Summary of Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise, by Shuyao Li et al.
Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise
by Shuyao Li, Sushrut Karmalkar, Ilias Diakonikolas, Jelena Diakonikolas
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Structures and Algorithms (cs.DS); Optimization and Control (math.OC); Machine Learning (stat.ML)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of learning a single neuron in the presence of adversarial distribution shifts. They aim to find a “best-fit” function by minimizing the squared loss with respect to the worst-case distribution that is close to the reference distribution. The authors design an efficient algorithm that recovers a vector satisfying a certain condition, which involves expectations and risks. This algorithm follows a primal-dual framework and directly bounds the risk with respect to the original non-convex loss function. The paper’s contributions open up new avenues for designing algorithms under structured non-convexity in machine learning. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists investigate how to learn a single neuron when the data is changed unexpectedly. They try to find the best way to describe this unexpected change and then use it to make predictions. The researchers create an efficient method that can handle these changes well. This work has important implications for making computers smarter. |
Keywords
* Artificial intelligence * Loss function * Machine learning