bandits Thompson Sampling

FairGen: Towards Fair Graph Generation

no code implementations30 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.

Data Augmentation Fairness +3

Fairness-aware Multi-view Clustering

1 code implementation11 Feb 2023 Lecheng Zheng, Yada Zhu, Jingrui He

We also derive insights regarding the relative performance of the proposed regularizers in various scenarios.

Clustering Contrastive Learning +2

Non-IID Transfer Learning on Graphs

1 code implementation15 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.

Generalization Bounds Link Prediction +2

Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative

1 code implementation7 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).

Contrastive Learning Fairness +1

Improved Algorithms for Neural Active Learning

1 code implementation2 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.

Active Learning

BOBA: Byzantine-Robust Federated Learning with Label Skewness

no code implementations27 Aug 2022 Wenxuan Bao, Jingrui He

In federated learning, most existing techniques for robust aggregation against Byzantine attacks are designed for the IID setting, i. e., the data distributions for clients are independent and identically distributed.

Federated Learning Selection bias

MentorGNN: Deriving Curriculum for Pre-Training GNNs

1 code implementation21 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.

Domain Adaptation Graph Mining

A Unified Meta-Learning Framework for Dynamic Transfer Learning

1 code implementation5 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.

Meta-Learning Transfer Learning

Privacy-preserving Graph Analytics: Secure Generation and Federated Learning

no code implementations30 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.

Federated Learning Graph Generation +2

Fairness-aware Model-agnostic Positive and Unlabeled Learning

no code implementations19 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.

Binary Classification Decision Making +3

Comprehensive Fair Meta-learned Recommender System

1 code implementation9 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.

counterfactual Fairness +3

Neural Bandit with Arm Group Graph

no code implementations8 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.

Multi-Armed Bandits

DISCO: Comprehensive and Explainable Disinformation Detection

1 code implementation9 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.

Fake News Detection

Neural Collaborative Filtering Bandits via Meta Learning

no code implementations31 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.

Collaborative Filtering Decision Making +2

FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes

no code implementations15 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.


Adaptive Transfer Learning for Plant Phenotyping

no code implementations14 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.

BIG-bench Machine Learning GPR +3

From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems

no code implementations28 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.

counterfactual Explainable Models +2

Deeper-GXX: Deepening Arbitrary GNNs

no code implementations26 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.

Contrastive Learning Link Prediction +2

Deep Active Learning by Leveraging Training Dynamics

no code implementations16 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.

Active Learning

EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits

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.

Multi-Armed Bandits Thompson Sampling

Metric Learning on Temporal Graphs via Few-Shot Examples

no code implementations29 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.

Drug Discovery Graph Classification +2

Convolutional Neural Bandit for Visual-aware Recommendation

no code implementations2 Jul 2021 Yikun Ban, Jingrui He

Online recommendation/advertising is ubiquitous in web business.

Multi-facet Contextual Bandits: A Neural Network Perspective

1 code implementation6 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.

Multi-Armed Bandits Recommendation Systems

Controllable Gradient Item Retrieval

1 code implementation31 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.

Attribute Disentanglement +1

Heterogeneous Contrastive Learning

1 code implementation19 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.

Contrastive Learning

Local Clustering in Contextual Multi-Armed Bandits

no code implementations26 Feb 2021 Yikun Ban, Jingrui He

We study identifying user clusters in contextual multi-armed bandits (MAB).

Clustering Multi-Armed Bandits

Deep Co-Attention Network for Multi-View Subspace Learning

1 code implementation15 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.

Continuous Transfer Learning

no code implementations1 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.

Transfer Learning

Robust Federated Learning for Neural Networks

no code implementations1 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.

Federated Learning

GAN-based Recommendation with Positive-Unlabeled Sampling

no code implementations12 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.

Generative Adversarial Network Information Retrieval +2

Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning

1 code implementation18 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.

Adversarial Robustness Federated Learning +1

A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes

1 code implementation15 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.

Descriptive Image Classification +1

Generic Outlier Detection in Multi-Armed Bandit

no code implementations14 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.

Outlier Detection

Continuous Transfer Learning with Label-informed Distribution Alignment

no code implementations5 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.

Transfer Learning

Visual Analytics of Anomalous User Behaviors: A Survey

no code implementations14 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.

Anomaly Detection

Deep Multimodality Model for Multi-task Multi-view Learning

1 code implementation25 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.).

General Classification Image Classification +1

Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching

no code implementations23 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.

ImVerde: Vertex-Diminished Random Walk for Learning Network Representation from Imbalanced Data

1 code implementation24 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

Unlearn What You Have Learned: Adaptive Crowd Teaching with Exponentially Decayed Memory Learners

1 code implementation17 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.

GenDeR: A Generic Diversified Ranking Algorithm

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.

Information Retrieval Retrieval

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