Summary of Machine Learning Robustness: a Primer, by Houssem Ben Braiek and Foutse Khomh
Machine Learning Robustness: A Primer
by Houssem Ben Braiek, Foutse Khomh
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Software Engineering (cs.SE)
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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 Machine Learning (ML) education summary: The paper explores the foundational concept of robustness in ML, which enables models to maintain stable performance across varied environmental conditions. Robustness is discussed through several lenses, including its complementarity with generalizability and its status as a requirement for trustworthy Artificial Intelligence (AI) systems. The paper also delves into factors that impede robustness, such as data bias, model complexity, and underspecified ML pipelines. Techniques for robustness assessment include adversarial attacks, non-adversarial data shifts, and Deep Learning software testing methodologies. Strategies to bolster robustness include data-centric approaches like debiasing and augmentation, as well as model-centric methods like transfer learning, adversarial training, and randomized smoothing. The paper highlights ongoing challenges in estimating and achieving ML robustness and offers insights for future research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A science communication summary: This chapter is about making sure that artificial intelligence systems are trustworthy. Trustworthiness means the system can work well even when things change unexpectedly. The chapter explains what makes some artificial intelligence systems more trustworthy than others, like how they handle unexpected changes in data or situations. It also talks about what gets in the way of trustworthiness, such as biased data or complicated models. To make sure artificial intelligence systems are trustworthy, scientists use special techniques to test them and make them better. This chapter is important because it helps us understand why we need to be careful when building artificial intelligence systems that can affect our lives. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Transfer learning
