Summary of Causal Contextual Bandits with Adaptive Context, by Rahul Madhavan et al.
Causal Contextual Bandits with Adaptive Context
by Rahul Madhavan, Aurghya Maiti, Gaurav Sinha, Siddharth Barman
First submitted to arxiv on: 28 May 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 paper studies a variant of causal contextual bandits where the context is chosen based on an initial intervention. The learner selects an initial action, which reveals a stochastic context, and then chooses a final action to receive a reward. The goal is to learn a policy with maximum expected reward after T rounds. In this setting, every action corresponds to intervening on a node in a known causal graph. The paper extends prior work from the deterministic context setting to obtain simple regret minimization guarantees using an instance-dependent causal parameter. It also proves that the simple regret is essentially tight for a large class of instances. The authors use convex optimization to address the bandit exploration problem and conduct experiments to validate their theoretical results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how a learner can make good decisions when they have some control over what happens next. Imagine you’re playing a game where you get to choose one of two actions, and then based on that choice, you see a new situation that affects your reward. The goal is to figure out the best strategy for choosing those initial actions so that you get the highest total reward. In this case, every action corresponds to changing something in a complex system, and the paper shows how to use mathematical techniques to make good decisions in this kind of situation. |
Keywords
» Artificial intelligence » Optimization
