Summary of Optimistic Information Directed Sampling, by Gergely Neu et al.
Optimistic Information Directed Sampling
by Gergely Neu, Matteo Papini, Ludovic Schwartz
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a new algorithmic template called Optimistic Information-Directed Sampling for online learning in contextual bandit problems. The loss function is assumed to belong to a known parametric function class. By combining insights from Bayesian and worst-case theories, the proposed framework achieves instance-dependent regret guarantees similar to classic Bayesian methods but without requiring Bayesian assumptions. The key innovation lies in introducing an optimistic surrogate model for the regret and defining a frequentist version of the Information Ratio. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how machines learn online when they don’t know everything about the situation beforehand. It gives us a new way to design algorithms that can make good decisions even when there’s uncertainty involved. This is important because it lets us create systems that can adapt quickly and efficiently in real-life situations. |
Keywords
* Artificial intelligence * Loss function * Online learning
