6 research outputs found
Probabilistic Spatial Transformers for Bayesian Data Augmentation
High-capacity models require vast amounts of data, and data augmentation is a
common remedy when this resource is limited. Standard augmentation techniques
apply small hand-tuned transformations to existing data, which is a brittle
process that realistically only allows for simple transformations. We propose a
Bayesian interpretation of data augmentation where the transformations are
modelled as latent variables to be marginalized, and show how these can be
inferred variationally in an end-to-end fashion. This allows for significantly
more complex transformations than manual tuning, and the marginalization
implies a form of test-time data augmentation. The resulting model can be
interpreted as a probabilistic extension of spatial transformer networks.
Experimentally, we demonstrate improvements in accuracy and uncertainty
quantification in image and time series classification tasks.Comment: Submitted to the International Conference on Machine Learning (ICML),
202
Last Layer Marginal Likelihood for Invariance Learning
Data augmentation is often used to incorporate inductive biases into models.
Traditionally, these are hand-crafted and tuned with cross validation. The
Bayesian paradigm for model selection provides a path towards end-to-end
learning of invariances using only the training data, by optimising the
marginal likelihood. We work towards bringing this approach to neural networks
by using an architecture with a Gaussian process in the last layer, a model for
which the marginal likelihood can be computed. Experimentally, we improve
performance by learning appropriate invariances in standard benchmarks, the low
data regime and in a medical imaging task. Optimisation challenges for
invariant Deep Kernel Gaussian processes are identified, and a systematic
analysis is presented to arrive at a robust training scheme. We introduce a new
lower bound to the marginal likelihood, which allows us to perform inference
for a larger class of likelihood functions than before, thereby overcoming some
of the training challenges that existed with previous approaches
The Long Arc of Fairness:Formalisations and Ethical Discourse
In recent years, the idea of formalising and modelling fairness for
algorithmic decision making (ADM) has advanced to a point of sophisticated
specialisation. However, the relations between technical (formalised) and
ethical discourse on fairness are not always clear and productive. Arguing for
an alternative perspective, we review existing fairness metrics and discuss
some common issues. For instance, the fairness of procedures and distributions
is often formalised and discussed statically, disregarding both structural
preconditions of the status quo and downstream effects of a given intervention.
We then introduce dynamic fairness modelling, a more comprehensive approach
that realigns formal fairness metrics with arguments from the ethical
discourse. A dynamic fairness model incorporates (1) ethical goals, (2) formal
metrics to quantify decision procedures and outcomes and (3) mid-term or
long-term downstream effects. By contextualising these elements of
fairness-related processes, dynamic fairness modelling explicates formerly
latent ethical aspects and thereby provides a helpful tool to navigate
trade-offs between different fairness interventions. To illustrate the
framework, we discuss an example application -- the current European efforts to
increase the number of women on company boards, e.g. via quota solutions -- and
present early technical work that fits within our framework