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4. DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation

Author

Zhong, Kailiang, Xiao, Fengtong, Ren, Yan, Liang, Yaorong, Yao, Wenqing, Yang, Xiaofeng, and Cen, Ling

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, I.2.m
Abstract

Causal Inference has wide applications in various areas such as E-commerce and precision medicine, and its performance heavily relies on the accurate estimation of the Individual Treatment Effect (ITE). Conventionally, ITE is predicted by modeling the treated and control response functions separately in their individual sample spaces. However, such an approach usually encounters two issues in practice, i.e. divergent distribution between treated and control groups due to treatment bias, and significant sample imbalance of their population sizes. This paper proposes Deep Entire Space Cross Networks (DESCN) to model treatment effects from an end-to-end perspective. DESCN captures the integrated information of the treatment propensity, the response, and the hidden treatment effect through a cross network in a multi-task learning manner. Our method jointly learns the treatment and response functions in the entire sample space to avoid treatment bias and employs an intermediate pseudo treatment effect prediction network to relieve sample imbalance. Extensive experiments are conducted on a synthetic dataset and a large-scaled production dataset from the E-commerce voucher distribution business. The results indicate that DESCN can successfully enhance the accuracy of ITE estimation and improve the uplift ranking performance. A sample of the production dataset and the source code are released to facilitate future research in the community, which is, to the best of our knowledge, the first large-scale public biased treatment dataset for causal inference., Comment: Accepted by SIGKDD 2022 Applied Data Science Track

Published
2022
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17. Stacked Ensemble of Convolutional Neural Networks for Follicles Detection on Scalp Images

Author

Ruta, Dymitr, Cen, Ling, Ruta, Andrzej, Vu, Quang Hieu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rutkowski, Leszek, editor, Scherer, Rafał, editor, Korytkowski, Marcin, editor, Pedrycz, Witold, editor, Tadeusiewicz, Ryszard, editor, and Zurada, Jacek M., editor

Published
2023
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23. CX3CR1 deficiency-induced TIL tumor restriction as a novel addition for CAR-T design in solid malignancies

Author

Trinh, ThuLe, Adams, William A., Calescibetta, Alexandra, Tu, Nhan, Dalton, Robert, So, Tina, Wei, Max, Ward, Grace, Kostenko, Elena, Christiansen, Sean, Cen, Ling, McLemore, Amy, Reed, Kayla, Whitting, Junmin, Gilvary, Danielle, Blanco, Neale Lopez, Segura, Carlos Moran, Nguyen, Jonathan, Kandell, Wendy, Chen, Xianghong, Cheng, Pingyan, Wright, Gabriela M., Cress, W. Douglas, Liu, Jinghong, Wright, Kenneth L., Wei, Sheng, and Eksioglu, Erika A.

Published
2023
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33. Augmented Immersive Reality (AIR) for Improved Learning Performance: A Quantitative Evaluation

Author

Cen, Ling, Ruta, Dymitr, Al Qassem, Lamees Mahmoud Mohd Said, and Ng, Jason

Abstract

Technology-enhanced learning has attracted increasing attention of educational community focused on improvement of traditional classroom learning. Augmented immersive reality (AIR) technologies enhance users' perception of reality by augmenting it with computer-generated components such as audio, video, 2/3-D graphics, GPS data, etc. The AIR introduces new dimensions of learning experience that ensure better attention, focus, and entertainment, thereby boosting students' motivation and attainment. This work presents an award winning AIR-based educational mobile system, code-named AIR-EDUTECH, that was developed to help high school students learn chemistry. The AIR-EDUTECH introduced new AIR features to help students better understand and learn basic concepts of molecular chemistry. It offers immersive 3D visualization and visual interaction with the examined structures that provides a broader and more retentive knowledge and improves intuition around forming basic chemical reactions. The system was introduced and tested in a field study with 45 students in the 11th grade chemistry class, and its impact was evaluated by the formal assessment quiz along with the feedback from survey conducted after the trial. Collected data have been subjected to an in-depth multi-modal quantitative analysis that revealed that AIR-EDUTECH stimulated significant improvements in understanding and retention of the taught content as well as turned learning chemistry into a fun, interesting and interactive experience. It also uncovered a hidden structure of taught knowledge dependencies and highlighted the role that AIR technology could play in reinforcing the retention of critical knowledge that may otherwise widen student knowledge gaps.

Published
2020
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40. Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks

Author

Cen, Ling-Ping, Ji, Jie, Lin, Jian-Wei, Ju, Si-Tong, Lin, Hong-Jie, Li, Tai-Ping, Wang, Yun, Yang, Jian-Feng, Liu, Yu-Fen, Tan, Shaoying, Tan, Li, Li, Dongjie, Wang, Yifan, Zheng, Dezhi, Xiong, Yongqun, Wu, Hanfu, Jiang, Jingjing, Wu, Zhenggen, Huang, Dingguo, Shi, Tingkun, Chen, Binyao, Yang, Jianling, Zhang, Xiaoling, Luo, Li, Huang, Chukai, Zhang, Guihua, Huang, Yuqiang, Ng, Tsz Kin, Chen, Haoyu, Chen, Weiqi, Pang, Chi Pui, and Zhang, Mingzhi

Published
2021
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44. Managing a Large-Scale Multiomics Project: A Team Science Case Study in Proteogenomics

Author

Stewart, Paul A., primary, Welsh, Eric A., additional, Fang, Bin, additional, Izumi, Victoria, additional, Mesa, Tania, additional, Zhang, Chaomei, additional, Yoder, Sean, additional, Zhang, Guolin, additional, Cen, Ling, additional, Pettersson, Fredrik, additional, Zhang, Yonghong, additional, Chen, Zhihua, additional, Cheng, Chia-Ho, additional, Thapa, Ram, additional, Thompson, Zachary, additional, Avedon, Melissa, additional, Wloch, Marek, additional, Fournier, Michelle, additional, Fellows, Katherine M., additional, Francis, Jewel M., additional, Saller, James J., additional, Boyle, Theresa A., additional, Chen, Y. Ann, additional, Haura, Eric B., additional, Teer, Jamie K., additional, Eschrich, Steven A., additional, and Koomen, John M., additional

Published
2020
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49. Deep Learning Method for Sleep Stage Classification

Author

Cen, Ling, Yu, Zhu Liang, Tang, Yun, Shi, Wen, Kluge, Tilmann, Ser, Wee, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Liu, Derong, editor, Xie, Shengli, editor, Li, Yuanqing, editor, Zhao, Dongbin, editor, and El-Alfy, El-Sayed M., editor

Published
2017
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50. Summarization-Guided Greedy Optimization of Machine Learning Model

Author

Ruta, Dymitr, Cen, Ling, Damiani, Ernesto, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, and Perner, Petra, editor

Published
2017
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