probably approximately correct learning
PulseAugur coverage of probably approximately correct learning — every cluster mentioning probably approximately correct learning across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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New theory defines optimal scale for ML model learnability
Researchers have introduced a new theoretical framework called Scale-Sensitive Shattering to understand the optimal scale for machine learning model learnability and uniform convergence. The findings establish equivalen…
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New framework tackles trajectory planning under agent uncertainty
Researchers have developed a new framework for interactive trajectory planning that accounts for uncertainty in the decisions of other agents. This approach combines Probably Approximately Correct (PAC) learning with Di…
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New SGDe framework compiles workflows for small language models
Researchers have developed Semantic Gradient Descent (SGDe), a novel teacher-student framework designed to compile complex agentic workflows into deterministic structures for enterprise deployment of smaller language mo…