79 research outputs found
Regression with Sensor Data Containing Incomplete Observations
This paper addresses a regression problem in which output label values are
the results of sensing the magnitude of a phenomenon. A low value of such
labels can mean either that the actual magnitude of the phenomenon was low or
that the sensor made an incomplete observation. This leads to a bias toward
lower values in labels and its resultant learning because labels may have lower
values due to incomplete observations, even if the actual magnitude of the
phenomenon was high. Moreover, because an incomplete observation does not
provide any tags indicating incompleteness, we cannot eliminate or impute them.
To address this issue, we propose a learning algorithm that explicitly models
incomplete observations corrupted with an asymmetric noise that always has a
negative value. We show that our algorithm is unbiased as if it were learned
from uncorrupted data that does not involve incomplete observations. We
demonstrate the advantages of our algorithm through numerical experiments
Who Benefits from a Multi-Cloud Market? A Trading Networks Based Analysis
In enterprise cloud computing, there is a big and increasing investment to
move to multi-cloud computing, which allows enterprises to seamlessly utilize
IT resources from multiple cloud providers, so as to take advantage of
different cloud providers' capabilities and costs. This investment raises
several key questions: Will multi-cloud always be more beneficial to the cloud
users? How will this impact the cloud providers? Is it possible to create a
multi-cloud market that is beneficial to all participants?
In this work, we begin addressing these questions by using the game theoretic
model of trading networks and formally compare between the single and
multi-cloud markets. This comparson a) provides a sufficient condition under
which the multi-cloud network can be considered more efficient than the single
cloud one in the sense that a centralized coordinator having full information
can impose an outcome that is strongly Pareto-dominant for all players and b)
shows a surprising result that without centralized coordination, settings are
possible in which even the cloud buyers' utilities may decrease when moving
from a single cloud to a multi-cloud network. As these two results emphasize
the need for centralized coordination to ensure a Pareto-dominant outcome and
as the aforementioned Pareto-dominant result requires truthful revelation of
participant's private information, we provide an automated mechanism design
(AMD) approach, which, in the Bayesian setting, finds mechanisms which result
in expectation in such Pareto-dominant outcomes, and in which truthful
revelation of the parties' private information is the dominant strategy. We
also provide empirical analysis to show the validity of our AMD approach
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