Search Results for author: Ting-Hsiang Wang

Found 5 papers, 2 papers with code

Marginal Nodes Matter: Towards Structure Fairness in Graphs

no code implementations23 Oct 2023 Xiaotian Han, Kaixiong Zhou, Ting-Hsiang Wang, Jundong Li, Fei Wang, Na Zou

Specifically, we first analyzed multiple graphs and observed that marginal nodes in graphs have a worse performance of downstream tasks than others in graph neural networks.

Fairness

DivAug: Plug-in Automated Data Augmentation with Explicit Diversity Maximization

1 code implementation ICCV 2021 Zirui Liu, Haifeng Jin, Ting-Hsiang Wang, Kaixiong Zhou, Xia Hu

We validate in experiments that the relative gain from automated data augmentation in test accuracy is highly correlated to Variance Diversity.

Data Augmentation

Detecting Interactions from Neural Networks via Topological Analysis

no code implementations NeurIPS 2020 Zirui Liu, Qingquan Song, Kaixiong Zhou, Ting-Hsiang Wang, Ying Shan, Xia Hu

Motivated by the observation, in this paper, we propose to investigate the interaction detection problem from a novel topological perspective by analyzing the connectivity in neural networks.

AutoRec: An Automated Recommender System

1 code implementation26 Jun 2020 Ting-Hsiang Wang, Qingquan Song, Xiaotian Han, Zirui Liu, Haifeng Jin, Xia Hu

To address the need, we present AutoRec, an open-source automated machine learning (AutoML) platform extended from the TensorFlow ecosystem and, to our knowledge, the first framework to leverage AutoML for model search and hyperparameter tuning in deep recommendation models.

AutoML Click-Through Rate Prediction +1

Superhighway: Bypass Data Sparsity in Cross-Domain CF

no code implementations28 Aug 2018 Kwei-Herng Lai, Ting-Hsiang Wang, Heng-Yu Chi, Yi-An Chen, Ming-Feng Tsai, Chuan-Ju Wang

Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in single-domain CF by leveraging knowledge transferred from related domains.

Collaborative Filtering

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