913 research outputs found
Random model for RNA interference yields scale free network
We introduce a random bit-string model of post-transcriptional genetic
regulation based on sequence matching. The model spontaneously yields a scale
free network with power law scaling with and also exhibits
log-periodic behaviour. The in-degree distribution is much narrower, and
exhibits a pronounced peak followed by a Gaussian distribution. The network is
of the smallest world type, with the average minimum path length independent of
the size of the network, as long as the network consists of one giant cluster.
The percolation threshold depends on the system size.Comment: 9 pages, 13 figures, submitted to Midterm Conference COSIN on
``Growing Networks and Graphs in Statistical Physics, Finance, Biology and
Social Systems'', Rome, 1-5 September 200
On Profit-Maximizing Pricing for the Highway and Tollbooth Problems
In the \emph{tollbooth problem}, we are given a tree \bT=(V,E) with
edges, and a set of customers, each of whom is interested in purchasing a
path on the tree. Each customer has a fixed budget, and the objective is to
price the edges of \bT such that the total revenue made by selling the paths
to the customers that can afford them is maximized. An important special case
of this problem, known as the \emph{highway problem}, is when \bT is
restricted to be a line.
For the tollbooth problem, we present a randomized -approximation,
improving on the current best -approximation. We also study a
special case of the tollbooth problem, when all the paths that customers are
interested in purchasing go towards a fixed root of \bT. In this case, we
present an algorithm that returns a -approximation, for any
, and runs in quasi-polynomial time. On the other hand, we rule
out the existence of an FPTAS by showing that even for the line case, the
problem is strongly NP-hard. Finally, we show that in the \emph{coupon model},
when we allow some items to be priced below zero to improve the overall profit,
the problem becomes even APX-hard
Statistical Active Learning Algorithms for Noise Tolerance and Differential Privacy
We describe a framework for designing efficient active learning algorithms
that are tolerant to random classification noise and are
differentially-private. The framework is based on active learning algorithms
that are statistical in the sense that they rely on estimates of expectations
of functions of filtered random examples. It builds on the powerful statistical
query framework of Kearns (1993).
We show that any efficient active statistical learning algorithm can be
automatically converted to an efficient active learning algorithm which is
tolerant to random classification noise as well as other forms of
"uncorrelated" noise. The complexity of the resulting algorithms has
information-theoretically optimal quadratic dependence on , where
is the noise rate.
We show that commonly studied concept classes including thresholds,
rectangles, and linear separators can be efficiently actively learned in our
framework. These results combined with our generic conversion lead to the first
computationally-efficient algorithms for actively learning some of these
concept classes in the presence of random classification noise that provide
exponential improvement in the dependence on the error over their
passive counterparts. In addition, we show that our algorithms can be
automatically converted to efficient active differentially-private algorithms.
This leads to the first differentially-private active learning algorithms with
exponential label savings over the passive case.Comment: Extended abstract appears in NIPS 201
Combinatorial Auctions Do Need Modest Interaction
We study the necessity of interaction for obtaining efficient allocations in
subadditive combinatorial auctions. This problem was originally introduced by
Dobzinski, Nisan, and Oren (STOC'14) as the following simple market scenario:
items are to be allocated among bidders in a distributed setting where
bidders valuations are private and hence communication is needed to obtain an
efficient allocation. The communication happens in rounds: in each round, each
bidder, simultaneously with others, broadcasts a message to all parties
involved and the central planner computes an allocation solely based on the
communicated messages. Dobzinski et.al. showed that no non-interactive
(-round) protocol with polynomial communication (in the number of items and
bidders) can achieve approximation ratio better than ,
while for any , there exists -round protocols that achieve
approximation with polynomial
communication; in particular, rounds of interaction suffice to
obtain an (almost) efficient allocation.
A natural question at this point is to identify the "right" level of
interaction (i.e., number of rounds) necessary to obtain an efficient
allocation. In this paper, we resolve this question by providing an almost
tight round-approximation tradeoff for this problem: we show that for any , any -round protocol that uses polynomial communication can only
approximate the social welfare up to a factor of . This in particular implies that
rounds of interaction are necessary for
obtaining any efficient allocation in these markets. Our work builds on the
recent multi-party round-elimination technique of Alon, Nisan, Raz, and
Weinstein (FOCS'15) and settles an open question posed by Dobzinski et.al. and
Alon et. al
Robust Interactive Learning
In this paper we propose and study a generalization of the standard
active-learning model where a more general type of query, class conditional
query, is allowed. Such queries have been quite useful in applications, but
have been lacking theoretical understanding. In this work, we characterize the
power of such queries under two well-known noise models. We give nearly tight
upper and lower bounds on the number of queries needed to learn both for the
general agnostic setting and for the bounded noise model. We further show that
our methods can be made adaptive to the (unknown) noise rate, with only
negligible loss in query complexity
INTERNAL RATINGS SYSTEMS: AN EMPIRICAL APPROACH
The objective of this article is to describe the standard architecture of an internal rating system, based on the theoretical references and empirical evidences of a limited number of banking groups operating in UE, USA and Romania. The first part of the paper sets out the theoretical and conceptual framework and it defines the methodology. The second part is focused on the internal rating system components and its organization.credit risk parameters, risk management, rating assignment
- …