Summary of Online Data Collection For Efficient Semiparametric Inference, by Shantanu Gupta et al.
Online Data Collection for Efficient Semiparametric Inference
by Shantanu Gupta, Zachary C. Lipton, David Childers
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 presents a novel approach to sequential data collection for estimating target parameters in the presence of multiple data sources and budget constraints. The authors formalize this problem using Online Moment Selection, a semiparametric framework that applies to any parameter identified by a set of moment conditions. They propose two online data collection policies, Explore-then-Commit and Explore-then-Greedy, which use parameter estimates at each step to optimally allocate the remaining budget. The authors prove that both policies achieve zero regret relative to an oracle policy and empirically validate their methods on synthetic and real-world causal effect estimation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how to collect data from different sources in a way that helps us estimate some important numbers. This is a hard problem because we need to decide which data source to use, how many samples to take, and how much it will cost. The authors came up with two new ways to do this: Explore-then-Commit and Explore-then-Greedy. These methods work by using the information we have so far to make better decisions about what data to collect next. The authors tested these methods on some fake data and real-world data, and they showed that they can help us get more accurate estimates. |
