2,330 research outputs found
A New PAC-Bayesian Perspective on Domain Adaptation
We study the issue of PAC-Bayesian domain adaptation: We want to learn, from
a source domain, a majority vote model dedicated to a target one. Our
theoretical contribution brings a new perspective by deriving an upper-bound on
the target risk where the distributions' divergence---expressed as a
ratio---controls the trade-off between a source error measure and the target
voters' disagreement. Our bound suggests that one has to focus on regions where
the source data is informative.From this result, we derive a PAC-Bayesian
generalization bound, and specialize it to linear classifiers. Then, we infer a
learning algorithmand perform experiments on real data.Comment: Published at ICML 201
Does Confidence Reporting from the Crowd Benefit Crowdsourcing Performance?
We explore the design of an effective crowdsourcing system for an -ary
classification task. Crowd workers complete simple binary microtasks whose
results are aggregated to give the final classification decision. We consider
the scenario where the workers have a reject option so that they are allowed to
skip microtasks when they are unable to or choose not to respond to binary
microtasks. Additionally, the workers report quantized confidence levels when
they are able to submit definitive answers. We present an aggregation approach
using a weighted majority voting rule, where each worker's response is assigned
an optimized weight to maximize crowd's classification performance. We obtain a
couterintuitive result that the classification performance does not benefit
from workers reporting quantized confidence. Therefore, the crowdsourcing
system designer should employ the reject option without requiring confidence
reporting.Comment: 6 pages, 4 figures, SocialSens 2017. arXiv admin note: text overlap
with arXiv:1602.0057
Acyclic Games and Iterative Voting
We consider iterative voting models and position them within the general
framework of acyclic games and game forms. More specifically, we classify
convergence results based on the underlying assumptions on the agent scheduler
(the order of players) and the action scheduler (which better-reply is played).
Our main technical result is providing a complete picture of conditions for
acyclicity in several variations of Plurality voting. In particular, we show
that (a) under the traditional lexicographic tie-breaking, the game converges
for any order of players under a weak restriction on voters' actions; and (b)
Plurality with randomized tie-breaking is not guaranteed to converge under
arbitrary agent schedulers, but from any initial state there is \emph{some}
path of better-replies to a Nash equilibrium. We thus show a first separation
between restricted-acyclicity and weak-acyclicity of game forms, thereby
settling an open question from [Kukushkin, IJGT 2011]. In addition, we refute
another conjecture regarding strongly-acyclic voting rules.Comment: some of the results appeared in preliminary versions of this paper:
Convergence to Equilibrium of Plurality Voting, Meir et al., AAAI 2010;
Strong and Weak Acyclicity in Iterative Voting, Meir, COMSOC 201
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