69,625 research outputs found
Learning and Communications Co-Design for Remote Inference Systems: Feature Length Selection and Transmission Scheduling
In this paper, we consider a remote inference system, where a neural network
is used to infer a time-varying target (e.g., robot movement), based on
features (e.g., video clips) that are progressively received from a sensing
node (e.g., a camera). Each feature is a temporal sequence of sensory data. The
learning performance of the system is determined by (i) the timeliness and (ii)
the temporal sequence length of the features, where we use Age of Information
(AoI) as a metric for timeliness. While a longer feature can typically provide
better learning performance, it often requires more channel resources for
sending the feature. To minimize the time-averaged inference error, we study a
learning and communication co-design problem that jointly optimizes feature
length selection and transmission scheduling. When there is a single
sensor-predictor pair and a single channel, we develop low-complexity optimal
co-designs for both the cases of time-invariant and time-variant feature
length. When there are multiple sensor-predictor pairs and multiple channels,
the co-design problem becomes a restless multi-arm multi-action bandit problem
that is PSPACE-hard. For this setting, we design a low-complexity algorithm to
solve the problem. Trace-driven evaluations suggest that the proposed
co-designs can significantly reduce the time-averaged inference error of remote
inference systems.Comment: 41 pages, 8 figures. The manuscript has been submitted to IEEE
Journal on Selected Areas in Information Theor
Partition MCMC for inference on acyclic digraphs
Acyclic digraphs are the underlying representation of Bayesian networks, a
widely used class of probabilistic graphical models. Learning the underlying
graph from data is a way of gaining insights about the structural properties of
a domain. Structure learning forms one of the inference challenges of
statistical graphical models.
MCMC methods, notably structure MCMC, to sample graphs from the posterior
distribution given the data are probably the only viable option for Bayesian
model averaging. Score modularity and restrictions on the number of parents of
each node allow the graphs to be grouped into larger collections, which can be
scored as a whole to improve the chain's convergence. Current examples of
algorithms taking advantage of grouping are the biased order MCMC, which acts
on the alternative space of permuted triangular matrices, and non ergodic edge
reversal moves.
Here we propose a novel algorithm, which employs the underlying combinatorial
structure of DAGs to define a new grouping. As a result convergence is improved
compared to structure MCMC, while still retaining the property of producing an
unbiased sample. Finally the method can be combined with edge reversal moves to
improve the sampler further.Comment: Revised version. 34 pages, 16 figures. R code available at
https://github.com/annlia/partitionMCM
Reconstructing a Graph from Path Traces
This paper considers the problem of inferring the structure of a network from
indirect observations. Each observation (a "trace") is the unordered set of
nodes which are activated along a path through the network. Since a trace does
not convey information about the order of nodes within the path, there are many
feasible orders for each trace observed, and thus the problem of inferring the
network from traces is, in general, illposed. We propose and analyze an
algorithm which inserts edges by ordering each trace into a path according to
which pairs of nodes in the path co-occur most frequently in the observations.
When all traces involve exactly 3 nodes, we derive necessary and sufficient
conditions for the reconstruction algorithm to exactly recover the graph.
Finally, for a family of random graphs, we present expressions for
reconstruction error probabilities (false discoveries and missed detections)
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