Bounding overwatch

About: Bounding overwatch is a research topic. Over the lifetime, 966 publications have been published within this topic receiving 15156 citations.

Interval-valued regression and classication models in the framework of machine learning

Abstract: We present a new approach for constructing regression and classication models for interval-valued data. The risk functional is considered under a set of probability distributions, resulting from the application of a chosen inferential method to the data, such that the bounding distributions of the set depend on the regression and classication parameter. Two extreme (‘pessimistic’ and ‘optimistic’) strategies of decision making are presented. The method is applicable with many inferential methods and risk functionals. The general theory is presented together with the specic optimisation problems for several scenarios, including the extension of the support vector machine method for interval-valued data.

A Theory of Network Equivalence

Abstract: A family of equivalence tools for bounding network capacities is introduced. Part I treats networks of point-to-point channels. The main result is roughly as follows. Given a network of noisy, independent, memoryless point-to-point channels, a collection of communication demands can be met on the given network if and only if it can be met on another network where each noisy channel is replaced by a noiseless bit pipe with throughput equal to the noisy channel capacity. This result was known previously for the case of a single-source multicast demand. The result given here treats general demands -- including, for example, multiple unicast demands -- and applies even when the achievable rate region for the corresponding demands is unknown in the noiseless network. In part II, definitions of upper and lower bounding channel models for general channels are introduced. By these definitions, a collection of communication demands can be met on a network of independent channels if it can be met on a network where each channel is replaced by its lower bounding model andonly if it can be met on a network where each channel is replaced by its upper bounding model. This work derives general conditions under which a network of noiseless bit pipes is an upper or lower bounding model for a multiterminal channel. Example upper and lower bounding models for broadcast, multiple access, and interference channels are given. It is then shown that bounding the difference between the upper and lower bounding models for a given channel yields bounds on the accuracy of network capacity bounds derived using those models. By bounding the capacity of a network of independent noisy channels by the network coding capacity of a network of noiseless bit pipes, this approach represents one step towards the goal of building computational tools for bounding network capacities.

Bounding analysis as an inadequately specified methodology.

Abstract: The bounding analysis methodology described by Ha-Duong et al. (this issue) is logically incomplete and invites serious misuse and misinterpretation, as their own example and interpretation illustrate. A key issue is the extent to which these problems are inherent in their methodology, and resolvable by a logically complete assessment (such as Monte Carlo or Bayesian risk assessment), as opposed to being general problems in any risk-assessment methodology. I here attempt to apportion the problems between those inherent in the proposed bounding analysis and those that are more general, such as reliance on questionable expert elicitations. I conclude that the specific methodology of Ha-Duong et al. suffers from logical gaps in the definition and construction of inputs, and hence should not be used in the form proposed. Furthermore, the labor required to do a sound bounding analysis is great enough so that one may as well skip that analysis and carry out a more logically complete probabilistic analysis, one that will better inform the consumer of the appropriate level uncertainty. If analysts insist on carrying out a bounding analysis in place of more thorough assessments, extensive analyses of sensitivity to inputs and assumptions will be essential to display uncertainties, arguably more essential than it would be in full probabilistic analyses.

A Theory of Network Equivalence – Part II: Multiterminal Channels

Abstract: A technique for bounding the capacities of networks of independent channels is introduced. Parts I and II treat point-to-point and multiterminal channels, respectively. Bounds are derived using a new tool called a bounding model. Channel 1 is an upper (lower) bounding model for channel 2 if replacing channel 2 by channel 1 in any network yields a new network whose capacity region is a superset (subset) of the capacity region of the original network. This paper derives bounding models from noiseless links, with lower bounding models corresponding to points in the channel's capacity region and upper bounding models corresponding to points in a new channel characterization called an emulation region. Replacing all channels in a network by their noiseless upper (lower) bounding models yields a network of lossless links whose capacity region is a superset (subset) of the capacity region for the original network. This converts a general (often stochastic) network into a network coding instance, enabling the application of tools and results derived in that domain. A channel's upper and lower bounding models differ when the channel can carry more information in some networks than in others. Bounding the difference between upper and lower bounding models bounds both the accuracy of the technique and the price of separating source-network coding from channel coding.

A Unifying Tool for Bounding the Quality of Non-cooperative Solutions in Weighted Congestion Games

Abstract: We present a general technique, based on a primal-dual formulation, for analyzing the quality of self-emerging solutions in weighted congestion games. With respect to traditional combinatorial approaches, the primal-dual schema has at least three advantages: first, it provides an analytic tool which can always be used to prove tight upper bounds for all the cases in which we are able to characterize exactly the polyhedron of the solutions under analysis; secondly, in each such a case the complementary slackness conditions give us an hint on how to construct matching lower bounding instances; thirdly, proofs become simpler and easy to check. For the sake of exposition, we first apply our technique to the problems of bounding the prices of anarchy and stability of exact and approximate pure Nash equilibria, as well as the approximation ratio of the solutions achieved after a one-round walk starting from the empty strategy profile, in the case of affine latency functions and we show how all the known upper bounds for these measures (and some of their generalizations) can be easily reobtained under a unified approach. Then, we use the technique to attack the more challenging setting of polynomial latency functions. In particular, we obtain the first known upper bounds on the price of stability of pure Nash equilibria and on the approximation ratio of the solutions achieved after a one-round walk starting from the empty strategy profile for unweighted players in the cases of quadratic and cubic latency functions.