Summary of Robust Feature Learning For Multi-index Models in High Dimensions, by Alireza Mousavi-hosseini and Adel Javanmard and Murat A. Erdogdu
Robust Feature Learning for Multi-Index Models in High Dimensions
by Alireza Mousavi-Hosseini, Adel Javanmard, Murat A. Erdogdu
First submitted to arxiv on: 21 Oct 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 investigates feature learning with neural networks, specifically exploring its implications in adversarial settings. Building upon prior works that demonstrate efficient recovery of low-dimensional projections, the authors prove that hidden directions in a multi-index model can provide a Bayes-optimal low-dimensional projection for robustness against _2-bounded perturbations under squared loss. This leads to the surprising finding that robust learning is just as easy as standard learning, with no additional sample complexity depending on dimensionality. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper explores how neural networks learn features and uses them in situations where an attacker might try to trick the system by changing some of the input data. The authors show that even if an attacker tries to make small changes to the inputs, the network can still learn good features that are robust against these attacks. |
