21 citations · 57 across the 8 of their papers we have counts for
12 papers
Stable Learning via Sparse Variable Independence
Han Yu, Peng Cui, Yue He +4
The problem of covariate-shift generalization has attracted intensive research attention. Previous stable learning algorithms employ sample reweighting schemes to decorrelate the c…
NICO++: Towards Better Benchmarking for Domain Generalization
Xingxuan Zhang, Yue He, Renzhe Xu +3
Despite the remarkable performance that modern deep neural networks have achieved on independent and identically distributed (I.I.D.) data, they can crash under distribution shifts…
Regulatory Instruments for Fair Personalized Pricing
Renzhe Xu, Xingxuan Zhang, Peng Cui +3
Personalized pricing is a business strategy to charge different prices to individual consumers based on their characteristics and behaviors. It has become common practice in many i…
Kernelized Heterogeneous Risk Minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui +2
The ability to generalize under distributional shifts is essential to reliable machine learning, while models optimized with empirical risk minimization usually fail on non-…
De-randomizing MCMC dynamics with the diffusion Stein operator
Zheyang Shen, Markus Heinonen, Samuel Kaski
Approximate Bayesian inference estimates descriptors of an intractable target distribution - in essence, an optimization problem within a family of distributions. For example, Lang…
Heterogeneous Risk Minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui +2
Machine learning algorithms with empirical risk minimization usually suffer from poor generalization performance due to the greedy exploitation of correlations among the training d…