Summary of Linear Contextual Bandits with Hybrid Payoff: Revisited, by Nirjhar Das et al.
Linear Contextual Bandits with Hybrid Payoff: Revisited
by Nirjhar Das, Gaurav Sinha
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Linear Contextual Bandit problem is studied in a hybrid reward setting, where each arm’s reward model contains both shared and arm-specific parameters. The authors show that this setting can be reduced to two simpler settings: the Shared setting, with no arm-specific parameters, and the Disjoint setting, with only arm-specific parameters. Two popular algorithms, LinUCB and DisLinUCB, are applied to these settings, and new regret analyses are provided for both. A new algorithm, HyLinUCB, is introduced that modifies LinUCB to account for sparsity in the hybrid setting. The authors prove that HyLinUCB incurs only O(sqrt(T)) regret, where T is the number of rounds. Empirical experiments on synthetic and real-world datasets demonstrate strong performance of HyLinUCB. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists study how to make smart choices when there are many possible options. They look at a special problem called the Linear Contextual Bandit problem, which has two types of rewards: shared and arm-specific. The researchers show that this complex problem can be broken down into simpler ones, and they use two popular methods, LinUCB and DisLinUCB, to solve these problems. They also create a new method, HyLinUCB, that works well in the complicated setting with many options. |
