418 research outputs found
Limitations of the Empirical Fisher Approximation for Natural Gradient Descent
Natural gradient descent, which preconditions a gradient descent update with
the Fisher information matrix of the underlying statistical model, is a way to
capture partial second-order information. Several highly visible works have
advocated an approximation known as the empirical Fisher, drawing connections
between approximate second-order methods and heuristics like Adam. We dispute
this argument by showing that the empirical Fisher---unlike the Fisher---does
not generally capture second-order information. We further argue that the
conditions under which the empirical Fisher approaches the Fisher (and the
Hessian) are unlikely to be met in practice, and that, even on simple
optimization problems, the pathologies of the empirical Fisher can have
undesirable effects.Comment: V3: Minor corrections (typographic errors
Individual Fairness in Pipelines
It is well understood that a system built from individually fair components
may not itself be individually fair. In this work, we investigate individual
fairness under pipeline composition. Pipelines differ from ordinary sequential
or repeated composition in that individuals may drop out at any stage, and
classification in subsequent stages may depend on the remaining "cohort" of
individuals. As an example, a company might hire a team for a new project and
at a later point promote the highest performer on the team. Unlike other
repeated classification settings, where the degree of unfairness degrades
gracefully over multiple fair steps, the degree of unfairness in pipelines can
be arbitrary, even in a pipeline with just two stages.
Guided by a panoply of real-world examples, we provide a rigorous framework
for evaluating different types of fairness guarantees for pipelines. We show
that na\"{i}ve auditing is unable to uncover systematic unfairness and that, in
order to ensure fairness, some form of dependence must exist between the design
of algorithms at different stages in the pipeline. Finally, we provide
constructions that permit flexibility at later stages, meaning that there is no
need to lock in the entire pipeline at the time that the early stage is
constructed
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Depth uncertainty in neural networks
Existing methods for estimating uncertainty in deep learning tend to require multiple forward passes, making them unsuitable for applications where computational resources are limited. To solve this, we perform probabilistic reasoning over the depth of neural networks. Different depths correspond to subnetworks which share weights and whose predictions are combined via marginalisation, yielding model uncertainty. By exploiting the sequential structure of feed-forward networks, we are able to both evaluate our training objective and make predictions with a single forward pass. We validate our approach on real-world regression and image classification tasks. Our approach provides uncertainty calibration, robustness to dataset shift, and accuracies competitive with more computationally expensive baselines
Simulation-based reinforcement learning for real-world autonomous driving
We use reinforcement learning in simulation to obtain a driving system
controlling a full-size real-world vehicle. The driving policy takes RGB images
from a single camera and their semantic segmentation as input. We use mostly
synthetic data, with labelled real-world data appearing only in the training of
the segmentation network.
Using reinforcement learning in simulation and synthetic data is motivated by
lowering costs and engineering effort.
In real-world experiments we confirm that we achieved successful sim-to-real
policy transfer. Based on the extensive evaluation, we analyze how design
decisions about perception, control, and training impact the real-world
performance
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