Summary of Off-policy Evaluation Of Slate Bandit Policies Via Optimizing Abstraction, by Haruka Kiyohara et al.
Off-Policy Evaluation of Slate Bandit Policies via Optimizing Abstraction
by Haruka Kiyohara, Masahiro Nomura, Yuta Saito
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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 This paper proposes a novel off-policy evaluation (OPE) method for slate contextual bandits, which is crucial in recommender systems, search engines, marketing, and medical applications. The problem arises from the high variance of traditional Inverse Propensity Scoring (IPS) estimators due to large action spaces. To address this challenge, they introduce Latent IPS (LIPS), an OPE estimator that defines importance weights in a low-dimensional slate abstraction space. LIPS minimizes bias and variance through data-driven optimization, making it more effective than existing methods like PseudoInverse (PI). Empirical evaluation shows that LIPS outperforms other estimators, particularly in scenarios with non-linear rewards and large slate spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in computer science. Imagine you’re a doctor trying to figure out which medicine works best for patients based on past data. But the data is messy, and it’s hard to know what medicines were actually used. The researchers created a new way to solve this problem using “slate contextual bandits.” It’s like finding the best medicine by looking at how well different treatments work in similar situations. They tested their method and showed that it works better than other methods when there are many possible treatments and the relationships between them are complicated. |
Keywords
* Artificial intelligence * Optimization
