Summary of Replicability in High Dimensional Statistics, by Max Hopkins et al.
Replicability in High Dimensional Statistics
by Max Hopkins, Russell Impagliazzo, Daniel Kane, Sihan Liu, Christopher Ye
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computational Complexity (cs.CC); Data Structures and Algorithms (cs.DS); 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 This paper addresses the replicability crisis in empirical science by studying the computational and statistical cost of replicable learning algorithms. Building on previous work by [Impagliazzo et al., 2022], which introduced replicable learning algorithms for 1-dimensional tasks, this research extends the concept to high-dimensional statistical tasks such as multi-hypothesis testing and mean estimation. The study focuses on evaluating the feasibility of these tasks using various evaluation metrics, datasets, and benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about making sure that scientific discoveries can be repeated and verified. The researchers are trying to figure out how to make complex statistical calculations more reliable and efficient. They’re building on previous work that showed it’s possible to do this for simpler tasks, but now they’re exploring how to apply these ideas to bigger problems like testing many hypotheses at once or estimating averages in high-dimensional data. |
