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20212023
most citedCausPref: Causal Preference Learning for Out-of-Distribution Recommendation

49 citations · 171 across the 23 of their papers we have counts for

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Showing 2023 · cs.LGShow all

7 papers · 2 filters

cs.LG2023

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…

cs.LG2023★ 1 cited

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…

cs.LG2023

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…

cs.LG2023★ 1 cited

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…

cs.LG2023★ 3 cited

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…

cs.LG2023

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…

cs.LG2023★ 10 cited

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…