Summary of Conservative Contextual Bandits: Beyond Linear Representations, by Rohan Deb et al.
Conservative Contextual Bandits: Beyond Linear Representations
by Rohan Deb, Mohammad Ghavamzadeh, Arindam Banerjee
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Conservative Contextual Bandits (CCBs) address safety concerns in sequential decision making by requiring policies to minimize regret while also satisfying a safety constraint. This constraint ensures that the agent’s policy does not perform worse than a baseline policy by more than a certain factor (1 + α). The paper develops two algorithms, C-SquareCB and C-FastCB, which use Inverse Gap Weighting (IGW) based exploration and an online regression oracle to satisfy this safety constraint. These algorithms achieve sub-linear regret bounds in the horizon T for C-SquareCB and in cumulative loss L* for C-FastCB. Additionally, using neural networks for function approximation and online gradient descent as the regression oracle leads to improved regret bounds of O(√KT + K/α) and O(√KL* + K (1 + 1/α)). The paper demonstrates the effectiveness of these algorithms on real-world data, showing significant performance improvements over existing baselines while maintaining performance guarantees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re making decisions one by one without knowing what will happen next. You want to make good choices but also ensure that they don’t hurt your previous choices too much. This is called Conservative Contextual Bandits (CCBs). The goal is to balance the need for new information and the risk of making mistakes. Researchers have developed two new ways to do this, which are more effective than existing methods when faced with complex situations where costs aren’t always linear. These new algorithms work well in real-world scenarios and outperform previous approaches. |
Keywords
» Artificial intelligence » Gradient descent » Regression
