Learning Graphical Models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. (Wikipedia)
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CanITrustMyFairnessMetric?AssessingFairness withUnlabeledDataandBayesianInference
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We investigate the problem of reliably assessing group fairness when labeled examples are few but unlabeled examples are plentiful.
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Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach
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Suppose an online platform wants to compare a treatment and control policy, e.g.,
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697200c9d1710c2799720b660abd11bb-Paper-Conference.pdf
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Bayesian model evidence gives a clear criteria for such model selection. However, computing model evidence requires integration over the likelihood, which is challenging, particularly when the likelihood is non-closed-form and/or expensive.
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Understanding Square Lossin Training Overparameterized Neural Network Classifiers
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Let defined denote conditional (x)= P(y =1|x).
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