b'Xia Hu'

research

∙ 09/20/2023

Towards Data-centric Graph Machine Learning: Review and Outlook

Data-centric AI, with its primary focus on the collection, management, a...

0 Xin Zheng, et al. ∙

research

∙ 07/22/2023

Collaborative Graph Neural Networks for Attributed Network Embedding

Graph neural networks (GNNs) have shown prominent performance on attribu...

0 Qiaoyu Tan, et al. ∙

research

∙ 06/15/2023

FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methods

This paper introduces the Fair Fairness Benchmark (), a benchmarking fra...

0 Xiaotian Han, et al. ∙

research

∙ 06/09/2023

Efficient GNN Explanation via Learning Removal-based Attribution

As Graph Neural Networks (GNNs) have been widely used in real-world appl...

0 Yao Rong, et al. ∙

research

∙ 05/22/2023

DEGREE: Decomposition Based Explanation For Graph Neural Networks

Graph Neural Networks (GNNs) are gaining extensive attention for their a...

0 Qizhang Feng, et al. ∙

research

∙ 05/21/2023

Multi-factor Sequential Re-ranking with Perception-Aware Diversification

Feed recommendation systems, which recommend a sequence of items for use...

0 Yue Xu, et al. ∙

research

∙ 05/17/2023

Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt

Large Language Models (LLMs), armed with billions of parameters, exhibit...

9 Zhaozhuo Xu, et al. ∙

research

∙ 05/03/2023

Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models

Sharding a large machine learning model across multiple devices to balan...

4 Daochen Zha, et al. ∙

research

∙ 04/26/2023

Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

This paper presents a comprehensive and practical guide for practitioner...

10 Jingfeng Yang, et al. ∙

research

∙ 04/21/2023

Interactive System-wise Anomaly Detection

Anomaly detection, where data instances are discovered containing featur...

0 Guanchu Wang, et al. ∙

research

∙ 03/30/2023

Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant

Organ transplant is the essential treatment method for some end-stage di...

0 Sirui Ding, et al. ∙

research

∙ 03/24/2023

Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint

Clinical trials are indispensable in developing new treatments, but they...

0 Chia-Yuan Chang, et al. ∙

research

∙ 03/24/2023

LLM for Patient-Trial Matching: Privacy-Aware Data Augmentation Towards Better Performance and Generalizability

The process of matching patients with suitable clinical trials is essent...

0 Jiayi Yuan, et al. ∙

research

∙ 03/23/2023

SPeC: A Soft Prompt-Based Calibration on Mitigating Performance Variability in Clinical Notes Summarization

Electronic health records (EHRs) store an extensive array of patient inf...

0 Yu-Neng Chuang, et al. ∙

research

∙ 03/20/2023

Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking

The huge supporting training data on the Internet has been a key factor ...

0 Ruixiang Tang, et al. ∙

research

∙ 03/19/2023

PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data

Detailed phenotype information is fundamental to accurate diagnosis and ...

0 Shenghan Zhang, et al. ∙

research

∙ 03/08/2023

Does Synthetic Data Generation of LLMs Help Clinical Text Mining?

Recent advancements in large language models (LLMs) have led to the deve...

0 Ruixiang Tang, et al. ∙

research

∙ 03/05/2023

CoRTX: Contrastive Framework for Real-time Explanation

Recent advancements in explainable machine learning provide effective an...

0 Yu-Neng Chuang, et al. ∙

research

∙ 02/28/2023

Towards Personalized Preprocessing Pipeline Search

Feature preprocessing, which transforms raw input features into numerica...

0 Diego Martinez, et al. ∙

research

∙ 02/18/2023

Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning

Liver transplant is an essential therapy performed for severe liver dise...

16 Sirui Ding, et al. ∙

research

∙ 02/07/2023

Efficient XAI Techniques: A Taxonomic Survey

Recently, there has been a growing demand for the deployment of Explaina...

0 Yu-Neng Chuang, et al. ∙

research

∙ 12/23/2022

Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection

Graph neural networks (GNNs) have received remarkable success in link pr...

17 Qiaoyu Tan, et al. ∙

research

∙ 12/20/2022

MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning

Molecular representation learning is crucial for the problem of molecula...

0 Cameron Diao, et al. ∙

research

∙ 12/08/2022

TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems

There has been an explosion of interest in designing various Knowledge G...

0 Huiyuan Chen, et al. ∙

research

∙ 10/19/2022

RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations

The training of graph neural networks (GNNs) is extremely time consuming...

0 Zirui Liu, et al. ∙

research

∙ 10/14/2022

A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking

Large-scale graph training is a notoriously challenging problem for grap...

0 Keyu Duan, et al. ∙

research

∙ 09/30/2022

MLPInit: Embarrassingly Simple GNN Training Acceleration with MLP Initialization

Training graph neural networks (GNNs) on large graphs is complex and ext...

0 Xiaotian Han, et al. ∙

research

∙ 08/25/2022

Shortcut Learning of Large Language Models in Natural Language Understanding: A Survey

Large language models (LLMs) have achieved state-of-the-art performance ...

0 Mengnan Du, et al. ∙

research

∙ 08/12/2022

AutoShard: Automated Embedding Table Sharding for Recommender Systems

Embedding learning is an important technique in deep recommendation mode...

9 Daochen Zha, et al. ∙

research

∙ 08/04/2022

Differentially Private Counterfactuals via Functional Mechanism

Counterfactual, serving as one emerging type of model explanation, has a...

6 Fan Yang, et al. ∙

research

∙ 07/20/2022

Mitigating Algorithmic Bias with Limited Annotations

Existing work on fairness modeling commonly assumes that sensitive attri...

0 Guanchu Wang, et al. ∙

research

∙ 06/29/2022

Fair Machine Learning in Healthcare: A Review

Benefiting from the digitization of healthcare data and the development ...

0 Qizhang Feng, et al. ∙

research

∙ 06/17/2022

Accelerating Shapley Explanation via Contributive Cooperator Selection

Even though Shapley value provides an effective explanation for a DNN mo...

0 Guanchu Wang, et al. ∙

research

∙ 05/27/2022

Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture

Physics-informed neural networks (PINNs) are revolutionizing science and...

0 Yicheng Wang, et al. ∙

research

∙ 01/21/2022

Deconfounding to Explanation Evaluation in Graph Neural Networks

Explainability of graph neural networks (GNNs) aims to answer “Why the G...

0 Ying Xin, et al. ∙

research

∙ 01/07/2022

MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs

We introduce a novel masked graph autoencoder (MGAE) framework to perfor...

0 Qiaoyu Tan, et al. ∙

research

∙ 11/08/2021

Defense Against Explanation Manipulation

Explainable machine learning attracts increasing attention as it improve...

23 Ruixiang Tang, et al. ∙

research

∙ 10/28/2021

CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks

Despite the recent advances of graph neural networks (GNNs) in modeling ...

0 Haotian Xue, et al. ∙

research

∙ 10/16/2021

What do Compressed Large Language Models Forget? Robustness Challenges in Model Compression

Recent works have focused on compressing pre-trained language models (PL...

13 Mengnan Du, et al. ∙

research

∙ 09/23/2021

Orthogonal Graph Neural Networks

Graph neural networks (GNNs) have received tremendous attention due to t...

0 Kai Guo, et al. ∙

research

∙ 08/30/2021

Adaptive Label Smoothing To Regularize Large-Scale Graph Training

Graph neural networks (GNNs), which learn the node representations by re...

0 Kaixiong Zhou, et al. ∙

research

∙ 08/24/2021

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study

Training deep graph neural networks (GNNs) is notoriously hard. Besides ...

0 Tianlong Chen, et al. ∙

research

∙ 07/06/2021

Dirichlet Energy Constrained Learning for Deep Graph Neural Networks

Graph neural networks (GNNs) integrate deep architectures and topologica...

0 Kaixiong Zhou, et al. ∙

research

∙ 06/29/2021

Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning

Recent work on graph generative models has made remarkable progress towa...

0 Kiarash Zahirnia, et al. ∙

research

∙ 06/23/2021

Fairness via Representation Neutralization

Existing bias mitigation methods for DNN models primarily work on learni...

5 Mengnan Du, et al. ∙

research

∙ 06/16/2021

Model-Based Counterfactual Synthesizer for Interpretation

Counterfactuals, serving as one of the emerging type of model interpreta...

12 Fan Yang, et al. ∙

research

∙ 06/11/2021

DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

Games are abstractions of the real world, where artificial agents learn ...

0 Daochen Zha, et al. ∙

research

∙ 05/28/2021

A General Taylor Framework for Unifying and Revisiting Attribution Methods

Attribution methods provide an insight into the decision-making process ...

0 Huiqi Deng, et al. ∙

research

∙ 05/17/2021

Learning Disentangled Representations for Time Series

Time-series representation learning is a fundamental task for time-serie...

17 Yuening Li, et al. ∙

research

∙ 04/14/2021

Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution

Back propagation based visualizations have been proposed to interpret de...

0 Huiqi Deng, et al. ∙

Xia Hu

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