Summary of Robust Sparse Regression with Non-isotropic Designs, by Chih-hung Liu et al.
Robust Sparse Regression with Non-Isotropic Designs
by Chih-Hung Liu, Gleb Novikov
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Machine Learning (stat.ML)
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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 A novel technique for designing efficient estimators in sparse linear regression is proposed, capable of handling both oblivious and adaptive adversaries. The method outperforms existing approaches, even when an oblivious adversary simply adds Gaussian noise. A polynomial-time algorithm is developed that accurately recovers the signal up to error O(sqrt(ε)) as long as the number of samples n ≥ Õ(k^2/ε), assuming bounds on the third and fourth moments of the data distribution D. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We develop a way to make linear regression more robust. It’s like trying to find a specific sound in a noisy song, but instead of noise, we have malicious attacks from two types of bad guys: ones that just add random noise and others that try to trick us. Our new method is better than what’s out there now and can even handle when the bad guys are really sneaky. It works fast too! We need a certain number of good data points to make it work, but if we have enough, we can get a pretty accurate answer. |
Keywords
» Artificial intelligence » Linear regression
