1,140 research outputs found
Performance of distributed mechanisms for flow admission in wireless adhoc networks
Given a wireless network where some pairs of communication links interfere
with each other, we study sufficient conditions for determining whether a given
set of minimum bandwidth quality-of-service (QoS) requirements can be
satisfied. We are especially interested in algorithms which have low
communication overhead and low processing complexity. The interference in the
network is modeled using a conflict graph whose vertices correspond to the
communication links in the network. Two links are adjacent in this graph if and
only if they interfere with each other due to being in the same vicinity and
hence cannot be simultaneously active. The problem of scheduling the
transmission of the various links is then essentially a fractional, weighted
vertex coloring problem, for which upper bounds on the fractional chromatic
number are sought using only localized information. We recall some distributed
algorithms for this problem, and then assess their worst-case performance. Our
results on this fundamental problem imply that for some well known classes of
networks and interference models, the performance of these distributed
algorithms is within a bounded factor away from that of an optimal, centralized
algorithm. The performance bounds are simple expressions in terms of graph
invariants. It is seen that the induced star number of a network plays an
important role in the design and performance of such networks.Comment: 21 pages, submitted. Journal version of arXiv:0906.378
Learning from networked examples
Many machine learning algorithms are based on the assumption that training
examples are drawn independently. However, this assumption does not hold
anymore when learning from a networked sample because two or more training
examples may share some common objects, and hence share the features of these
shared objects. We show that the classic approach of ignoring this problem
potentially can have a harmful effect on the accuracy of statistics, and then
consider alternatives. One of these is to only use independent examples,
discarding other information. However, this is clearly suboptimal. We analyze
sample error bounds in this networked setting, providing significantly improved
results. An important component of our approach is formed by efficient sample
weighting schemes, which leads to novel concentration inequalities
Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary -Mixing Processes
Pac-Bayes bounds are among the most accurate generalization bounds for
classifiers learned from independently and identically distributed (IID) data,
and it is particularly so for margin classifiers: there have been recent
contributions showing how practical these bounds can be either to perform model
selection (Ambroladze et al., 2007) or even to directly guide the learning of
linear classifiers (Germain et al., 2009). However, there are many practical
situations where the training data show some dependencies and where the
traditional IID assumption does not hold. Stating generalization bounds for
such frameworks is therefore of the utmost interest, both from theoretical and
practical standpoints. In this work, we propose the first - to the best of our
knowledge - Pac-Bayes generalization bounds for classifiers trained on data
exhibiting interdependencies. The approach undertaken to establish our results
is based on the decomposition of a so-called dependency graph that encodes the
dependencies within the data, in sets of independent data, thanks to graph
fractional covers. Our bounds are very general, since being able to find an
upper bound on the fractional chromatic number of the dependency graph is
sufficient to get new Pac-Bayes bounds for specific settings. We show how our
results can be used to derive bounds for ranking statistics (such as Auc) and
classifiers trained on data distributed according to a stationary {\ss}-mixing
process. In the way, we show how our approach seemlessly allows us to deal with
U-processes. As a side note, we also provide a Pac-Bayes generalization bound
for classifiers learned on data from stationary -mixing distributions.Comment: Long version of the AISTATS 09 paper:
http://jmlr.csail.mit.edu/proceedings/papers/v5/ralaivola09a/ralaivola09a.pd
An approximability-related parameter on graphs―-properties and applications
Graph TheoryInternational audienceWe introduce a binary parameter on optimisation problems called separation. The parameter is used to relate the approximation ratios of different optimisation problems; in other words, we can convert approximability (and non-approximability) result for one problem into (non)-approximability results for other problems. Our main application is the problem (weighted) maximum H-colourable subgraph (Max H-Col), which is a restriction of the general maximum constraint satisfaction problem (Max CSP) to a single, binary, and symmetric relation. Using known approximation ratios for Max k-cut, we obtain general asymptotic approximability results for Max H-Col for an arbitrary graph H. For several classes of graphs, we provide near-optimal results under the unique games conjecture. We also investigate separation as a graph parameter. In this vein, we study its properties on circular complete graphs. Furthermore, we establish a close connection to work by Šámal on cubical colourings of graphs. This connection shows that our parameter is closely related to a special type of chromatic number. We believe that this insight may turn out to be crucial for understanding the behaviour of the parameter, and in the longer term, for understanding the approximability of optimisation problems such as Max H-Col
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