14 research outputs found
Bayesian Active Learning With Abstention Feedbacks
We study pool-based active learning with abstention feedbacks where a labeler
can abstain from labeling a queried example with some unknown abstention rate.
This is an important problem with many useful applications. We take a Bayesian
approach to the problem and develop two new greedy algorithms that learn both
the classification problem and the unknown abstention rate at the same time.
These are achieved by simply incorporating the estimated average abstention
rate into the greedy criteria. We prove that both algorithms have
near-optimality guarantees: they respectively achieve a
constant factor approximation of the optimal expected or worst-case value of a
useful utility function. Our experiments show the algorithms perform well in
various practical scenarios.Comment: Poster presented at 2019 ICML Workshop on Human in the Loop Learning
2019 (non-archival). arXiv admin note: substantial text overlap with
arXiv:1705.0848
Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it
We empirically show that Bayesian inference can be inconsistent under misspecification in simple linear regression problems, both in a model averaging/selection and in a Bayesian ridge regression setting. We use the standard linear model, which assumes homoskedasticity, whereas the data are heteroskedastic (though, significantly, there are no outliers). As sample size increases, the posterior puts its mass on worse and worse models of ever higher dimension. This is caused by hypercompression, the phenomenon that the posterior puts its mass on distributions that have much larger KL divergence from the ground truth than their average, i.e. the Bayes predictive distribution. To remedy the problem, we equip the likelihood in Bayes' theorem with an exponent called the learning rate, and we propose the SafeBayesian method to learn the learning rate from the data. SafeBayes tends to select small learning rates, and regularizes more, as soon as hypercompression takes place. Its results on our data are quite encouraging
Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It
We empirically show that Bayesian inference can be inconsistent under
misspecification in simple linear regression problems, both in a model
averaging/selection and in a Bayesian ridge regression setting. We use the
standard linear model, which assumes homoskedasticity, whereas the data are
heteroskedastic, and observe that the posterior puts its mass on ever more
high-dimensional models as the sample size increases. To remedy the problem, we
equip the likelihood in Bayes' theorem with an exponent called the learning
rate, and we propose the Safe Bayesian method to learn the learning rate from
the data. SafeBayes tends to select small learning rates as soon the standard
posterior is not `cumulatively concentrated', and its results on our data are
quite encouraging.Comment: 70 pages, 20 figure