Summary of Aggregating Data For Optimal and Private Learning, by Sushant Agarwal et al.
Aggregating Data for Optimal and Private Learning
by Sushant Agarwal, Yukti Makhija, Rishi Saket, Aravindan Raghuveer
First submitted to arxiv on: 28 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 investigates two learning frameworks, Multiple Instance Regression (MIR) and Learning from Label Proportions (LLP), which are commonly used in applications where training data is partitioned into bags with aggregate labels. The authors study the optimal way to partition datasets into bags for various loss functions in MIR and LLP, focusing on maximizing utility for downstream tasks like linear regression. They provide theoretical guarantees for utility and show that the optimal bagging strategy reduces to finding an optimal clustering of feature vectors or labels. Additionally, they demonstrate how their mechanisms can be made label-differentially private with some utility error. The authors also generalize their results to Generalized Linear Models (GLMs) and experimentally validate their findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at two ways that computers learn from data when all we know is the average of the labels for each group of data points. It’s like trying to figure out what a bunch of people are thinking just by knowing how many agree or disagree with something. The authors want to find the best way to group similar data points together so that they can make good predictions later on. They show that this is related to finding clusters in the data, which is like grouping people who have similar opinions. They also figure out how to keep their method private and secure while still making it useful. |
Keywords
» Artificial intelligence » Bagging » Clustering » Linear regression » Regression
