Summary of Mode Estimation with Partial Feedback, by Charles Arnal et al.
Mode Estimation with Partial Feedback
by Charles Arnal, Vivien Cabannes, Vianney Perchet
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Information Retrieval (cs.IR); Information Theory (cs.IT); 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 This paper proposes a novel theoretical framework for weakly supervised and active learning in the context of lightly supervised pre-training and online fine-tuning. The authors formalize key aspects of these new learning pipelines by developing a simple problem: estimating the mode of a distribution using partial feedback. By leveraging entropy coding, they show how to acquire optimal information from partial feedback, followed by coarse sufficient statistics for mode identification and adaptation of bandit algorithms to this setting. Ultimately, the paper combines these contributions into a statistically and computationally efficient solution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand new ways that machines can learn from incomplete or unclear information. The authors create a simple problem to study how well we can find the center of a group (mode) when only given some hints about where it is. They show how using entropy coding can help gather the most important information, and then develop methods for identifying the mode based on this feedback. Finally, they put all these ideas together into an efficient way to solve this problem. |
Keywords
* Artificial intelligence * Active learning * Fine tuning * Supervised
