49 citations · 171 across the 23 of their papers we have counts for
11 papers · 1 filter
Towards Accelerated Model Training via Bayesian Data Selection
Zhijie Deng, Peng Cui, Jun Zhu
Mislabeled, duplicated, or biased data in real-world scenarios can lead to prolonged training and even hinder model convergence. Traditional solutions prioritizing easy or hard sam…
Rethinking Distribution Shifts: Empirical Analysis and Modeling for Tabular Data
Tianyu Wang, Jiashuo Liu, Peng Cui +1
Different distribution shifts require different interventions, and algorithms must be grounded in the specific shifts they address. However, methodological development for robust a…
Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks
Shixuan Liu, Changjun Fan, Kewei Cheng +4
Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation t…
Meta Adaptive Task Sampling for Few-Domain Generalization
Zheyan Shen, Han Yu, Peng Cui +4
To ensure the out-of-distribution (OOD) generalization performance, traditional domain generalization (DG) methods resort to training on data from multiple sources with different u…
RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text
Wangchunshu Zhou, Yuchen Eleanor Jiang, Peng Cui +5
The fixed-size context of Transformer makes GPT models incapable of generating arbitrarily long text. In this paper, we introduce RecurrentGPT, a language-based simulacrum of the r…
Exploring and Exploiting Data Heterogeneity in Recommendation
Zimu Wang, Jiashuo Liu, Hao Zou +4
Massive amounts of data are the foundation of data-driven recommendation models. As an inherent nature of big data, data heterogeneity widely exists in real-world recommendation sy…
Rethinking the Evaluation Protocol of Domain Generalization
Han Yu, Xingxuan Zhang, Renzhe Xu +3
Domain generalization aims to solve the challenge of Out-of-Distribution (OOD) generalization by leveraging common knowledge learned from multiple training domains to generalize to…
Learning Sample Difficulty from Pre-trained Models for Reliable Prediction
Peng Cui, Dan Zhang, Zhijie Deng +2
Large-scale pre-trained models have achieved remarkable success in many applications, but how to leverage them to improve the prediction reliability of downstream models is undesir…
Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization
Xingxuan Zhang, Renzhe Xu, Han Yu +2
Recently, flat minima are proven to be effective for improving generalization and sharpness-aware minimization (SAM) achieves state-of-the-art performance. Yet the current definiti…
Confidence-based Reliable Learning under Dual Noises
Peng Cui, Yang Yue, Zhijie Deng +1
Deep neural networks (DNNs) have achieved remarkable success in a variety of computer vision tasks, where massive labeled images are routinely required for model optimization. Yet,…
Model Agnostic Sample Reweighting for Out-of-Distribution Learning
Xiao Zhou, Yong Lin, Renjie Pi +4
Distributionally robust optimization (DRO) and invariant risk minimization (IRM) are two popular methods proposed to improve out-of-distribution (OOD) generalization performance of…