2,029 research outputs found
From average case complexity to improper learning complexity
The basic problem in the PAC model of computational learning theory is to
determine which hypothesis classes are efficiently learnable. There is
presently a dearth of results showing hardness of learning problems. Moreover,
the existing lower bounds fall short of the best known algorithms.
The biggest challenge in proving complexity results is to establish hardness
of {\em improper learning} (a.k.a. representation independent learning).The
difficulty in proving lower bounds for improper learning is that the standard
reductions from -hard problems do not seem to apply in this
context. There is essentially only one known approach to proving lower bounds
on improper learning. It was initiated in (Kearns and Valiant 89) and relies on
cryptographic assumptions.
We introduce a new technique for proving hardness of improper learning, based
on reductions from problems that are hard on average. We put forward a (fairly
strong) generalization of Feige's assumption (Feige 02) about the complexity of
refuting random constraint satisfaction problems. Combining this assumption
with our new technique yields far reaching implications. In particular,
1. Learning 's is hard.
2. Agnostically learning halfspaces with a constant approximation ratio is
hard.
3. Learning an intersection of halfspaces is hard.Comment: 34 page
Fake View Analytics in Online Video Services
Online video-on-demand(VoD) services invariably maintain a view count for
each video they serve, and it has become an important currency for various
stakeholders, from viewers, to content owners, advertizers, and the online
service providers themselves. There is often significant financial incentive to
use a robot (or a botnet) to artificially create fake views. How can we detect
the fake views? Can we detect them (and stop them) using online algorithms as
they occur? What is the extent of fake views with current VoD service
providers? These are the questions we study in the paper. We develop some
algorithms and show that they are quite effective for this problem.Comment: 25 pages, 15 figure
Second-Generation Objects in the Universe: Radiative Cooling and Collapse of Halos with Virial Temperatures Above 10^4 Kelvin
The first generation of protogalaxies likely formed out of primordial gas via
H2-cooling in cosmological minihalos with virial temperatures of a few 1000K.
However, their abundance is likely to have been severely limited by feedback
processes which suppressed H2 formation. The formation of the protogalaxies
responsible for reionization and metal-enrichment of the intergalactic medium,
then had to await the collapse of larger halos. Here we investigate the
radiative cooling and collapse of gas in halos with virial temperatures Tvir >
10^4K. In these halos, efficient atomic line radiation allows rapid cooling of
the gas to 8000 K; subsequently the gas can contract nearly isothermally at
this temperature. Without an additional coolant, the gas would likely settle
into a locally gravitationally stable disk; only disks with unusually low spin
would be unstable. However, we find that the initial atomic line cooling leaves
a large, out-of-equilibrium residual free electron fraction. This allows the
molecular fraction to build up to a universal value of about x(H2) = 10^-3,
almost independently of initial density and temperature. We show that this is a
non--equilibrium freezeout value that can be understood in terms of timescale
arguments. Furthermore, unlike in less massive halos, H2 formation is largely
impervious to feedback from external UV fields, due to the high initial
densities achieved by atomic cooling. The H2 molecules cool the gas further to
about 100K, and allow the gas to fragment on scales of a few 100 Msun. We
investigate the importance of various feedback effects such as
H2-photodissociation from internal UV fields and radiation pressure due to
Ly-alpha photon trapping, which are likely to regulate the efficiency of star
formation.Comment: Revised version accepted by ApJ; some reorganization for clarit
Detecting Sockpuppets in Deceptive Opinion Spam
This paper explores the problem of sockpuppet detection in deceptive opinion
spam using authorship attribution and verification approaches. Two methods are
explored. The first is a feature subsampling scheme that uses the KL-Divergence
on stylistic language models of an author to find discriminative features. The
second is a transduction scheme, spy induction that leverages the diversity of
authors in the unlabeled test set by sending a set of spies (positive samples)
from the training set to retrieve hidden samples in the unlabeled test set
using nearest and farthest neighbors. Experiments using ground truth sockpuppet
data show the effectiveness of the proposed schemes.Comment: 18 pages, Accepted at CICLing 2017, 18th International Conference on
Intelligent Text Processing and Computational Linguistic
Near-optimal Linear Decision Trees for k-SUM and Related Problems
We construct near-optimal linear decision trees for a variety of decision problems in combinatorics and discrete geometry. For example, for any constant
k
, we construct linear decision trees that solve the
k
-SUM problem on
n
elements using
O
(
n
log
2
n
) linear queries. Moreover, the queries we use are comparison queries, which compare the sums of two
k
-subsets; when viewed as linear queries, comparison queries are 2
k
-sparse and have only { â1,0,1} coefficients. We give similar constructions for sorting sumsets A+B and for solving the SUBSET-SUM problem, both with optimal number of queries, up to poly-logarithmic terms.
Our constructions are based on the notion of âinference dimension,â recently introduced by the authors in the context of active classification with comparison queries. This can be viewed as another contribution to the fruitful link between machine learning and discrete geometry, which goes back to the discovery of the VC dimension
Subsampling in Smoothed Range Spaces
We consider smoothed versions of geometric range spaces, so an element of the
ground set (e.g. a point) can be contained in a range with a non-binary value
in . Similar notions have been considered for kernels; we extend them to
more general types of ranges. We then consider approximations of these range
spaces through -nets and -samples (aka
-approximations). We characterize when size bounds for
-samples on kernels can be extended to these more general
smoothed range spaces. We also describe new generalizations for -nets to these range spaces and show when results from binary range spaces can
carry over to these smoothed ones.Comment: This is the full version of the paper which appeared in ALT 2015. 16
pages, 3 figures. In Algorithmic Learning Theory, pp. 224-238. Springer
International Publishing, 201
Optimal estimation for Large-Eddy Simulation of turbulence and application to the analysis of subgrid models
The tools of optimal estimation are applied to the study of subgrid models
for Large-Eddy Simulation of turbulence. The concept of optimal estimator is
introduced and its properties are analyzed in the context of applications to a
priori tests of subgrid models. Attention is focused on the Cook and Riley
model in the case of a scalar field in isotropic turbulence. Using DNS data,
the relevance of the beta assumption is estimated by computing (i) generalized
optimal estimators and (ii) the error brought by this assumption alone. Optimal
estimators are computed for the subgrid variance using various sets of
variables and various techniques (histograms and neural networks). It is shown
that optimal estimators allow a thorough exploration of models. Neural networks
are proved to be relevant and very efficient in this framework, and further
usages are suggested
A preliminary approach to the multilabel classification problem of Portuguese juridical documents
Portuguese juridical documents from Supreme Courts and the Attorney Generalâs Office are manually classified by juridical experts into a set of classes belonging to a taxonomy of concepts. In this paper, a preliminary approach to develop techniques to automat- ically classify these juridical documents, is proposed. As basic strategy, the integration of natural language processing techniques with machine learning ones is used. Support Vector Machines (SVM) are used as learn- ing algorithm and the obtained results are presented and compared with other approaches, such as C4.5 and Naive Bayes
Learning from Minimum Entropy Queries in a Large Committee Machine
In supervised learning, the redundancy contained in random examples can be
avoided by learning from queries. Using statistical mechanics, we study
learning from minimum entropy queries in a large tree-committee machine. The
generalization error decreases exponentially with the number of training
examples, providing a significant improvement over the algebraic decay for
random examples. The connection between entropy and generalization error in
multi-layer networks is discussed, and a computationally cheap algorithm for
constructing queries is suggested and analysed.Comment: 4 pages, REVTeX, multicol, epsf, two postscript figures. To appear in
Physical Review E (Rapid Communications
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