Search Results for author:
Found 119 papers, 51 papers with code
Evaluating and Mitigating Inherent Linguistic Bias of African American English through Inference
In this work, we conduct a pioneering study of the English variety use of African American English (AAE) in NLI task.
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
To test the potential of the dataset, we introduce three tasks in this work: (1) next-product recommendation, (2) next-product recommendation with domain shifts, and (3) next-product title generation.
Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs
The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding.
Recommender Systems in the Era of Large Language Models (LLMs)
As a result, recent studies have attempted to harness the power of LLMs to enhance recommender systems.
Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
Furthermore, new and diverse datasets have also been created to better evaluate the effectiveness of these new models.
Empowering Molecule Discovery for Molecule-Caption Translation with Large Language Models: A ChatGPT Perspective
Molecule discovery plays a crucial role in various scientific fields, advancing the design of tailored materials and drugs.
Ranked #1 on
Text-based de novo Molecule Generation
on ChEBI-20
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and certain heterophilic graphs.
DiffusionShield: A Watermark for Copyright Protection against Generative Diffusion Models
By detecting the watermark from generated images, copyright infringement can be exposed with evidence.
Towards Label Position Bias in Graph Neural Networks
In this work, we uncover a new bias - label position bias, which indicates that the node closer to the labeled nodes tends to perform better.
Single-Cell Multimodal Prediction via Transformers
The recent development of multimodal single-cell technology has made the possibility of acquiring multiple omics data from individual cells, thereby enabling a deeper understanding of cellular states and dynamics.
Toward Degree Bias in Embedding-Based Knowledge Graph Completion
It aims to predict unseen edges by learning representations for all the entities and relations in a KG.
Generative Diffusion Models on Graphs: Methods and Applications
Given the great success of diffusion models in image generation, increasing efforts have been made to leverage these techniques to advance graph generation in recent years.
Single Cells Are Spatial Tokens: Transformers for Spatial Transcriptomic Data Imputation
In particular, investigate the following two key questions: (1) $\textit{how to encode spatial information of cells in transformers}$, and (2) $\textit{ how to train a transformer for transcriptomic imputation}$.
Deep Learning in Single-Cell Analysis
Under each task, we describe the most recent developments in classical and deep learning methods and discuss their advantages and disadvantages.
Whole Page Unbiased Learning to Rank
To address the above challenges, we propose a Bias Agnostic whole-page unbiased Learning to rank algorithm, BAL, to automatically discover and mitigate the biases from multiple SERP features with no specific design.
Transferable Unlearnable Examples
The unlearnable strategies have been introduced to prevent third parties from training on the data without permission.
Towards Fair Classification against Poisoning Attacks
We demonstrate that the fairly trained classifiers can be greatly vulnerable to such poisoning attacks, with much worse accuracy & fairness trade-off, even when we apply some of the most effective defenses (originally proposed to defend traditional classification tasks).
Probabilistic Categorical Adversarial Attack & Adversarial Training
To tackle this problem, we propose Probabilistic Categorical Adversarial Attack (PCAA), which transfers the discrete optimization problem to a continuous problem that can be solved efficiently by Projected Gradient Descent.
Test-Time Training for Graph Neural Networks
To bridge such gap, in this work we introduce the first test-time training framework for GNNs to enhance the model generalization capacity for the graph classification task.
Empowering Graph Representation Learning with Test-Time Graph Transformation
In this work, we provide a data-centric view to tackle these issues and propose a graph transformation framework named GTrans which adapts and refines graph data at test time to achieve better performance.
Learning Representations for Hyper-Relational Knowledge Graphs
It motivates us to design a framework that utilizes multiple aggregators to learn representations for hyper-relational facts: one from the perspective of the base triple and the other one from the perspective of the qualifiers.
A Large Scale Search Dataset for Unbiased Learning to Rank
The unbiased learning to rank (ULTR) problem has been greatly advanced by recent deep learning techniques and well-designed debias algorithms.
pyKT: A Python Library to Benchmark Deep Learning based Knowledge Tracing Models
However, the success behind deep learning based knowledge tracing (DLKT) approaches is still left somewhat unknown and proper measurement and analysis of these DLKT approaches remain a challenge.
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective
In this paper, we propose a new perspective to look at the performance degradation of deep GNNs, i. e., feature overcorrelation.
Detecting Harmful Online Conversational Content towards LGBTQIA+ Individuals
Online discussions, panels, talk page edits, etc., often contain harmful conversational content i. e., hate speech, death threats and offensive language, especially towards certain demographic groups.
Condensing Graphs via One-Step Gradient Matching
However, existing approaches have their inherent limitations: (1) they are not directly applicable to graphs where the data is discrete; and (2) the condensation process is computationally expensive due to the involved nested optimization.
Alternately Optimized Graph Neural Networks
Extensive experiments demonstrate that the proposed method can achieve comparable or better performance with state-of-the-art baselines while it has significantly better computation and memory efficiency.
Defense Against Gradient Leakage Attacks via Learning to Obscure Data
However, federated learning is still under the risk of privacy leakage because of the existence of attackers who deliberately conduct gradient leakage attacks to reconstruct the client data.
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?
This suggests a conflation of scoring function design, loss function design, and MP in prior work, with promising insights regarding the scalability of state-of-the-art KGC methods today, as well as careful attention to more suitable MP designs for KGC tasks tomorrow.
Enhancing Adversarial Training with Feature Separability
Deep Neural Network (DNN) are vulnerable to adversarial attacks.
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society.
Graph Enhanced BERT for Query Understanding
In other words, GE-BERT can capture both the semantic information and the users' search behavioral information of queries.
Graph Neural Networks for Multimodal Single-Cell Data Integration
Recent advances in multimodal single-cell technologies have enabled simultaneous acquisitions of multiple omics data from the same cell, providing deeper insights into cellular states and dynamics.
An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering
Thus, graph pre-training has the great potential to alleviate data sparsity in GNN-based recommendations.
Graph Neural Networks with Adaptive Residual
Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks.
Graph Condensation for Graph Neural Networks
Given the prevalence of large-scale graphs in real-world applications, the storage and time for training neural models have raised increasing concerns.
Toward Annotator Group Bias in Crowdsourcing
We conduct experiments on both synthetic and real-world datasets.
Towards Feature Overcorrelation in Deeper Graph Neural Networks
In this paper, we observe a new issue in deeper GNNs, i. e., feature overcorrelation, and perform a thorough study to deepen our understanding on this issue.
What Truly Matters? Using Linguistic Cues for Analyzing the #BlackLivesMatter Movement and its Counter Protests: 2013 to 2020
Consequently, our findings highlight that social activism done by Black Lives Matter activists does not diverge from the social issues and topics involving police-brutality related and racially-motivated killings of Black individuals due to the shape of its topical graph that topics and conversations encircling the largest component directly relate to the topic of Black Lives Matter.
Graph Trend Filtering Networks for Recommendations
The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e. g., clicks, add-to-cart, purchases, etc.
Localized Graph Collaborative Filtering
These methods often make recommendations based on the learned user and item embeddings.
Decentralized Composite Optimization with Compression
Decentralized optimization and communication compression have exhibited their great potential in accelerating distributed machine learning by mitigating the communication bottleneck in practice.
Jointly Attacking Graph Neural Network and its Explanations
Despite the great success, recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs.
Imbalanced Adversarial Training with Reweighting
Adversarial training has been empirically proven to be one of the most effective and reliable defense methods against adversarial attacks.
Solving ESL Sentence Completion Questions via Pre-trained Neural Language Models
Sentence completion (SC) questions present a sentence with one or more blanks that need to be filled in, three to five possible words or phrases as options.
Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models
In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective study habits.
Trustworthy AI: A Computational Perspective
In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone's daily life and profoundly altering the course of human society.
Elastic Graph Neural Networks
While many existing graph neural networks (GNNs) have been proven to perform $\ell_2$-based graph smoothing that enforces smoothness globally, in this work we aim to further enhance the local smoothness adaptivity of GNNs via $\ell_1$-based graph smoothing.
AutoLoss: Automated Loss Function Search in Recommendations
Unlike existing algorithms, the proposed controller can adaptively generate the loss probabilities for different data examples according to their varied convergence behaviors.
Is Homophily a Necessity for Graph Neural Networks?
We find that this claim is not quite true, and in fact, GCNs can achieve strong performance on heterophilous graphs under certain conditions.
Automated Self-Supervised Learning for Graphs
Then we propose the AutoSSL framework which can automatically search over combinations of various self-supervised tasks.
Towards the Memorization Effect of Neural Networks in Adversarial Training
In this work, we study the effect of memorization in adversarial trained DNNs and disclose two important findings: (a) Memorizing atypical samples is only effective to improve DNN's accuracy on clean atypical samples, but hardly improve their adversarial robustness and (b) Memorizing certain atypical samples will even hurt the DNN's performance on typical samples.
Graph Feature Gating Networks
Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs.
The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classification
The results show that the text classification models trained under our proposed framework outperform traditional models significantly in terms of fairness, and also slightly in terms of classification performance.
Node Similarity Preserving Graph Convolutional Networks
Specifically, to balance information from graph structure and node features, we propose a feature similarity preserving aggregation which adaptively integrates graph structure and node features.
Personalized Multimodal Feedback Generation in Education
The automatic evaluation for school assignments is an important application of AI in the education field.
To be Robust or to be Fair: Towards Fairness in Adversarial Training
However, we find that adversarial training algorithms tend to introduce severe disparity of accuracy and robustness between different groups of data.
A Unified View on Graph Neural Networks as Graph Signal Denoising
In this work, we establish mathematically that the aggregation processes in a group of representative GNN models including GCN, GAT, PPNP, and APPNP can be regarded as (approximately) solving a graph denoising problem with a smoothness assumption.
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning
Extensive experiments on two real-world conversation datasets show that our framework significantly reduces gender bias in dialogue models while maintaining the response quality.
Representation Learning from Limited Educational Data with Crowdsourced Labels
Extensive experiments conducted on three real-world data sets demonstrate the superiority of our framework on learning representations from limited data with crowdsourced labels, comparing with various state-of-the-art baselines.
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily
Thus, in this paper, we perform the initial study about adversarial attacks on meta learning under the few-shot classification problem.
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
To address these challenges, in this paper, we propose OC4Seq, a multi-scale one-class recurrent neural network for detecting anomalies in discrete event sequences.
Linear Convergent Decentralized Optimization with Compression
Communication compression has become a key strategy to speed up distributed optimization.
Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks
Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective.
Memory-efficient Embedding for Recommendations
Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.
Self-supervised Learning on Graphs: Deep Insights and New Direction
Thus, we seek to harness SSL for GNNs to fully exploit the unlabeled data.
XGNN: Towards Model-Level Explanations of Graph Neural Networks
Furthermore, our experimental results indicate that the generated graphs can provide guidance on how to improve the trained GNNs.
Chat as Expected: Learning to Manipulate Black-box Neural Dialogue Models
Recently, neural network based dialogue systems have become ubiquitous in our increasingly digitalized society.
Customized Graph Neural Networks
Therefore, in this paper, we aim to develop customized graph neural networks for graph classification.
Graph Structure Learning for Robust Graph Neural Networks
A natural idea to defend adversarial attacks is to clean the perturbed graph.
Attacking Black-box Recommendations via Copying Cross-domain User Profiles
In this work, we present our framework CopyAttack, which is a reinforcement learning based black-box attack method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items.
Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms
Asking questions is one of the most crucial pedagogical techniques used by teachers in class.
Siamese Neural Networks for Class Activity Detection
Classroom activity detection (CAD) aims at accurately recognizing speaker roles (either teacher or student) in classrooms.
DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses
DeepRobust is a PyTorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field.
Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies
As the extensions of DNNs to graphs, Graph Neural Networks (GNNs) have been demonstrated to inherit this vulnerability.
Jointly Learning to Recommend and Advertise
Online recommendation and advertising are two major income channels for online recommendation platforms (e. g. e-commerce and news feed site).
AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations
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) and meaningfully transform them in the low-dimensional space.
Social Science Guided Feature Engineering: A Novel Approach to Signed Link Analysis
The existence of negative links piques research interests in investigating whether properties and principles of signed networks differ from those of unsigned networks, and mandates dedicated efforts on link analysis for signed social networks.
Graduate Employment Prediction with Bias
The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide.
Characterizing the Decision Boundary of Deep Neural Networks
In this regard, one crucial aspect of deep neural network classifiers that can help us deepen our knowledge about their decision-making behavior is to investigate their decision boundaries.
Learning Multi-level Dependencies for Robust Word Recognition
Robust language processing systems are becoming increasingly important given the recent awareness of dangerous situations where brittle machine learning models can be easily broken with the presence of noises.
Does Gender Matter? Towards Fairness in Dialogue Systems
In particular, we construct a benchmark dataset and propose quantitative measures to understand fairness in dialogue models.
A Double Residual Compression Algorithm for Efficient Distributed Learning
Large-scale machine learning models are often trained by parallel stochastic gradient descent algorithms.
Automatic Short Answer Grading via Multiway Attention Networks
Automatic short answer grading (ASAG), which autonomously score student answers according to reference answers, provides a cost-effective and consistent approach to teaching professionals and can reduce their monotonous and tedious grading workloads.
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
In this survey, we review the state of the art algorithms for generating adversarial examples and the countermeasures against adversarial examples, for the three popular data types, i. e., images, graphs and text.
Say What I Want: Towards the Dark Side of Neural Dialogue Models
Neural dialogue models have been widely adopted in various chatbot applications because of their good performance in simulating and generalizing human conversations.
DEAR: Deep Reinforcement Learning for Online Advertising Impression in Recommender Systems
However, most RL-based advertising algorithms focus on optimizing ads' revenue while ignoring the possible negative influence of ads on user experience of recommended items (products, articles and videos).
Deep Knowledge Tracing with Side Information
Monitoring student knowledge states or skill acquisition levels known as knowledge tracing, is a fundamental part of intelligent tutoring systems.
Learning Effective Embeddings From Crowdsourced Labels: An Educational Case Study
In practice, the crowdsourced labels are usually inconsistent among crowd workers given their diverse expertise and the number of crowdsourced labels is very limited.
Recommender Systems with Heterogeneous Side Information
In modern recommender systems, both users and items are associated with rich side information, which can help understand users and items.
Deep Social Collaborative Filtering
Meanwhile, most of these models treat neighbors' information equally without considering the specific recommendations.
R-Transformer: Recurrent Neural Network Enhanced Transformer
Recurrent Neural Networks have long been the dominating choice for sequence modeling.
Ranked #1 on
Music Modeling
on Nottingham
Toward Simulating Environments in Reinforcement Learning Based Recommendations
Thus, it calls for a user simulator that can mimic real users' behaviors where we can pre-train and evaluate new recommendation algorithms.
Attacking Graph Convolutional Networks via Rewiring
Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks such as node classification and graph classification.
Deep Adversarial Social Recommendation
Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life.
Graph Convolutional Networks with EigenPooling
To apply graph neural networks for the graph classification task, approaches to generate the \textit{graph representation} from node representations are demanded.
Ranked #1 on
Graph Classification
on NC1
Learning Hierarchical Discourse-level Structure for Fake News Detection
Incorporating hierarchical discourse-level structure of fake and real news articles is one crucial step toward a better understanding of how these articles are structured.
Deep Adversarial Network Alignment
Network alignment, in general, seeks to discover the hidden underlying correspondence between nodes across two (or more) networks when given their network structure.
Graph Neural Networks for Social Recommendation
These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key.
Ranked #3 on
Recommendation Systems
on Epinions
(using extra training data)
Whole-Chain Recommendations
With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems.
Multi-agent Reinforcement Learning
Recommendation Systems
+1
Deep reinforcement learning for search, recommendation, and online advertising: a survey
Search, recommendation, and online advertising are the three most important information-providing mechanisms on the web.
Streaming Graph Neural Networks
Current graph neural network models cannot utilize the dynamic information in dynamic graphs.
Signed Graph Convolutional Network
However, since previous GCN models have primarily focused on unsigned networks (or graphs consisting of only positive links), it is unclear how they could be applied to signed networks due to the challenges presented by negative links.
Ranked #2 on
Link Sign Prediction
on Epinions
Social and Information Networks Physics and Society
Linked Recurrent Neural Networks
Recurrent Neural Networks (RNNs) have been proven to be effective in modeling sequential data and they have been applied to boost a variety of tasks such as document classification, speech recognition and machine translation.
Multi-dimensional Graph Convolutional Networks
Convolutional neural networks (CNNs) leverage the great power in representation learning on regular grid data such as image and video.
Social and Information Networks
Multi-Source Multi-Class Fake News Detection
Fake news spreading through media outlets poses a real threat to the trustworthiness of information and detecting fake news has attracted increasing attention in recent years.
Deep Reinforcement Learning for Page-wise Recommendations
In particular, we propose a principled approach to jointly generate a set of complementary items and the corresponding strategy to display them in a 2-D page; and propose a novel page-wise recommendation framework based on deep reinforcement learning, DeepPage, which can optimize a page of items with proper display based on real-time feedback from users.
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning
Users' feedback can be positive and negative and both types of feedback have great potentials to boost recommendations.
Deep Reinforcement Learning for List-wise Recommendations
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services.
A Survey on Dialogue Systems: Recent Advances and New Frontiers
Dialogue systems have attracted more and more attention.
Fake News Detection on Social Media: A Data Mining Perspective
First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination.
Attributed Network Embedding for Learning in a Dynamic Environment
To our best knowledge, we are the first to tackle this problem with the following two challenges: (1) the inherently correlated network and node attributes could be noisy and incomplete, it necessitates a robust consensus representation to capture their individual properties and correlations; (2) the embedding learning needs to be performed in an online fashion to adapt to the changes accordingly.
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
However, sophisticated learning models are computationally unfeasible for data with millions of features.
Hierarchical Attention Network for Action Recognition in Videos
Understanding human actions in wild videos is an important task with a broad range of applications.
Streaming Recommender Systems
The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios.
PPP: Joint Pointwise and Pairwise Image Label Prediction
However, pointwise labels in image classification and tag annotation are inherently related to the pairwise labels.
Feature Selection: A Data Perspective
To facilitate and promote the research in this community, we also present an open-source feature selection repository that consists of most of the popular feature selection algorithms (\url{http://featureselection. asu. edu/}).
A Survey of Signed Network Mining in Social Media
Many real-world relations can be represented by signed networks with positive and negative links, as a result of which signed network analysis has attracted increasing attention from multiple disciplines.
mTrust: Discerning Multi-Faceted Trust in a Connected World
Traditionally, research about trust assumes a single type of trust between users.
