38 citations · 92 across the 17 of their papers we have counts for
17 papers
Orthogonal Gromov-Wasserstein Discrepancy with Efficient Lower Bound
Hongwei Jin, Zishun Yu, Xinhua Zhang
Comparing structured data from possibly different metric-measure spaces is a fundamental task in machine learning, with applications in, e.g., graph classification. The Gromov-Wass…
Proximal Mapping for Deep Regularization
Mao Li, Yingyi Ma, Xinhua Zhang
Underpinning the success of deep learning is effective regularizations that allow a variety of priors in data to be modeled. For example, robustness to adversarial perturbations, a…
Convex Representation Learning for Generalized Invariance in Semi-Inner-Product Space
Yingyi Ma, Vignesh Ganapathiraman, Yaoliang Yu +1
Invariance (defined in a general sense) has been one of the most effective priors for representation learning. Direct factorization of parametric models is feasible only for a smal…
Generalised Lipschitz Regularisation Equals Distributional Robustness
Zac Cranko, Zhan Shi, Xinhua Zhang +2
The problem of adversarial examples has highlighted the need for a theory of regularisation that is general enough to apply to exotic function classes, such as universal approximat…
Consistent Robust Adversarial Prediction for General Multiclass Classification
Rizal Fathony, Kaiser Asif, Anqi Liu +5
We propose a robust adversarial prediction framework for general multiclass classification. Our method seeks predictive distributions that robustly optimize non-convex and non-cont…
Distributionally Robust Graphical Models
Rizal Fathony, Ashkan Rezaei, Mohammad Ali Bashiri +2
In many structured prediction problems, complex relationships between variables are compactly defined using graphical structures. The most prevalent graphical prediction methods---…