VC dimension
PulseAugur coverage of VC dimension — every cluster mentioning VC dimension across labs, papers, and developer communities, ranked by signal.
3 day(s) with sentiment data
-
New optimal agnostic PAC algorithm matches theoretical learning bounds
Researchers have developed an optimal agnostic PAC algorithm that achieves statistically optimal risk bounds for learning from independent and identically distributed samples. This new algorithm matches existing lower b…
-
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…
-
New research unifies GNN expressivity and geometry, explores random features
Two new arXiv papers explore the theoretical underpinnings of Graph Neural Networks (GNNs). The first paper introduces a framework using empirical Rademacher complexity to unify GNN expressivity and geometry, offering t…
-
New framework enhances model selection with domain knowledge
A new paper introduces a theoretical framework for model selection using cross-validation, particularly when domain knowledge is incorporated. The research establishes deviation bounds based on VC dimension for the enti…
-
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 …
-
Domain generalization research introduces domain shattering dimension
Researchers have introduced a new combinatorial measure called the domain shattering dimension to address a core question in domain generalization. This measure quantifies how many randomly sampled domains are needed to…
-
Contradiction Graphs Precisely Determine VC Dimension
Researchers have introduced a novel method using contradiction graphs to determine the VC dimension of binary concept classes. This approach establishes that the order-m contradiction graph, G_m(H), can ascertain if the…
-
Papers challenge deep learning theory with generalization bound critiques
Two papers, one from 2016 by Zhang et al. and another from 2019 by Nagarajan and Kolter, are discussed for their impact on deep learning theory. The 2016 paper demonstrated that standard neural networks could easily mem…