Summary of Have Askotch: a Neat Solution For Large-scale Kernel Ridge Regression, by Pratik Rathore et al.
Have ASkotch: A Neat Solution for Large-scale Kernel Ridge Regression
by Pratik Rathore, Zachary Frangella, Jiaming Yang, Michał Dereziński, Madeleine Udell
First submitted to arxiv on: 14 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a new solver for kernel ridge regression (KRR), a fundamental computational tool appearing in various fields, including computational chemistry and health analytics. The current solvers for KRR are challenging to scale to large datasets, leading to prohibitive computational and storage costs. To address this issue, the authors introduce ASkotch, a scalable, accelerated, iterative method that provides better solutions faster than existing state-of-the-art solvers. ASkotch achieves linear convergence under certain conditions, relying on the theory of ridge leverage scores and determinantal point processes. The paper demonstrates the superiority of full KRR over inducing points KRR through experiments on 23 large-scale KRR regression and classification tasks from various application domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to solve a big math problem that helps with many things, like predicting what will happen in chemistry or medicine. Right now, solving this problem takes too much time and memory for really big datasets. The authors created a new method called ASkotch that can do it faster and better than the old ways. This means we might be able to use this math tool for even more things in the future. |
Keywords
* Artificial intelligence * Classification * Regression
