Summary of Experiment Planning with Function Approximation, by Aldo Pacchiano et al.
Experiment Planning with Function Approximation
by Aldo Pacchiano, Jonathan N. Lee, Emma Brunskill
First submitted to arxiv on: 10 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 paper proposes two novel strategies for experiment planning with function approximation in contextual bandit problems. In scenarios where deploying adaptive algorithms is challenging, pre-designing data collection policies becomes crucial. The authors focus on the setting where a large dataset of contexts is available, but not rewards. They propose two approaches: an eluder-based planning and sampling procedure that guarantees optimality depending on the eluder dimension; and a uniform sampler that achieves competitive optimality rates when the number of actions is small. These methods are designed to work with complex reward models, filling a gap in existing research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us plan experiments better in situations where we don’t have enough information about what will happen next. Imagine you’re trying to figure out which medicine works best for a certain illness. You would want to test many different medicines and see how well they work before deciding which one is the best. This paper shows two new ways to do this planning, using something called “function approximation”. It’s like having a special tool that helps you decide which medicines to try first. The authors also explain why these methods are important and how they can help us make better decisions. |
