Summary of Lower Bounds For Time-varying Kernelized Bandits, by Xu Cai and Jonathan Scarlett
Lower Bounds for Time-Varying Kernelized Bandits
by Xu Cai, Jonathan Scarlett
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Information Theory (cs.IT); Machine Learning (cs.LG)
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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 tackles the challenge of optimizing black-box functions with noisy observations in non-stationary scenarios. By developing algorithm-independent lower bounds under specific time variation norms, researchers can better understand this problem and improve optimization methods for applications like machine learning. The study focuses on total variation budgets according to function norms, providing a foundation for future work. The results show close proximity to existing upper bounds, with an open question remaining about potential improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to optimize functions when we only get noisy information and the situation changes over time. This is important because it can help us improve machine learning models and make them more reliable. The researchers created a new way to measure how well an algorithm does in this situation, which is important for understanding how to make better algorithms. |
Keywords
* Artificial intelligence * Machine learning * Optimization
