1,927 research outputs found
Surrogate Outcomes and Transportability
Identification of causal effects is one of the most fundamental tasks of
causal inference. We consider an identifiability problem where some
experimental and observational data are available but neither data alone is
sufficient for the identification of the causal effect of interest. Instead of
the outcome of interest, surrogate outcomes are measured in the experiments.
This problem is a generalization of identifiability using surrogate experiments
and we label it as surrogate outcome identifiability. We show that the concept
of transportability provides a sufficient criteria for determining surrogate
outcome identifiability for a large class of queries.Comment: This is the version published in the International Journal of
Approximate Reasonin
A quantum causal discovery algorithm
Finding a causal model for a set of classical variables is now a
well-established task---but what about the quantum equivalent? Even the notion
of a quantum causal model is controversial. Here, we present a causal discovery
algorithm for quantum systems. The input to the algorithm is a process matrix
describing correlations between quantum events. Its output consists of
different levels of information about the underlying causal model. Our
algorithm determines whether the process is causally ordered by grouping the
events into causally-ordered non-signaling sets. It detects if all relevant
common causes are included in the process, which we label Markovian, or
alternatively if some causal relations are mediated through some external
memory. For a Markovian process, it outputs a causal model, namely the causal
relations and the corresponding mechanisms, represented as quantum states and
channels. Our algorithm provides a first step towards more general methods for
quantum causal discovery.Comment: 11 pages, 10 figures, revised to match published versio
Online Domain Adaptation for Multi-Object Tracking
Automatically detecting, labeling, and tracking objects in videos depends
first and foremost on accurate category-level object detectors. These might,
however, not always be available in practice, as acquiring high-quality large
scale labeled training datasets is either too costly or impractical for all
possible real-world application scenarios. A scalable solution consists in
re-using object detectors pre-trained on generic datasets. This work is the
first to investigate the problem of on-line domain adaptation of object
detectors for causal multi-object tracking (MOT). We propose to alleviate the
dataset bias by adapting detectors from category to instances, and back: (i) we
jointly learn all target models by adapting them from the pre-trained one, and
(ii) we also adapt the pre-trained model on-line. We introduce an on-line
multi-task learning algorithm to efficiently share parameters and reduce drift,
while gradually improving recall. Our approach is applicable to any linear
object detector, and we evaluate both cheap "mini-Fisher Vectors" and expensive
"off-the-shelf" ConvNet features. We quantitatively measure the benefit of our
domain adaptation strategy on the KITTI tracking benchmark and on a new dataset
(PASCAL-to-KITTI) we introduce to study the domain mismatch problem in MOT.Comment: To appear at BMVC 201
- …