Summary of Thompson Sampling For Stochastic Bandits with Noisy Contexts: An Information-theoretic Regret Analysis, by Sharu Theresa Jose and Shana Moothedath
Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis
by Sharu Theresa Jose, Shana Moothedath
First submitted to arxiv on: 21 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 research paper proposes novel algorithms for solving stochastic contextual linear bandit problems, where an agent must make decisions based on noisy observations of context. The goal is to design policies that can approximate those of an oracle, which has access to the true reward model and context information. To achieve this, the authors introduce a Thompson sampling algorithm for Gaussian bandits with Gaussian context noise, and analyze its Bayesian regret compared to an oracle’s policy. Additionally, they extend the problem to scenarios where there is a delay in observing the true context after receiving the reward, showing that this can lead to lower Bayesian regret. The proposed algorithms are empirically demonstrated to outperform baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps us understand how machines can make good decisions when the information they use is noisy or corrupted. Imagine you’re playing a game where you need to make choices based on incomplete or wrong information. The authors develop new ways for computers to play this game, called linear bandits, by using Bayesian statistics and algorithms like Thompson sampling. They test their methods against existing approaches and show that they work better in certain situations. This research can lead to improvements in decision-making processes in areas like finance, healthcare, or recommendation systems. |
