Summary of Replicable Online Learning, by Saba Ahmadi et al.
Replicable Online Learning
by Saba Ahmadi, Siddharth Bhandari, Avrim Blum
First submitted to arxiv on: 20 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 research paper explores the concept of algorithmic replicability in an online setting, building upon previous works such as Impagliazzo et al. (2022), Ghazi et al. (2021), and Ahn et al. (2024). The authors introduce a new model where an adversary generates input sequences from time-varying distributions, and the goal is to design low-regret online algorithms that produce the same sequence of actions when run on two independently sampled input sequences. These algorithms are referred to as adversarially replicable. Unlike previous works (Esfandiari et al., 2022), which focused on replicability under inputs generated independently from a fixed distribution, this paper generalizes the concept to capture both adversarial and iid input sequences, as well as their mixtures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how online algorithms can be designed to produce the same results even when faced with different types of data. The researchers introduce a new way for an algorithm to behave consistently by generating inputs in a special way. They call this “algorithmic replicability”. The goal is to create low-regret algorithms that make the same decisions regardless of the input. This concept can be applied to various situations where data is generated differently, and it’s important to understand how algorithms respond. |
