2,453 research outputs found
Asymptotically Truthful Equilibrium Selection in Large Congestion Games
Studying games in the complete information model makes them analytically
tractable. However, large player interactions are more realistically
modeled as games of incomplete information, where players may know little to
nothing about the types of other players. Unfortunately, games in incomplete
information settings lose many of the nice properties of complete information
games: the quality of equilibria can become worse, the equilibria lose their
ex-post properties, and coordinating on an equilibrium becomes even more
difficult. Because of these problems, we would like to study games of
incomplete information, but still implement equilibria of the complete
information game induced by the (unknown) realized player types.
This problem was recently studied by Kearns et al. and solved in large games
by means of introducing a weak mediator: their mediator took as input reported
types of players, and output suggested actions which formed a correlated
equilibrium of the underlying game. Players had the option to play
independently of the mediator, or ignore its suggestions, but crucially, if
they decided to opt-in to the mediator, they did not have the power to lie
about their type. In this paper, we rectify this deficiency in the setting of
large congestion games. We give, in a sense, the weakest possible mediator: it
cannot enforce participation, verify types, or enforce its suggestions.
Moreover, our mediator implements a Nash equilibrium of the complete
information game. We show that it is an (asymptotic) ex-post equilibrium of the
incomplete information game for all players to use the mediator honestly, and
that when they do so, they end up playing an approximate Nash equilibrium of
the induced complete information game. In particular, truthful use of the
mediator is a Bayes-Nash equilibrium in any Bayesian game for any prior.Comment: The conference version of this paper appeared in EC 2014. This
manuscript has been merged and subsumed by the preprint "Robust Mediators in
Large Games": http://arxiv.org/abs/1512.0269
LDC Arabic Treebanks and Associated Corpora: Data Divisions Manual
The Linguistic Data Consortium (LDC) has developed hundreds of data corpora
for natural language processing (NLP) research. Among these are a number of
annotated treebank corpora for Arabic. Typically, these corpora consist of a
single collection of annotated documents. NLP research, however, usually
requires multiple data sets for the purposes of training models, developing
techniques, and final evaluation. Therefore it becomes necessary to divide the
corpora used into the required data sets (divisions). This document details a
set of rules that have been defined to enable consistent divisions for old and
new Arabic treebanks (ATB) and related corpora.Comment: 14 pages; one cove
Private Pareto Optimal Exchange
We consider the problem of implementing an individually rational,
asymptotically Pareto optimal allocation in a barter-exchange economy where
agents are endowed with goods and have preferences over the goods of others,
but may not use money as a medium of exchange. Because one of the most
important instantiations of such economies is kidney exchange -- where the
"input"to the problem consists of sensitive patient medical records -- we ask
to what extent such exchanges can be carried out while providing formal privacy
guarantees to the participants. We show that individually rational allocations
cannot achieve any non-trivial approximation to Pareto optimality if carried
out under the constraint of differential privacy -- or even the relaxation of
\emph{joint} differential privacy, under which it is known that asymptotically
optimal allocations can be computed in two-sided markets, where there is a
distinction between buyers and sellers and we are concerned only with privacy
of the buyers~\citep{Matching}. We therefore consider a further relaxation that
we call \emph{marginal} differential privacy -- which promises, informally,
that the privacy of every agent is protected from every other agent so long as does not collude or share allocation information with other
agents. We show that, under marginal differential privacy, it is possible to
compute an individually rational and asymptotically Pareto optimal allocation
in such exchange economies
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Automatic Identification of Errors in Arabic Handwriting Recognition
Arabic handwriting recognition (HR) is a challenging problem due to Arabic's connected letter forms, consonantal diacritics and rich morphology. In this paper we isolate the task of identification of erroneous words in HR from the task of producing corrections for these words. We consider a variety of linguistic (morphological and syntactic) and non-linguistic features to automatically identify these errors. We also consider a learning curve varying in two dimensions: number of segments and number of n-best hypotheses to train on. We additionally evaluate the performance on different test sets with different degrees of errors in them. Our best approach achieves a roughly ~20% absolute increase in F-score over a simple but reasonable baseline. A detailed error analysis shows that linguistic features, such as lemma models, help improve HR-error detection precisely where we expect them to: semantically inconsistent error words
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CATiB: The Columbia Arabic Treebank
The Columbia Arabic Treebank (CATiB) is a resource for Arabic parsing. CATiB contrasts with previous efforts on Arabic treebanking and treebanking of morphologically rich languages in that it encodes less linguistic information in the interest of speedier annotation of large amounts of text. This paper describes CATiB's representation and annotation procedure, and reports on achieved inter-annotator agreement and annotation speed
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