Robust Machine Learning by Transforming and Augmenting Imperfect
  Training Data
Elliot Creager
Machine Learning (ML) is an expressive framework for turning data into
computer programs. Across many problem domains -- both in industry and policy
settings -- the types of computer programs needed for accurate prediction or
optimal control are difficult to write by hand. On the other hand, collecting
instances of desired system behavior may be relatively more feasible. This
makes ML broadly appealing, but also induces data sensitivities that often
manifest as unexpected failure modes during deployment. In this sense, the
training data available tend to be imperfect for the task at hand. This thesis
explores several data sensitivities of modern machine learning and how to
address them. We begin by discussing how to prevent ML from codifying prior
human discrimination measured in the training data, where we take a fair
representation learning approach. We then discuss the problem of learning from
data containing spurious features, which provide predictive fidelity during
training but are unreliable upon deployment. Here we observe that insofar as
standard training methods tend to learn such features, this propensity can be
leveraged to search for partitions of training data that expose this
inconsistency, ultimately promoting learning algorithms invariant to spurious
features. Finally, we turn our attention to reinforcement learning from data
with insufficient coverage over all possible states and actions. To address the
coverage issue, we discuss how causal priors can be used to model the
single-step dynamics of the setting where data are collected. This enables a
new type of data augmentation where observed trajectories are stitched together
to produce new but plausible counterfactual trajectories.