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Mayank Agarwal is studying for a Bachelor of Computer Science and Engineering at the RV College of Engineering, Bengaluru, India. Mayank joined a team of three students working alongside the leader of our Machine Learning Library project, Roger Dev (Senior Architect, LexisNexis Risk Solutions Group). The main focus of the Causality Project 2021 is to research and implement some features that will extend our ML Library in this field. Mayank's intern project focused on Independence, Conditional Independence and Directionality, which involved becoming familiar with Reproducing Kernel Hilbert Space and experimenting with various kernels. Since this is a new and groundbreaking area, Mayank had to do a lot of research by reading a number of papers written in the field as well as interpreting the experiments referred to and learning how to apply them. Mayank's project contributes greatly to our Machine Learning Library, helping to accelerate progress on the Causality project. As well as the resources included here, read Mayank's intern blog journal which includes a more in depth look of his work. |
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One of the improved approximation techniques is the Linday Pilla Basak (LPB) method , an approximation technique for a weighted sum of chi-squared random variables. Like LPB, there are various other approximation methods, one of which I used is the Hall–Buckley–Eagleson (HBE) method. The third major feature of RCoT is its excellent runtime performance and scalability relative to other methods. RCoT achieves this by using Random Fourier Features to approximate the working of the previous model, KCIT (Kernel-based Conditional Independence Testing) to a great degree without taking as much computation time. Random Fourier features is a widely used, simple, and effective technique for scaling up kernel methods. This allows approximation with arbitrary precision using a lower dimensional model, giving RCoT unprecedented runtime performance and scalability, even for high dimensional conditioning. I evaluate the runtime and accuracy performance of the resulting RCoT muldule, and compare it to our previous method.
The RCoT algorithm for Independence testing will result in the formation of a concrete foundation for the HPCC Systems Causality Toolkit, which will further enhance their performance using the HPCC Systems Platform's robust parallelization capabilities.
Presentation
In this Video Recording, Mayank provides a tour and explanation of his poster content.
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