27 citations · 61 across the 11 of their papers we have counts for
11 papers
LimiX: Unleashing Structured-Data Modeling Capability for Generalist Intelligence
Xingxuan Zhang, Gang Ren, Han Yu +35
We argue that progress toward general intelligence requires complementary foundation models grounded in language, the physical world, and structured data. This report presents Limi…
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…
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…
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…
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…
Adversarial Eigen Attack on Black-Box Models
Linjun Zhou, Peng Cui, Yinan Jiang +1
Black-box adversarial attack has attracted a lot of research interests for its practical use in AI safety. Compared with the white-box attack, a black-box setting is more difficult…
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…
Learning to Select Base Classes for Few-shot Classification
Linjun Zhou, Peng Cui, Xu Jia +2
Few-shot learning has attracted intensive research attention in recent years. Many methods have been proposed to generalize a model learned from provided base classes to novel clas…
Learning to Learn Image Classifiers with Visual Analogy
Linjun Zhou, Peng Cui, Shiqiang Yang +2
Humans are far better learners who can learn a new concept very fast with only a few samples compared with machines. The plausible mystery making the difference is two fundamental…