Summary of Upper Bounds For Learning in Reproducing Kernel Hilbert Spaces For Non Iid Samples, by Priyanka Roy and Susanne Saminger-platz
Upper Bounds for Learning in Reproducing Kernel Hilbert Spaces for Non IID Samples
by Priyanka Roy, Susanne Saminger-Platz
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Functional Analysis (math.FA)
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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 The paper proposes a novel stochastic gradient algorithm based on Markov chains, which can approximate the optimal solution of a quadratic loss function in general Hilbert spaces. The authors establish probabilistic upper bounds on the convergence of this algorithm and extend the results to an online regularized learning algorithm in reproducing kernel Hilbert spaces. This approach is particularly useful when dealing with non-i.i.d. samples drawn along a Markov chain trajectory. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at a new way to find the best solution for a problem by using a special kind of math called Markov chains. It’s trying to solve a tricky equation that has a “quadratic loss function” which means it gets more complicated as you try to solve it. The authors want to make sure their method works well and they do this by showing how close they get to the perfect answer. They also show how this method can be used when the data isn’t randomly chosen, but is following a pattern. |
Keywords
* Artificial intelligence * Loss function
