Search Results for author: Mucong Ding

Found 9 papers, 2 papers with code

A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs

no code implementations29 Sep 2021 Mucong Ding, Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Micah Goldblum, David Wipf, Furong Huang, Tom Goldstein

We observe that in most cases, we need both a suitable domain generalization algorithm and a strong GNN backbone model to optimize out-of-distribution test performance.

Domain Generalization Graph Classification +1

Understanding Overparameterization in Generative Adversarial Networks

no code implementations12 Apr 2021 Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi

We also empirically study the role of model overparameterization in GANs using several large-scale experiments on CIFAR-10 and Celeb-A datasets.

Understanding Over-parameterization in Generative Adversarial Networks

no code implementations ICLR 2021 Yogesh Balaji, Mohammadmahdi Sajedi, Neha Mukund Kalibhat, Mucong Ding, Dominik Stöger, Mahdi Soltanolkotabi, Soheil Feizi

In this work, we present a comprehensive analysis of the importance of model over-parameterization in GANs both theoretically and empirically.

Robust Optimization as Data Augmentation for Large-scale Graphs

3 code implementations19 Oct 2020 Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein

Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks).

Data Augmentation Graph Classification +3

GANs with Conditional Independence Graphs: On Subadditivity of Probability Divergences

no code implementations2 Mar 2020 Mucong Ding, Constantinos Daskalakis, Soheil Feizi

GANs, however, are designed in a model-free fashion where no additional information about the underlying distribution is available.

Image-to-Image Translation Time Series

Transfer Learning using Representation Learning in Massive Open Online Courses

no code implementations12 Dec 2018 Mucong Ding, Yanbang Wang, Erik Hemberg, Una-May O'Reilly

It consists of two alternative transfer methods based on representation learning with auto-encoders: a passive approach using transductive principal component analysis and an active approach that uses a correlation alignment loss term.

Representation Learning Transfer Learning

Effective Feature Learning with Unsupervised Learning for Improving the Predictive Models in Massive Open Online Courses

no code implementations12 Dec 2018 Mucong Ding, Kai Yang, Dit-yan Yeung, Ting-Chuen Pong

A major challenge that has to be addressed when building such models is to design handcrafted features that are effective for the prediction task at hand.

Selection of Random Walkers that Optimizes the Global Mean First-Passage Time for Search in Complex Networks

no code implementations12 Dec 2018 Mucong Ding, Kwok Yip Szeto

We design a method to optimize the global mean first-passage time (GMFPT) of multiple random walkers searching in complex networks for a general target, without specifying the property of the target node.

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