Hidden in Plain Sight: Subgroup Shifts Escape OOD Detection

The safe application of machine learning systems in healthcare relies on valid performance claims. Such claims are typically established in a clinical validation setting designed to be as close as possible to the intended use, but inadvertent domain or population shifts remain a fundamental problem. In particular, subgroups may be differently represented in the data distribution in the validation compared to the application setting. For example, algorithms trained on population cohort data spanning all age groups may be predominantly applied in elderly people. While these data are not “out-of distribution”, changes in the prevalence of different subgroups may have considerable impact on algorithm performance or will at least render original performance claims invalid. Both are serious problems for safely deploying machine learning systems. In this paper, we demonstrate the fundamental limitations of individual example out-of-distribution detection for such scenarios, and show that subgroup shifts can be detected on a population-level instead. We formulate population-level shift detection in the framework of statistical hypothesis testing and show that recent state-of-the-art statistical tests can be effectively applied to subgroup shift detection in a synthetic scenario as well as real histopathology images

Fast and Scalable Score-Based Kernel Calibration Tests

We introduce the Kernel Calibration Conditional Stein Discrepancy test (KCCSD test), a nonparametric, kernel-based test for assessing the calibration of probabilistic models with well-defined scores. In contrast to previous methods, our test avoids the need for possibly expensive expectation approximations while providing control over its type-I error. We achieve these improvements by using a new family of kernels for score-based probabilities that can be estimated without probability density samples, and by using a conditional goodness-of-fit criterion for the KCCSD test's U-statistic. We demonstrate the properties of our test on various synthetic settings

Eyes on the Prize: Creating Lifelong Learners Through Engagement with Assessment

In his seminal RSA lecture, Sir Ken Robinson summed up the prevailing view of assessment as one where to every question, there is one answer… and it is at the back of the book (Robinson 2010). Assessment, in the world Robinson describes, is seen as summative and is populated with predetermined outcomes that students feel they have to meet

Thinking like a teacher: Is the early career framework the answer to early career teachers’ prayers?

It’s a declaration that every teacher educator has heard from their trainees: ‘I’ve passed all the teaching standards so now I am a teacher!’ The current structure of teacher education encourages the view that training is a series of experiences that, once completed, provide confirmation of competency rather than a construction of a robust and well rooted professional identity that we identified in a previous blog post (Wolstencroft & Gretton, 2020). Since the articulation of required teacher knowledge was captured in the form of teacher standards, it has been assumed that knowledge occurs at the point of performance (Verran et al., 2007), something which may not be wholly true as these performances must be consistently re-enacted into their own practice to demonstrate that any real learning has occurred (Tenenberg, 2016)

When the subject becomes the object: Redefining the teacher standards as learner standards

Blog Pos

Kernel Sequential Monte Carlo

We propose kernel sequential Monte Carlo (KSMC), a framework for sampling from static target densities. KSMC is a family of sequential Monte Carlo algorithms that are based on building emulator models of the current particle system in a reproducing kernel Hilbert space. We here focus on modelling nonlinear covariance structure and gradients of the target. The emulator’s geometry is adaptively updated and subsequently used to inform local proposals. Unlike in adaptive Markov chain Monte Carlo, continuous adaptation does not compromise convergence of the sampler. KSMC combines the strengths of sequental Monte Carlo and kernel methods: superior performance for multimodal targets and the ability to estimate model evidence as compared to Markov chain Monte Carlo, and the emulator’s ability to represent targets that exhibit high degrees of nonlinearity. As KSMC does not require access to target gradients, it is particularly applicable on targets whose gradients are unknown or prohibitively expensive. We describe necessary tuning details and demonstrate the benefits of the the proposed methodology on a series of challenging synthetic and real-world examples

Geometrical Insights for Implicit Generative Modeling

Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion. A careful look at the geometries induced by these distances on the space of probability measures reveals interesting differences. In particular, we can establish surprising approximate global convergence guarantees for the $1$-Wasserstein distance,even when the parametric generator has a nonconvex parametrization.Comment: this version fixes a typo in a definitio