Search Results for author:
Found 58 papers, 26 papers with code
VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections
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.
Automated Contrastive Learning Strategy Search for Time Series
In recent years, Contrastive Learning (CL) has become a predominant representation learning paradigm for time series.
Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs
Through the utilization of both knowledge graph reasoning and LLMs, it successfully derives answers for each subquestion.
Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond
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.
Multi-modal Causal Structure Learning and Root Cause Analysis
Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses, and ensuring the smooth operation and management of complex systems.
Neural Contextual Bandits for Personalized Recommendation
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.
Contextual Bandits with Online Neural Regression
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.
Dataset Distillation via the Wasserstein Metric
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.
Robust Basket Recommendation via Noise-tolerated Graph Contrastive Learning
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.
Adaptive Test-Time Personalization for Federated Learning
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.
Topological Augmentation for Class-Imbalanced Node Classification
Class imbalance is prevalent in real-world node classification tasks and often biases graph learning models toward majority classes.
Graph Neural Bandits
Contextual bandits algorithms aim to choose the optimal arm with the highest reward out of a set of candidates based on the contextual information.
NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning
Fine-tuning a pre-trained language model (PLM) emerges as the predominant strategy in many natural language processing applications.
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey
We systematically review related works from the data to the computational aspects.
Optimizing the Collaboration Structure in Cross-Silo Federated Learning
In federated learning (FL), multiple clients collaborate to train machine learning models together while keeping their data decentralized.
Neural Exploitation and Exploration of Contextual Bandits
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.
FairGen: Towards Fair Graph Generation
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.
Fairness-aware Multi-view Clustering
We also derive insights regarding the relative performance of the proposed regularizers in various scenarios.
Non-IID Transfer Learning on Graphs
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.
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
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).
Improved Algorithms for Neural Active Learning
We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting.
BOBA: Byzantine-Robust Federated Learning with Label Skewness
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.
MentorGNN: Deriving Curriculum for Pre-Training GNNs
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.
A Unified Meta-Learning Framework for Dynamic Transfer Learning
Transfer learning refers to the transfer of knowledge or information from a relevant source task to a target task.
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning
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.
Fairness-aware Model-agnostic Positive and Unlabeled Learning
Our framework is proven to be statistically consistent in terms of both the classification error and the fairness metric.
Comprehensive Fair Meta-learned Recommender System
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.
Neural Bandit with Arm Group Graph
Contextual bandits aim to identify among a set of arms the optimal one with the highest reward based on their contextual information.
DISCO: Comprehensive and Explainable Disinformation Detection
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.
Neural Collaborative Filtering Bandits via Meta Learning
Contextual multi-armed bandits provide powerful tools to solve the exploitation-exploration dilemma in decision making, with direct applications in the personalized recommendation.
FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes
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
Plant phenotyping (Guo et al. 2021; Pieruschka et al. 2019) focuses on studying the diverse traits of plants related to the plants' growth.
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications.
Deeper-GXX: Deepening Arbitrary GNNs
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
Deep Active Learning by Leveraging Training Dynamics
Active learning theories and methods have been extensively studied in classical statistical learning settings.
EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits
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.
Metric Learning on Temporal Graphs via Few-Shot Examples
Graph metric learning methods aim to learn the distance metric over graphs such that similar graphs are closer and dissimilar graphs are farther apart.
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data
In the big data era, the relationship between entries becomes more and more complex.
Convolutional Neural Bandit for Visual-aware Recommendation
Online recommendation/advertising is ubiquitous in web business.
Multi-facet Contextual Bandits: A Neural Network Perspective
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.
Controllable Gradient Item Retrieval
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.
Heterogeneous Contrastive Learning
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.
Local Clustering in Contextual Multi-Armed Bandits
We study identifying user clusters in contextual multi-armed bandits (MAB).
Deep Co-Attention Network for Multi-View Subspace Learning
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.
Robust Federated Learning for Neural Networks
In federated learning, data is distributed among local clients which collaboratively train a prediction model using secure aggregation.
Continuous Transfer Learning
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.
GAN-based Recommendation with Positive-Unlabeled Sampling
Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products.
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning
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.
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes
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.
Generic Outlier Detection in Multi-Armed Bandit
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.
Continuous Transfer Learning with Label-informed Distribution Alignment
To bridge this gap, in this paper, we study a novel continuous transfer learning setting with a time evolving target domain.
DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
Graph data widely exist in many high-impact applications.
Ranked #1 on
Node Classification
on BlogCatalog
Visual Analytics of Anomalous User Behaviors: A Survey
The increasing accessibility of data provides substantial opportunities for understanding user behaviors.
Deep Multimodality Model for Multi-task Multi-view Learning
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.).
Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching
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
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
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
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.
