236 citations · 468 across the 20 of their papers we have counts for
20 papers
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…
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…
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization
Renzhe Xu, Xingxuan Zhang, Zheyan Shen +2
Covariate-shift generalization, a typical case in out-of-distribution (OOD) generalization, requires a good performance on the unknown test distribution, which varies from the acce…
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…
Towards Out-Of-Distribution Generalization: A Survey
Jiashuo Liu, Zheyan Shen, Yue He +4
Traditional machine learning paradigms are based on the assumption that both training and test data follow the same statistical pattern, which is mathematically referred to as Inde…
Towards Unsupervised Domain Generalization
Xingxuan Zhang, Linjun Zhou, Renzhe Xu +3
Domain generalization (DG) aims to help models trained on a set of source domains generalize better on unseen target domains. The performances of current DG methods largely rely on…
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…
Distributionally Robust Learning with Stable Adversarial Training
Jiashuo Liu, Zheyan Shen, Peng Cui +3
Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts due to the greedy adoption of all the correlations found in training data. T…
Deep Stable Learning for Out-Of-Distribution Generalization
Xingxuan Zhang, Peng Cui, Renzhe Xu +3
Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise. Th…
Algorithmic Decision Making with Conditional Fairness
Renzhe Xu, Peng Cui, Kun Kuang +4
Nowadays fairness issues have raised great concerns in decision-making systems. Various fairness notions have been proposed to measure the degree to which an algorithm is unfair. I…
Stable Adversarial Learning under Distributional Shifts
Jiashuo Liu, Zheyan Shen, Peng Cui +4
Machine learning algorithms with empirical risk minimization are vulnerable under distributional shifts due to the greedy adoption of all the correlations found in training data. R…
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations
Simone Rossi, Markus Heinonen, Edwin V. Bonilla +2
Variational inference techniques based on inducing variables provide an elegant framework for scalable posterior estimation in Gaussian process (GP) models. Besides enabling scalab…
Stable Learning via Sample Reweighting
Zheyan Shen, Peng Cui, Tong Zhang +1
We consider the problem of learning linear prediction models with model misspecification bias. In such case, the collinearity among input variables may inflate the error of paramet…
Towards Non-I.I.D. Image Classification: A Dataset and Baselines
Yue He, Zheyan Shen, Peng Cui
I.I.D. hypothesis between training and testing data is the basis of numerous image classification methods. Such property can hardly be guaranteed in practice where the Non-IIDness…
Learning spectrograms with convolutional spectral kernels
Zheyang Shen, Markus Heinonen, Samuel Kaski
We introduce the convolutional spectral kernel (CSK), a novel family of non-stationary, nonparametric covariance kernels for Gaussian process (GP) models, derived from the convolut…
Harmonizable mixture kernels with variational Fourier features
Zheyang Shen, Markus Heinonen, Samuel Kaski
The expressive power of Gaussian processes depends heavily on the choice of kernel. In this work we propose the novel harmonizable mixture kernel (HMK), a family of expressive, int…
Causally Regularized Learning with Agnostic Data Selection Bias
Zheyan Shen, Peng Cui, Kun Kuang +2
Most of previous machine learning algorithms are proposed based on the i.i.d. hypothesis. However, this ideal assumption is often violated in real applications, where selection bia…