Summary of Open Problem: Tight Bounds For Kernelized Multi-armed Bandits with Bernoulli Rewards, by Marco Mussi and Simone Drago and Alberto Maria Metelli
Open Problem: Tight Bounds for Kernelized Multi-Armed Bandits with Bernoulli Rewards
by Marco Mussi, Simone Drago, Alberto Maria Metelli
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: 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 We consider Kernelized Bandits (KBs) that optimize a function f : X → [0,1] belonging to the Reproducing Kernel Hilbert Space (RKHS) Hk. Unlike mainstream works on kernelized bandits, which focus on subgaussian noise models, we investigate KBs under Bernoulli noise observations yt ∼ Ber(ft(xt)), where ft(xt) is the parameter of a Bernoulli distribution. The paper aims to draw attention to this open problem in online learning. This work builds upon previous research in multi-armed bandits, logistic bandits, and bandits in metric spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a type of machine learning called kernelized bandits. It’s trying to solve an old problem that hasn’t been solved yet. Normally, people who work on this kind of thing assume that the data they’re using is a mix of correct and incorrect information. But in this case, the data comes from a special kind of distribution where each piece of information has a 0 or 1 chance of being correct. The researchers want to draw attention to this unsolved problem so that other experts can work on it. |
Keywords
* Artificial intelligence * Attention * Machine learning * Online learning
