Felix Yu

1.3k total citations
15 papers, 466 citations indexed

About

Felix Yu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Felix Yu has authored 15 papers receiving a total of 466 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 6 papers in Computer Vision and Pattern Recognition and 2 papers in Information Systems. Recurrent topics in Felix Yu's work include Topic Modeling (4 papers), Stochastic Gradient Optimization Techniques (3 papers) and Natural Language Processing Techniques (3 papers). Felix Yu is often cited by papers focused on Topic Modeling (4 papers), Stochastic Gradient Optimization Techniques (3 papers) and Natural Language Processing Techniques (3 papers). Felix Yu collaborates with scholars based in United States, Hong Kong and Canada. Felix Yu's co-authors include Sanjiv Kumar, Shih‐Fu Chang, Yunchao Gong, Ed H., Tiansheng Yao, Aditya Krishna Menon, Ting Chen, Derek Zhiyuan Cheng, Lichan Hong and Xinyang Yi and has published in prestigious journals such as ACM Transactions on Graphics, JAMA Network Open and Signal Processing Image Communication.

In The Last Decade

Felix Yu

14 papers receiving 455 citations

Peers

Felix Yu

AI

CVPR

IS

RNMI

CNC

Jane C. Asmuth United States

Nesma Settouti Algeria

Mohammed Alawad United States

Murtaza Ali Khan Saudi Arabia

Saeed Iqbal Pakistan

Ahmad Ali Abin Iran

Seok-Bum Ko Canada

Amit Kumar Jaiswal India

Noël De Palma France

Shibao Zheng China

Jane C. Asmuth United States

Felix Yu

87 ×0.3

AI

387 ×2.1

CVPR

218 ×1.7

IS

140 ×4.0

RNMI

20 ×0.6

CNC

Citations per year, relative to Felix Yu Felix Yu (= 1×) peers Jane C. Asmuth

Countries citing papers authored by Felix Yu

Since Specialization

Citations

This map shows the geographic impact of Felix Yu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Felix Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felix Yu more than expected).

Fields of papers citing papers by Felix Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Felix Yu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Felix Yu. The network helps show where Felix Yu may publish in the future.

Co-authorship network of co-authors of Felix Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Felix Yu. A scholar is included among the top collaborators of Felix Yu based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Felix Yu. Felix Yu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

15 of 15 papers shown

1.

Hsieh, Cho‐Jui, Si Si, Felix Yu, & Inderjit S. Dhillon. (2024). Automatic Engineering of Long Prompts. 10672–10685. 5 indexed citations

2.

Łukasik, Michał, Harikrishna Narasimhan, Aditya Krishna Menon, Felix Yu, & Sanjiv Kumar. (2024). Regression Aware Inference with LLMs. 13667–13678.

3.

Yu, Felix, et al.. (2023). Self-embedding reversible color-to-grayscale conversion with watermarking feature. Signal Processing Image Communication. 119. 117061–117061. 2 indexed citations

4.

Li, Daliang, Ankit Singh Rawat, Manzil Zaheer, et al.. (2023). Large Language Models with Controllable Working Memory. 1774–1793. 22 indexed citations

5.

Xiong, Yuanhao, Ruochen Wang, Minhao Cheng, Felix Yu, & Cho‐Jui Hsieh. (2023). FedDM: Iterative Distribution Matching for Communication-Efficient Federated Learning. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 16323–16332. 46 indexed citations

6.

Yu, Felix, et al.. (2021). InFillmore: Frame-Guided Language Generation with Bidirectional Context. 129–142. 2 indexed citations

7.

Yao, Tiansheng, Xinyang Yi, Derek Zhiyuan Cheng, et al.. (2021). Self-supervised Learning for Large-scale Item Recommendations. 4321–4330. 135 indexed citations

8.

Yu, Felix, Zhiwei Deng, Karthik Narasimhan, & Olga Russakovsky. (2020). Take the Scenic Route: Improving Generalization in Vision-and-Language Navigation. 4000–4004. 6 indexed citations

9.

Yu, Felix, Tae Soo Kim, Gregory D. Hager, et al.. (2019). Assessment of Automated Identification of Phases in Videos of Cataract Surgery Using Machine Learning and Deep Learning Techniques. JAMA Network Open. 2(4). e191860–e191860. 68 indexed citations

10.

Tseng, Ethan, Felix Yu, Fahim Mannan, et al.. (2019). Hyperparameter optimization in black-box image processing using differentiable proxies. ACM Transactions on Graphics. 38(4). 1–14. 39 indexed citations

11.

Agarwal, Naman, Ananda Theertha Suresh, Felix Yu, Sanjiv Kumar, & H. Brendan McMahan. (2018). cpSGD: communication-efficient and differentially-private distributed SGD. Neural Information Processing Systems. 31. 7575–7586. 49 indexed citations

12.

Medina, Andrés Muñoz, et al.. (2016). Learning mobile phone battery consumptions. 1 indexed citations

13.

Jou, Brendan, et al.. (2016). Tamp. 1206–1209. 2 indexed citations

14.

Yu, Felix, Sanjiv Kumar, Yunchao Gong, & Shih‐Fu Chang. (2014). Circulant Binary Embedding. arXiv (Cornell University). 946–954. 83 indexed citations

15.

Yu, Felix, et al.. (2013). $\propto$SVM for Learning with Label Proportions. International Conference on Machine Learning. 504–512. 6 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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