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...
share
research
∙
07/22/2023
Collaborative Graph Neural Networks for Attributed Network Embedding
Graph neural networks (GNNs) have shown prominent performance on attribu...
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...
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...
research
∙
05/22/2023
DEGREE: Decomposition Based Explanation For Graph Neural Networks
Graph Neural Networks (GNNs) are gaining extensive attention for their a...
research
∙
05/21/2023
Multi-factor Sequential Re-ranking with Perception-Aware Diversification
Feed recommendation systems, which recommend a sequence of items for use...
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...
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...
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...
research
∙
04/21/2023
Interactive System-wise Anomaly Detection
Anomaly detection, where data instances are discovered containing featur...
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...
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...
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...
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...
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 ...
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 ...
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...
research
∙
03/05/2023
CoRTX: Contrastive Framework for Real-time Explanation
Recent advancements in explainable machine learning provide effective an...
research
∙
02/28/2023
Towards Personalized Preprocessing Pipeline Search
Feature preprocessing, which transforms raw input features into numerica...
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...
research
∙
02/07/2023
Efficient XAI Techniques: A Taxonomic Survey
Recently, there has been a growing demand for the deployment of Explaina...
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...
research
∙
12/20/2022
MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning
Molecular representation learning is crucial for the problem of molecula...
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...
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...
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...
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...
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 ...
research
∙
08/12/2022
AutoShard: Automated Embedding Table Sharding for Recommender Systems
Embedding learning is an important technique in deep recommendation mode...
research
∙
08/04/2022
Differentially Private Counterfactuals via Functional Mechanism
Counterfactual, serving as one emerging type of model explanation, has a...
research
∙
07/20/2022
Mitigating Algorithmic Bias with Limited Annotations
Existing work on fairness modeling commonly assumes that sensitive attri...
research
∙
06/29/2022
Fair Machine Learning in Healthcare: A Review
Benefiting from the digitization of healthcare data and the development ...
research
∙
06/17/2022
Accelerating Shapley Explanation via Contributive Cooperator Selection
Even though Shapley value provides an effective explanation for a DNN mo...
research
∙
05/27/2022
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture
Physics-informed neural networks (PINNs) are revolutionizing science and...
research
∙
01/21/2022
Deconfounding to Explanation Evaluation in Graph Neural Networks
Explainability of graph neural networks (GNNs) aims to answer “Why the G...
research
∙
01/07/2022
MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs
We introduce a novel masked graph autoencoder (MGAE) framework to perfor...
research
∙
11/08/2021
Defense Against Explanation Manipulation
Explainable machine learning attracts increasing attention as it improve...
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 ...
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...
research
∙
09/23/2021
Orthogonal Graph Neural Networks
Graph neural networks (GNNs) have received tremendous attention due to t...
research
∙
08/30/2021
Adaptive Label Smoothing To Regularize Large-Scale Graph Training
Graph neural networks (GNNs), which learn the node representations by re...
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 ...
research
∙
07/06/2021
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
Graph neural networks (GNNs) integrate deep architectures and topologica...
research
∙
06/29/2021
Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning
Recent work on graph generative models has made remarkable progress towa...
research
∙
06/23/2021
Fairness via Representation Neutralization
Existing bias mitigation methods for DNN models primarily work on learni...
research
∙
06/16/2021
Model-Based Counterfactual Synthesizer for Interpretation
Counterfactuals, serving as one of the emerging type of model interpreta...
research
∙
06/11/2021
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
Games are abstractions of the real world, where artificial agents learn ...
research
∙
05/28/2021
A General Taylor Framework for Unifying and Revisiting Attribution Methods
Attribution methods provide an insight into the decision-making process ...
research
∙
05/17/2021
Learning Disentangled Representations for Time Series
Time-series representation learning is a fundamental task for time-serie...
research
∙
04/14/2021