New ML research defines "self-directed apple tasting" problem

A new research paper explores the "self-directed apple tasting" problem within machine learning, a scenario where a learner only receives true labels for its own correct predictions. The study, building on prior work by Raman et al., establishes a tetrachotomy for expected mistakes in the realizable setting, showing rates can vary based on VC dimension, $\emptyset$-centered star number, and self-directed dimension. In the agnostic setting, the minimax regret is determined by the VC dimension, resulting in rates of $\tilde extrm{\\Theta}(\sqrt{n})$ or $\textrm{\\Theta}(n)$. The paper also highlights various associated tools and features for exploring academic papers, including citation trackers and code finders. AI

IMPACT Introduces a new theoretical framework for partial feedback learning problems in machine learning.

RANK_REASON Academic paper detailing a new theoretical problem in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• arXivLabs
• CatalyzeX Code Finder for Papers
• Connected Papers
• CORE Recommender
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv Recommender
• Influence Flower
• Litmaps
• Raman et al.
• ScienceCast
• scite Smart Citations

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →