102 citations · 263 across the 28 of their papers we have counts for
28 papers
You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Time
Xiaotian Han, Tianlong Chen, Kaixiong Zhou +3
Deep neural networks are prone to various bias issues, jeopardizing their applications for high-stake decision-making. Existing fairness methods typically offer a fixed accuracy-fa…
MAIN-RAG: Multi-Agent Filtering Retrieval-Augmented Generation
Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh +8
Large Language Models (LLMs) are becoming essential tools for various natural language processing tasks but often suffer from generating outdated or incorrect information. Retrieva…
Gradient Rewiring for Editable Graph Neural Network Training
Zhimeng Jiang, Zirui Liu, Xiaotian Han +6
Deep neural networks are ubiquitously adopted in many applications, such as computer vision, natural language processing, and graph analytics. However, well-trained neural networks…
LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning
Hongye Jin, Xiaotian Han, Jingfeng Yang +5
It is well known that LLMs cannot generalize well to long contexts whose lengths are larger than the training sequence length. This poses challenges when employing LLMs for process…
Towards Mitigating Dimensional Collapse of Representations in Collaborative Filtering
Huiyuan Chen, Vivian Lai, Hongye Jin +3
Contrastive Learning (CL) has shown promising performance in collaborative filtering. The key idea is to generate augmentation-invariant embeddings by maximizing the Mutual Informa…
Chasing Fairness in Graphs: A GNN Architecture Perspective
Zhimeng Jiang, Xiaotian Han, Chao Fan +4
There has been significant progress in improving the performance of graph neural networks (GNNs) through enhancements in graph data, model architecture design, and training strateg…
CODA: Temporal Domain Generalization via Concept Drift Simulator
Chia-Yuan Chang, Yu-Neng Chuang, Zhimeng Jiang +3
In real-world applications, machine learning models often become obsolete due to shifts in the joint distribution arising from underlying temporal trends, a phenomenon known as the…
GrowLength: Accelerating LLMs Pretraining by Progressively Growing Training Length
Hongye Jin, Xiaotian Han, Jingfeng Yang +3
The evolving sophistication and intricacies of Large Language Models (LLMs) yield unprecedented advancements, yet they simultaneously demand considerable computational resources an…
Graph Mixup with Soft Alignments
Hongyi Ling, Zhimeng Jiang, Meng Liu +2
We study graph data augmentation by mixup, which has been used successfully on images. A key operation of mixup is to compute a convex combination of a pair of inputs. This operati…
Editable Graph Neural Network for Node Classifications
Zirui Liu, Zhimeng Jiang, Shaochen Zhong +5
Despite Graph Neural Networks (GNNs) have achieved prominent success in many graph-based learning problem, such as credit risk assessment in financial networks and fake news detect…
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
Zirui Liu, Guanchu Wang, Shaochen Zhong +8
With the rapid growth in model size, fine-tuning the large pre-trained language model has become increasingly difficult due to its extensive memory usage. Previous works usually fo…
Data-centric Artificial Intelligence: A Survey
Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai +4
Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for buil…
Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approach
Zhimeng Jiang, Xiaotian Han, Hongye Jin +4
Fairness in machine learning has attracted increasing attention in recent years. The fairness methods improving algorithmic fairness for in-distribution data may not perform well u…
Retiring DP: New Distribution-Level Metrics for Demographic Parity
Xiaotian Han, Zhimeng Jiang, Hongye Jin +4
Demographic parity is the most widely recognized measure of group fairness in machine learning, which ensures equal treatment of different demographic groups. Numerous works aim to…
Adaptive Risk-Aware Bidding with Budget Constraint in Display Advertising
Zhimeng Jiang, Kaixiong Zhou, Mi Zhang +3
Real-time bidding (RTB) has become a major paradigm of display advertising. Each ad impression generated from a user visit is auctioned in real time, where demand-side platform (DS…
Towards Generating Adversarial Examples on Mixed-type Data
Han Xu, Menghai Pan, Zhimeng Jiang +4
The existence of adversarial attacks (or adversarial examples) brings huge concern about the machine learning (ML) model's safety issues. For many safety-critical ML tasks, such as…
DIVISION: Memory Efficient Training via Dual Activation Precision
Guanchu Wang, Zirui Liu, Zhimeng Jiang +3
Activation compressed training provides a solution towards reducing the memory cost of training deep neural networks~(DNNs). However, state-of-the-art work combines a search of qua…
G-Mixup: Graph Data Augmentation for Graph Classification
Xiaotian Han, Zhimeng Jiang, Ninghao Liu +1
This work develops \emph{mixup for graph data}. Mixup has shown superiority in improving the generalization and robustness of neural networks by interpolating features and labels b…
Geometric Graph Representation Learning via Maximizing Rate Reduction
Xiaotian Han, Zhimeng Jiang, Ninghao Liu +3
Learning discriminative node representations benefits various downstream tasks in graph analysis such as community detection and node classification. Existing graph representation…
FMP: Toward Fair Graph Message Passing against Topology Bias
Zhimeng Jiang, Xiaotian Han, Chao Fan +4
Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), t…
BED: A Real-Time Object Detection System for Edge Devices
Guanchu Wang, Zaid Pervaiz Bhat, Zhimeng Jiang +8
Deploying deep neural networks~(DNNs) on edge devices provides efficient and effective solutions for the real-world tasks. Edge devices have been used for collecting a large volume…
On the Sum-Rate Capacity of Poisson Multiple Access Channel with Non-Perfect Photon-Counting Receiver
Zhimeng Jiang, Chen Gong, Guanchu Wang +1
We first investigate two-user nonasymmetric sum-rate Poisson capacity with non-perfect photoncounting receiver under certain condition and demonstrate three possible transmission s…
A Double-station Access Protocol for Optical Wireless Scattering Communication Networks
Guanchu Wang, Chen Gong, Zhimeng Jiang +1
We propose a double-station access protocol (DS-CSMA) with multiple backoff mechanism for optical wireless scattering communication networks (OWSCN). %, where two stations can tran…
On the Achievable Rate and Capacity for a Sample-based Practical Photon-counting Receiver
Zhimeng Jiang, Chen Gong, Guanchu Wang +1
We investigate the achievable rate and capacity of a non-perfect photon-counting receiver. For the case of long symbol duration, the achievable rate under on-off keying modulation…