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.
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New research explores robust PAC learning under Cressie--Read divergences
Researchers have published a paper detailing the sample complexity of distributionally robust PAC learning, specifically focusing on Cressie--Read divergences. The study establishes new bounds for hypothesis classes wit…
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New PAC learning approach for stochastic games with private info
Researchers have developed a new approach to PAC learning in turn-based stochastic games (TBSGs) with reachability objectives. This work introduces a method that allows for decentralized learning, where players do not s…
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Quantum ML shows provable learning separation over classical methods
Researchers have demonstrated a provable learning separation for predicting the time-evolution of quantum many-body systems. The study, published on arXiv, outlines a supervised learning problem where quantum machine le…
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New research tackles scientific discovery complexity with PAC learning
A new research paper explores the sample complexity of scientific discovery through the lens of PAC learning, focusing on compositional function trees. The study proves that the generalization quantity, Rademacher compl…
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New combinatorial condition settles proper positive-only learning question
Researchers have settled a long-standing question in machine learning regarding proper positive-only learning. The study establishes that a concept class is properly learnable from positive-only samples if it possesses …
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New minimax PAC bounds for learning in exogenous contextual MDPs
Researchers have developed new minimax PAC bounds for learning in exogenous contextual Markov decision processes (MDPs). The study focuses on tabular discounted MDPs with exogenous, i.i.d. contexts that can influence re…
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New research details sample complexity for bandit learning algorithms
Two new research papers explore the sample complexity of bandit learning algorithms in multiclass classification settings. The first paper introduces the "bandit DS dimension" to characterize sample complexity for PAC l…
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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…