1,202 research outputs found
Convex Hulls under Uncertainty
We study the convex-hull problem in a probabilistic setting, motivated by the
need to handle data uncertainty inherent in many applications, including sensor
databases, location-based services and computer vision. In our framework, the
uncertainty of each input site is described by a probability distribution over
a finite number of possible locations including a \emph{null} location to
account for non-existence of the point. Our results include both exact and
approximation algorithms for computing the probability of a query point lying
inside the convex hull of the input, time-space tradeoffs for the membership
queries, a connection between Tukey depth and membership queries, as well as a
new notion of \some-hull that may be a useful representation of uncertain
hulls
Multi-objective Robust Strategy Synthesis for Interval Markov Decision Processes
Interval Markov decision processes (IMDPs) generalise classical MDPs by
having interval-valued transition probabilities. They provide a powerful
modelling tool for probabilistic systems with an additional variation or
uncertainty that prevents the knowledge of the exact transition probabilities.
In this paper, we consider the problem of multi-objective robust strategy
synthesis for interval MDPs, where the aim is to find a robust strategy that
guarantees the satisfaction of multiple properties at the same time in face of
the transition probability uncertainty. We first show that this problem is
PSPACE-hard. Then, we provide a value iteration-based decision algorithm to
approximate the Pareto set of achievable points. We finally demonstrate the
practical effectiveness of our proposed approaches by applying them on several
case studies using a prototypical tool.Comment: This article is a full version of a paper accepted to the Conference
on Quantitative Evaluation of SysTems (QEST) 201
Dispersion of Mass and the Complexity of Randomized Geometric Algorithms
How much can randomness help computation? Motivated by this general question
and by volume computation, one of the few instances where randomness provably
helps, we analyze a notion of dispersion and connect it to asymptotic convex
geometry. We obtain a nearly quadratic lower bound on the complexity of
randomized volume algorithms for convex bodies in R^n (the current best
algorithm has complexity roughly n^4, conjectured to be n^3). Our main tools,
dispersion of random determinants and dispersion of the length of a random
point from a convex body, are of independent interest and applicable more
generally; in particular, the latter is closely related to the variance
hypothesis from convex geometry. This geometric dispersion also leads to lower
bounds for matrix problems and property testing.Comment: Full version of L. Rademacher, S. Vempala: Dispersion of Mass and the
Complexity of Randomized Geometric Algorithms. Proc. 47th IEEE Annual Symp.
on Found. of Comp. Sci. (2006). A version of it to appear in Advances in
Mathematic
On the lattice structure of probability spaces in quantum mechanics
Let C be the set of all possible quantum states. We study the convex subsets
of C with attention focused on the lattice theoretical structure of these
convex subsets and, as a result, find a framework capable of unifying several
aspects of quantum mechanics, including entanglement and Jaynes' Max-Ent
principle. We also encounter links with entanglement witnesses, which leads to
a new separability criteria expressed in lattice language. We also provide an
extension of a separability criteria based on convex polytopes to the infinite
dimensional case and show that it reveals interesting facets concerning the
geometrical structure of the convex subsets. It is seen that the above
mentioned framework is also capable of generalization to any statistical theory
via the so-called convex operational models' approach. In particular, we show
how to extend the geometrical structure underlying entanglement to any
statistical model, an extension which may be useful for studying correlations
in different generalizations of quantum mechanics.Comment: arXiv admin note: substantial text overlap with arXiv:1008.416
From Cutting Planes Algorithms to Compression Schemes and Active Learning
Cutting-plane methods are well-studied localization(and optimization)
algorithms. We show that they provide a natural framework to perform
machinelearning ---and not just to solve optimization problems posed by
machinelearning--- in addition to their intended optimization use. In
particular, theyallow one to learn sparse classifiers and provide good
compression schemes.Moreover, we show that very little effort is required to
turn them intoeffective active learning methods. This last property provides a
generic way todesign a whole family of active learning algorithms from existing
passivemethods. We present numerical simulations testifying of the relevance
ofcutting-plane methods for passive and active learning tasks.Comment: IJCNN 2015, Jul 2015, Killarney, Ireland. 2015,
\<http://www.ijcnn.org/\&g
The State-of-the-Art of Set Visualization
Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net
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