FTRL algorithm

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Author: Tsz Ki Peter Wei (tmw86), Nhi Nguyen (npn25), Colin Erb (cte24) (ChemE 6800 Fall 2024)

Introduction

Algorithm Discussion

Numerical Examples

Application

The FTRL algorithm is a powerful framework for online learning problems due to its ability to handle large datasets and adapt to new data in real-time. It has been applied in finance, healthcare, and electrical engineering. In finance, the FTRL algorithm has been used in online advertising, e-commerce recommendations, and fraud detection [1][2]. In healthcare, the FTRL algorithm has been used for the analysis of patient data [3]. In electrical engineering, the FTRL algorithm has been used to optimize the performance of electricity-generating systems [4][5]. Specific case studies of the FTRL algorithm in each field are shown below.

Finance

The first application that the FTRL algorithm was used for was online advertising by Google [1]. In this case study, the FTRL algorithm was used to predict ad click-through rates (CTR) for sponsored search advertising at Google [1]. They were able to demonstrate the FTRL algorithm’s ability to both predict accurately and also be sparse compared to other online learning algorithms [1]. Additional case studies in the finance sector have been explored such as online portfolio optimization [6]. In this case study, the authors were able to build off the original FTRL algorithm to come up with a new algorithm called VB-FTRL that can maximize a trader’s return on their portfolio while having reduced runtime compared to the best-performing algorithm (Universal Portfolios) [6].  

Healthcare

In healthcare, the FTRL algorithm was used in a case study to classify thyroid nodules for Thyroid cancer diagnosis [7]. In this case study, the authors proposed a novel FTRL-Deep Neural Network technique to precisely classify thyroid nodules into benign or malignant. The algorithm would analyze ultrasound images of the thyroid nodules and then determine whether or not the patient has thyroid cancer or not [7]. They were able to show that their FTRL-Deep Neural Network algorithm had superior accuracy compared to other algorithms such as the Hybrid Feature Cropping Network and Multi-Channel Convolutional Neural Network [7].

Electrical Engineering

Recently, FTRL has been applied to electricity-generating systems. A recent case study used FTRL to optimize the output of a solar panel system to offset the effect of non-uniform irradiance [4]. They developed this algorithm to control a system of switches that would determine the optimal configuration of the solar panel array when non-uniform irradiance is detected to maximize the power output from the solar panel [4]. In an earlier case study, researchers were able to use an FTRL algorithm to predict when low voltages would occur in a power distribution system, allowing for better system management [5].

Current Implementation in Softwares and Platforms

Software tools such as H2O Driverless AI and Keras can utilize the FTRL algorithm for various machine-learning applications [8][9]. In addition, platforms such as Google Ads utilize the FTRL algorithm [2]. These are just a few examples of softwares and platforms where the FTRL algorithm is used. As machine-learning becomes more and more widespread in different applications, the usage of the FTRL algorithm is expected to increase.

Conclusion

References

  1. ↑ Jump up to: 1.0 1.1 1.2 1.3 H. B. McMahan et al., “Ad click prediction: a view from the trenches,” in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, Chicago Illinois USA: ACM, Aug. 2013, pp. 1222–1230. doi: 10.1145/2487575.2488200.
  2. ↑ Jump up to: 2.0 2.1 J. O. Schneppat, “Follow The Regularized Leader (FTRL),” Schneppat AI. Accessed: Nov. 30, 2024. [Online]. Available: https://schneppat.com/follow-the-regularized-leader_ftrl.html
  3. ↑ Z. Ye, F. Chen, and Y. Jiang, “Analysis and Privacy Protection of Healthcare Data Using Digital Signature,” in Proceedings of the 2024 3rd International Conference on Cryptography, Network Security and Communication Technology, Harbin China: ACM, Jan. 2024, pp. 171–176. doi: 10.1145/3673277.3673307.
  4. ↑ Jump up to: 4.0 4.1 4.2 X. Gao et al., “Followed The Regularized Leader (FTRL) prediction model based photovoltaic array reconfiguration for mitigation of mismatch losses in partial shading condition,” IET Renew. Power Gener., vol. 16, no. 1, pp. 159–176, 2022, doi: 10.1049/rpg2.12275.
  5. ↑ Jump up to: 5.0 5.1 C. Gao, Z. Ding, S. Yan, and H. Mai, “Low Voltage Prediction Based on Spark and Ftrl,” in Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017), Tianjin City, China: Atlantis Press, 2017. doi: 10.2991/ammee-17.2017.34.
  6. ↑ Jump up to: 6.0 6.1 R. Jézéquel, D. M. Ostrovskii, and P. Gaillard, “Efficient and Near-Optimal Online Portfolio Selection,” Sep. 28, 2022, arXiv: arXiv:2209.13932. doi: 10.48550/arXiv.2209.13932.
  7. ↑ Jump up to: 7.0 7.1 7.2 A. Beyyala, R. Priya, S. R. Choudari, and R. Bhavani, “Classification of Thyroid Nodules Using Follow the Regularized Leader Optimization Based Deep Neural Networks. | EBSCOhost.” Accessed: Nov. 30, 2024. [Online]. Available: https://openurl.ebsco.com/contentitem/doi:10.18280%2Fria.370315?sid=ebsco:plink:crawler&id=ebsco:doi:10.18280%2Fria.370315
  8. ↑ “Supported Algorithms — Using Driverless AI 1.11.0 documentation.” Accessed: Nov. 30, 2024. [Online]. Available: https://docs.h2o.ai/driverless-ai/1-10-lts/docs/userguide/supported-algorithms.html
  9. ↑ K. Team, “Keras documentation: Ftrl.” Accessed: Nov. 30, 2024. [Online]. Available: https://keras.io/api/optimizers/ftrl/

Stewards: Nathan Preuss, Wei-Han Chen, Tianqi Xiao, Guoqing Hu

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