The recently proposed Minimal Complexity Machine (MCM) finds a hyperplane classifier by minimizing an exact bound on the Vapnik-Chervonenkis (VC) dimension. The VC dimension measures the capacity of a learning machine, and a smaller VC dimension leads to improved generalization. On many benchmark datasets, the MCM gene…
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6 results for “MCM”
New framework for weakly supervised learning from label proportions.
problem Lack of consistent learning procedure and theoretical training criterion for LLP.
method Pose LLP as mutual contamination models (MCMs) and establish unbiased losses and generalization error bounds.
result Established novel technical results for MCMs and proposed a new experimental setting.
Multi-Context Models for Reasoning under Partial Knowledge: Generative Process and Inference Grammarcs.AI
Arriving at the complete probabilistic knowledge of a domain, i.e., learning how all variables interact, is indeed a demanding task. In reality, settings often arise for which an individual merely possesses partial knowledge of the domain, and yet, is expected to give adequate answers to a variety of posed queries. Tha…
Selective imputation improves treatment effect estimation from missing data.
problem Missing data complicates treatment effect estimation, especially with treatment variables.
method Introduced mixed confounded missingness (MCM) and selective imputation.
result Selective imputation provides unbiased treatment effect estimates.
An almost Robinson structure on an -dimensional Lorentzian manifold $(\mcM,g)$, where , , is a complex -plane distribution $\mcN$ that is totally null with respect to the complexified metric, and intersects its complex conjugate in a real null line distribution $\mcK$, say. When $\mcN$ an…
BERT improves machines' ethical and moral decision-making.
problem Teaching machines ethical and moral choices.
method Applying BERT to human texts to extract moral bias scores.
result BERT improves the moral compass of machines.