Search Results for author: Yao Ma

Found 67 papers, 26 papers with code

Addressing Shortcomings in Fair Graph Learning Datasets: Towards a New Benchmark

1 code implementation9 Mar 2024 Xiaowei Qian, Zhimeng Guo, Jialiang Li, Haitao Mao, Bingheng Li, Suhang Wang, Yao Ma

These datasets are thoughtfully designed to include relevant graph structures and bias information crucial for the fair evaluation of models.

Benchmarking Fairness +1

Hill Function-based Model of Transcriptional Response: Impact of Nonspecific Binding and RNAP Interactions

no code implementations4 Mar 2024 Wenjia Shi, Yao Ma, Peilin Hu, Mi Pang, Xiaona Huang, Yiting Dang, Yuxin Xie, Danni Wu

Its attribute of fitting may result in a lack of an underlying physical picture, yet the fitting parameters can provide information about biochemical reactions, such as the number of transcription factors (TFs) and the binding energy between regulatory elements.

Attribute

Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks

no code implementations24 Feb 2024 Qian Ma, Hongliang Chi, Hengrui Zhang, Kay Liu, Zhiwei Zhang, Lu Cheng, Suhang Wang, Philip S. Yu, Yao Ma

The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community.

Contrastive Learning Graph Learning +1

A Survey on Safe Multi-Modal Learning System

no code implementations8 Feb 2024 Tianyi Zhao, Liangliang Zhang, Yao Ma, Lu Cheng

In the rapidly evolving landscape of artificial intelligence, multimodal learning systems (MMLS) have gained traction for their ability to process and integrate information from diverse modality inputs.

Active Learning for Graphs with Noisy Structures

no code implementations4 Feb 2024 Hongliang Chi, Cong Qi, Suhang Wang, Yao Ma

Yet, the excessive cost of labeling large-scale graphs led to a focus on active learning on graphs, which aims for effective data selection to maximize downstream model performance.

Active Learning Node Classification

A Data Generation Perspective to the Mechanism of In-Context Learning

no code implementations3 Feb 2024 Haitao Mao, Guangliang Liu, Yao Ma, Rongrong Wang, Jiliang Tang

In-Context Learning (ICL) empowers Large Language Models (LLMs) with the capacity to learn in context, achieving downstream generalization without gradient updates but with a few in-context examples.

In-Context Learning

Graph Foundation Models

no code implementations3 Feb 2024 Haitao Mao, Zhikai Chen, Wenzhuo Tang, Jianan Zhao, Yao Ma, Tong Zhao, Neil Shah, Mikhail Galkin, Jiliang Tang

Graph Foundation Model (GFM) is a new trending research topic in the graph domain, aiming to develop a graph model capable of generalizing across different graphs and tasks.

A Survey on Graph Condensation

no code implementations3 Feb 2024 Hongjia Xu, Liangliang Zhang, Yao Ma, Sheng Zhou, Zhuonan Zheng, Bu Jiajun

Analytics on large-scale graphs have posed significant challenges to computational efficiency and resource requirements.

Computational Efficiency

Precedence-Constrained Winter Value for Effective Graph Data Valuation

no code implementations2 Feb 2024 Hongliang Chi, Wei Jin, Charu Aggarwal, Yao Ma

Data valuation is essential for quantifying data's worth, aiding in assessing data quality and determining fair compensation.

Data Valuation

Distance-Based Propagation for Efficient Knowledge Graph Reasoning

1 code implementation2 Nov 2023 Harry Shomer, Yao Ma, Juanhui Li, Bo Wu, Charu C. Aggarwal, Jiliang Tang

A new class of methods have been proposed to tackle this problem by aggregating path information.

LPFormer: An Adaptive Graph Transformer for Link Prediction

1 code implementation17 Oct 2023 Harry Shomer, Yao Ma, Haitao Mao, Juanhui Li, Bo Wu, Jiliang Tang

In recent years, a new class of methods has emerged that combines the advantages of message-passing neural networks (MPNN) and heuristics methods.

Inductive Bias Link Prediction

Fast Graph Condensation with Structure-based Neural Tangent Kernel

1 code implementation17 Oct 2023 Lin Wang, Wenqi Fan, Jiatong Li, Yao Ma, Qing Li

The rapid development of Internet technology has given rise to a vast amount of graph-structured data.

Dataset Condensation Graph Mining

Revisiting Link Prediction: A Data Perspective

1 code implementation1 Oct 2023 Haitao Mao, Juanhui Li, Harry Shomer, Bingheng Li, Wenqi Fan, Yao Ma, Tong Zhao, Neil Shah, Jiliang Tang

We recognize three fundamental factors critical to link prediction: local structural proximity, global structural proximity, and feature proximity.

Link Prediction

Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection

1 code implementation22 Aug 2023 Mahmoud Nazzal, Issa Khalil, Abdallah Khreishah, NhatHai Phan, Yao Ma

In this work, we call the attack that manipulates several nodes in the DMG concurrently a multi-instance evasion attack.

Adversarial Attack

Towards Fair Graph Neural Networks via Graph Counterfactual

1 code implementation10 Jul 2023 Zhimeng Guo, Jialiang Li, Teng Xiao, Yao Ma, Suhang Wang

Despite their great performance in modeling graphs, recent works show that GNNs tend to inherit and amplify the bias from training data, causing concerns of the adoption of GNNs in high-stake scenarios.

counterfactual Fairness +2

Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?

1 code implementation NeurIPS 2023 Haitao Mao, Zhikai Chen, Wei Jin, Haoyu Han, Yao Ma, Tong Zhao, Neil Shah, Jiliang Tang

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.

Node Classification

Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks

1 code implementation10 Nov 2022 Khang Tran, Phung Lai, NhatHai Phan, Issa Khalil, Yao Ma, Abdallah Khreishah, My Thai, Xintao Wu

Graph neural networks (GNNs) are susceptible to privacy inference attacks (PIAs), given their ability to learn joint representation from features and edges among nodes in graph data.

Learning Representations for Hyper-Relational Knowledge Graphs

1 code implementation30 Aug 2022 Harry Shomer, Wei Jin, Juanhui Li, Yao Ma, Jiliang Tang

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.

Enhancing Graph Contrastive Learning with Node Similarity

no code implementations13 Aug 2022 Hongliang Chi, Yao Ma

Graph contrastive learning (GCL) is a representative framework for self-supervised learning.

Contrastive Learning Data Augmentation +1

Trust-Aware Control of Automated Vehicles in Car-Following Interactions with Human Drivers

no code implementations5 Aug 2022 Mehmet Fatih Ozkan, Yao Ma

With the proposed approach, trust-aware AVs generate explicable plans by optimizing both predefined plans and explicability of the plans in the car-following interactions with the following human driver.

Decision Making

Inverse Resource Rational Based Stochastic Driver Behavior Model

no code implementations14 Jul 2022 Mehmet Ozkan, Yao Ma

An inverse resource rational-based stochastic inverse reinforcement learning approach (IRR-SIRL) is proposed to learn a distribution of the planning horizon and cost function of the human driver with a given series of human demonstrations.

Model Predictive Control

Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective

1 code implementation15 Jun 2022 Wei Jin, Xiaorui Liu, Yao Ma, Charu Aggarwal, Jiliang Tang

In this paper, we propose a new perspective to look at the performance degradation of deep GNNs, i. e., feature overcorrelation.

Drug Discovery Feature Correlation

Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?

1 code implementation21 May 2022 Juanhui Li, Harry Shomer, Jiayuan Ding, Yiqi Wang, Yao Ma, Neil Shah, Jiliang Tang, Dawei Yin

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.

Personalized Driving Behaviors and Fuel Economy over Realistic Commute Traffic: Modeling, Correlation, and Prediction

no code implementations26 Apr 2022 Yao Ma, Junmin Wang

A Gaussian Process Regression model is further trained, validated, and tested under different traffic and vehicle conditions to predict fuel consumption based on drivers' personalized behaviors.

Graph Enhanced BERT for Query Understanding

no code implementations3 Apr 2022 Juanhui Li, Yao Ma, Wei Zeng, Suqi Cheng, Jiliang Tang, Shuaiqiang Wang, Dawei Yin

In other words, GE-BERT can capture both the semantic information and the users' search behavioral information of queries.

Distributed Stochastic Model Predictive Control for Human-Leading Heavy-Duty Truck Platoon

no code implementations27 Jan 2022 Mehmet Fatih Ozkan, Yao Ma

The proposed DSMPC design integrates the stochastic driver behavior model of the human-driven leader truck with a distributed formation control design for the following automated trucks in the platoon.

Model Predictive Control

Socially Compatible Control Design of Automated Vehicle in Mixed Traffic

no code implementations3 Dec 2021 Mehmet Fatih Ozkan, Yao Ma

In the car-following scenarios, automated vehicles (AVs) usually plan motions without considering the impacts of their actions on the following human drivers.

Decision Making

Graph Neural Networks with Adaptive Residual

1 code implementation NeurIPS 2021 Xiaorui Liu, Jiayuan Ding, Wei Jin, Han Xu, Yao Ma, Zitao Liu, Jiliang Tang

Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks.

Graph Representation Learning

Towards Feature Overcorrelation in Deeper Graph Neural Networks

no code implementations29 Sep 2021 Wei Jin, Xiaorui Liu, Yao Ma, Charu Aggarwal, Jiliang Tang

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.

Feature Correlation Graph Representation Learning

Multimodality in Meta-Learning: A Comprehensive Survey

no code implementations28 Sep 2021 Yao Ma, Shilin Zhao, Weixiao Wang, Yaoman Li, Irwin King

This survey provides a comprehensive overview of the multimodality-based meta-learning landscape in terms of the methodologies and applications.

Few-Shot Learning Zero-Shot Learning

Multi-Criteria Radio Spectrum Sharing With Subspace-Based Pareto Tracing

no code implementations23 Aug 2021 Zachary Grey, Susanna Mosleh, Jacob Rezac, Yao Ma, Jason Coder, Andrew Dienstfrey

We perform an exploratory analysis of coexistence behavior by approximating active subspaces to identify low-dimensional structure in the optimization criteria, i. e., few linear combinations of parameters for simultaneously maximizing LAA and Wi-Fi throughputs.

Dimensionality Reduction

Elastic Graph Neural Networks

1 code implementation5 Jul 2021 Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, Jiliang Tang

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.

Inverse Reinforcement Learning Based Stochastic Driver Behavior Learning

no code implementations1 Jul 2021 Mehmet Fatih Ozkan, Abishek Joseph Rocque, Yao Ma

Compared to the deterministic baseline driver behavior model, the results reveal that the proposed stochastic driver behavior model can better replicate the driver's unique and rich driving strategies in a variety of traffic conditions.

reinforcement-learning Reinforcement Learning (RL)

Fuel-Economical Distributed Model Predictive Control for Heavy-Duty Truck Platoon

no code implementations15 Jun 2021 Mehmet Fatih Ozkan, Yao Ma

Simulation studies are conducted to investigate the fuel economy performance of the proposed control strategy and to validate the local and string stability of the platoon under a realistic traffic scenario.

Model Predictive Control

Is Homophily a Necessity for Graph Neural Networks?

no code implementations ICLR 2022 Yao Ma, Xiaorui Liu, Neil Shah, Jiliang Tang

We find that this claim is not quite true, and in fact, GCNs can achieve strong performance on heterophilous graphs under certain conditions.

Node Classification

Automated Self-Supervised Learning for Graphs

1 code implementation ICLR 2022 Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, Jiliang Tang

Then we propose the AutoSSL framework which can automatically search over combinations of various self-supervised tasks.

Clustering Node Classification +2

Graph Feature Gating Networks

no code implementations10 May 2021 Wei Jin, Xiaorui Liu, Yao Ma, Tyler Derr, Charu Aggarwal, Jiliang Tang

Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs.

Denoising

Personalized Adaptive Cruise Control and Impacts on Mixed Traffic

no code implementations26 Mar 2021 Mehmet Ozkan, Yao Ma

The proposed PACC design plans the motion of the SAV by minimizing the learned unique cost function considering the short preview information of the preceding human-driven vehicle.

Optimizing Unlicensed Band Spectrum Sharing With Subspace-Based Pareto Tracing

no code implementations2 Feb 2021 Zachary J. Grey, Susanna Mosleh, Jacob D. Rezac, Yao Ma, Jason B. Coder, Andrew M. Dienstfrey

We perform an exploratory analysis of coexistence behavior by approximating active subspaces to identify low-dimensional structure in the optimization criteria, i. e., few linear combinations of parameters for simultaneously maximizing KPIs.

Dimensionality Reduction

An $N/D$ study of the $S_{11}$ channel $πN$ scattering amplitude

no code implementations1 Feb 2021 Qu-Zhi Li, Yao Ma, Wen-Qi Niu, Yu-Fei Wang, Han-Qing Zheng

Extensive dynamical $N/D$ calculations are made in the study of $S_{11}$ channel low energy $\pi$N scatterings, based on various phenomenological model inputs of left cuts at tree level.

Nuclear Theory High Energy Physics - Phenomenology

Node Similarity Preserving Graph Convolutional Networks

1 code implementation19 Nov 2020 Wei Jin, Tyler Derr, Yiqi Wang, Yao Ma, Zitao Liu, Jiliang Tang

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.

Graph Representation Learning Self-Supervised Learning

A Unified View on Graph Neural Networks as Graph Signal Denoising

1 code implementation5 Oct 2020 Yao Ma, Xiaorui Liu, Tong Zhao, Yozen Liu, Jiliang Tang, Neil Shah

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.

Denoising

Customized Graph Neural Networks

no code implementations22 May 2020 Yiqi Wang, Yao Ma, Wei Jin, Chaozhuo Li, Charu Aggarwal, Jiliang Tang

Therefore, in this paper, we aim to develop customized graph neural networks for graph classification.

General Classification Graph Classification

Attacking Black-box Recommendations via Copying Cross-domain User Profiles

no code implementations17 May 2020 Wenqi Fan, Tyler Derr, Xiangyu Zhao, Yao Ma, Hui Liu, Jian-Ping Wang, Jiliang Tang, Qing Li

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.

Data Poisoning Recommendation Systems

Adversarial Attacks and Defenses in Images, Graphs and Text: A Review

4 code implementations17 Sep 2019 Han Xu, Yao Ma, Haochen Liu, Debayan Deb, Hui Liu, Jiliang Tang, Anil K. Jain

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.

Adversarial Attack

Deep Social Collaborative Filtering

no code implementations16 Jul 2019 Wenqi Fan, Yao Ma, Dawei Yin, Jian-Ping Wang, Jiliang Tang, Qing Li

Meanwhile, most of these models treat neighbors' information equally without considering the specific recommendations.

Collaborative Filtering Recommendation Systems

Attacking Graph Convolutional Networks via Rewiring

no code implementations10 Jun 2019 Yao Ma, Suhang Wang, Tyler Derr, Lingfei Wu, Jiliang Tang

Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks such as node classification and graph classification.

General Classification Graph Classification +1

Deep Adversarial Social Recommendation

2 code implementations30 May 2019 Wenqi Fan, Tyler Derr, Yao Ma, JianPing Wang, Jiliang Tang, Qing Li

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.

Recommendation Systems Representation Learning

Automata Guided Skill Composition

no code implementations ICLR 2019 Xiao Li, Yao Ma, Calin Belta

Skills learned through (deep) reinforcement learning often generalizes poorly across tasks and re-training is necessary when presented with a new task.

reinforcement-learning Reinforcement Learning (RL)

Graph Convolutional Networks with EigenPooling

1 code implementation30 Apr 2019 Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang

To apply graph neural networks for the graph classification task, approaches to generate the \textit{graph representation} from node representations are demanded.

General Classification Graph Classification +3

Graph Neural Networks for Social Recommendation

8 code implementations19 Feb 2019 Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin

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)

Recommendation Systems

Streaming Graph Neural Networks

2 code implementations24 Oct 2018 Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin

Current graph neural network models cannot utilize the dynamic information in dynamic graphs.

Community Detection General Classification +3

Automata Guided Reinforcement Learning With Demonstrations

no code implementations17 Sep 2018 Xiao Li, Yao Ma, Calin Belta

Tasks with complex temporal structures and long horizons pose a challenge for reinforcement learning agents due to the difficulty in specifying the tasks in terms of reward functions as well as large variances in the learning signals.

reinforcement-learning Reinforcement Learning (RL)

Signed Graph Convolutional Network

2 code implementations ICDM 2018 Tyler Derr, Yao Ma, Jiliang Tang

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.

Social and Information Networks Physics and Society

Linked Recurrent Neural Networks

no code implementations19 Aug 2018 Zhiwei Wang, Yao Ma, Dawei Yin, Jiliang Tang

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.

Document Classification Machine Translation +3

Multi-dimensional Graph Convolutional Networks

no code implementations18 Aug 2018 Yao Ma, Suhang Wang, Charu C. Aggarwal, Dawei Yin, Jiliang Tang

Convolutional neural networks (CNNs) leverage the great power in representation learning on regular grid data such as image and video.

Social and Information Networks

AUTOMATA GUIDED HIERARCHICAL REINFORCEMENT LEARNING FOR ZERO-SHOT SKILL COMPOSITION

no code implementations ICLR 2018 Xiao Li, Yao Ma, Calin Belta

An obstacle that prevents the wide adoption of (deep) reinforcement learning (RL) in control systems is its need for a large number of interactions with the environment in order to master a skill.

Hierarchical Reinforcement Learning reinforcement-learning +1

Automata-Guided Hierarchical Reinforcement Learning for Skill Composition

no code implementations31 Oct 2017 Xiao Li, Yao Ma, Calin Belta

Skills learned through (deep) reinforcement learning often generalizes poorly across domains and re-training is necessary when presented with a new task.

Hierarchical Reinforcement Learning reinforcement-learning +1

Crowdsourcing with Sparsely Interacting Workers

no code implementations20 Jun 2017 Yao Ma, Alex Olshevsky, Venkatesh Saligrama, Csaba Szepesvari

We then formulate a weighted rank-one optimization problem to estimate skills based on observations on an irreducible, aperiodic interaction graph.

Binary Classification Matrix Completion

Online Markov decision processes with policy iteration

no code implementations15 Oct 2015 Yao Ma, Hao Zhang, Masashi Sugiyama

The online Markov decision process (MDP) is a generalization of the classical Markov decision process that incorporates changing reward functions.

Bandit-Based Task Assignment for Heterogeneous Crowdsourcing

no code implementations21 Jul 2015 Hao Zhang, Yao Ma, Masashi Sugiyama

We consider a task assignment problem in crowdsourcing, which is aimed at collecting as many reliable labels as possible within a limited budget.

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