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\sessionHeader{Wed3A: Data Mining Process}{Johannes F\"{u}rnkranz}{Chem, LT1}
\timeTitlePeople{16:10--16:35}{A Framework for Evaluating the Smoothness of Data-Mining Results}{Gaurav Misra, Behzad Golshan and Evimaria Terzi} 

\abst{The data-mining literature is rich in problems that are formalized
as combinatorial-optimization problems.
An indicative example is the \emph{entity-selection}
formulation that has been used to model the problem of selecting
a subset of representative reviews from a review
corpus~%\cite{lappas10efficient,tsaparas11selecting}
or important nodes in a
social network.~%\cite{kempe03maximizing}.
Existing combinatorial algorithms for solving such entity-selection
problems identify a set
of entities (e.g., reviews or nodes) as important.
Here, we consider the following question: how do small or
large changes in the input dataset change the value or the structure of the
such reported solutions? 

We answer this question by developing a general framework for
evaluating the \emph{smoothness} (i.e., consistency) of the
data-mining results obtained for the input dataset $X$. We do so by
comparing these results with the results obtained for datasets that
are within a small or a large distance from $X$.  The algorithms we
design allow us to perform such comparisons effectively and thus,
approximate the results' smoothness efficiently.  Our experimental
evaluation on real datasets demonstrates the efficacy and the
practical utility of our framework in a wide range of applications.}


\timeTitlePeople{16:35--17:00}{Coupled Bayesian Sets Algorithm for Semi-supervised Learning and Information Extraction}{Saurabh Verma and Estevam R.\ Hruschka Jr.}

\abst{Our inspiration comes from Nell (Never Ending Language Learning), a computer program running at Carnegie Mellon University to extract structured information from unstructured web pages. We consider the problem of semi-supervised learning approach to extract category instances (e.g.\ country(USA), city(New York)) from web pages, starting with a handful of labeled training examples of each category or relation, plus hundreds of millions of unlabeled web documents. Semi-supervised approaches using a small number of labeled examples together with many unlabeled examples are often unreliable as they frequently produce an internally consistent, but nevertheless, incorrect set of extractions. We believe that this problem can be overcome by simultaneously learning independent classifiers in a new approach named Coupled Bayesian Sets algorithm, based on Bayesian Sets, for many different categories and relations (in the presence of an ontology defining constraints that couple the training of these classifiers). Experimental results show that simultaneously learning a coupled collection of classifiers for random 11 categories resulted in much more accurate extractions than training classifiers through original Bayesian Sets algorithm, Naive Bayes, BaS-all and Coupled Pattern Learner (the category extractor used in NELL).}

\timeTitlePeople{17:00--17:25}{Policy Iteration Based on a Learned Transition Model}{Vivek Ramavajjala and Charles Elkan}

\abst{This paper investigates a reinforcement learning method
that combines learning a model of the environment
with least-squares policy iteration (LSPI).
The LSPI algorithm learns a linear approximation
of the optimal state-action value function;
the idea studied here is to let this value function
depend on a learned estimate of the expected next state
instead of directly on the current state and action.
This approach makes it easier to define useful basis functions,
and hence to learn a useful linear approximation of the value function.
Experiments show that the new algorithm,
called NSPI for next-state policy iteration,
performs well on two standard benchmarks,
the well-known mountain car and inverted pendulum swing-up tasks.
More importantly, the NSPI algorithm performs well,
and better than a specialized recent method,
on a resource management task
known as the day-ahead wind commitment problem.
This latter task has action and state spaces
that are high-dimensional and continuous.}

\sessionend


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\sessionHeader{Wed3B: Sensor Data}{Martin Atzmueller}{Chem, LT2}
\timeTitlePeople{16:10--16:35}{MDL-Based Analysis of Time Series at Multiple Time-Scales}{Ugo Vespier, Arno Knobbe, Siegfried Nijssen and Joaquin Vanschoren}

\abst{The behavior of many complex physical systems is affected by a variety of phenomena occurring at different temporal scales. Time series data produced by measuring properties of such systems often mirrors this fact by appearing as a composition of signals across different time scales. When the final goal of the analysis is to model the individual phenomena affecting a system, it is crucial to be able to recognize the right temporal scales and to separate the individual components of the data. In this paper, we approach this challenge through a combination of the Minimum Description Length (MDL) principle, feature selection strategies, and convolution techniques from the signal processing field. As a result, our algorithm produces a good decomposition of a given time series and, as a side effect, builds a compact representation of its identified components. Experiments demonstrate that our method manages to identify correctly both the number and the temporal scale of the components for real-world as well as 
artificial data and show the usefulness of our method as an exploratory tool for analyzing time series data. }

\timeTitlePeople{16:35--17:00}{Separable Approximate Optimization of Support Vector Machines for Distributed Sensing}
{Sangkyun Lee, Marco Stolpe and Katharina Morik}

\abst{Sensor measurements from diverse locations connected with possibly
  low bandwidth communication channels pose a challenge of
  resource-restricted distributed data analyses. In such settings it
  would be desirable to perform learning in each location as much as
  possible, without transferring all data to a central node. Applying
  the support vector machines (SVMs) with nonlinear kernels becomes
  nontrivial, however.

  In this paper, we present an efficient optimization scheme for
  training SVMs over such sensor networks. Our framework performs
  optimization independently in each node, using only the local
  features stored in the respective node.  We make use of multiple
  local kernels and explicit approximations to the feature mappings
  induced by them. Together they allow us constructing a separable
  surrogate objective that provides an upper bound of the primal SVM
  objective. A central coordination is also designed to adjust the
  weights among local kernels for improved prediction, while
  minimizing communication cost.}


\timeTitlePeople{17:00--17:25}{Unsupervised Inference of Auditory Attention from Biosensors}{Melih Kandemir, Arto Klami, Akos Vetek and Samuel Kaski}

\abst{We study ways of automatically inferring the level of attention a user
is paying to auditory content, with applications for example in
automatic podcast highlighting and auto-pause, as well as in a
selection mechanism in auditory interfaces. In particular, we
demonstrate how the level of attention can be inferred in an
unsupervised fashion, without requiring any labeled training data. The
approach is based on measuring the (generalized) correlation or
synchrony between the auditory content and physiological signals
reflecting the state of the user. We hypothesize that the synchrony is
higher when the user is paying attention to the content, and show
empirically that the level of attention can indeed be inferred based
on the correlation. In particular, we demonstrate that the novel
method of time-varying Bayesian canonical correlation analysis gives
unsupervised prediction accuracy comparable to having trained a
supervised Gaussian process regression with labeled training data
recorded from other users.}

\sessionend


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\sessionHeader{Wed3C: Privacy and Security}{Patrick Gallinari}{Chem, LT3}
\timeTitlePeople{16:10--16:35}{\emph{AUDIO}: An Integrity \underline{Audi}ting Framework of \underline{O}utlier-Mining-as-a-Service Systems}{Ruilin Liu, Hui (Wendy) Wang, Anna Monreale, Dino Pedreschi, Fosca Giannotti and Wenge Guo}

\abst{Spurred by developments such as cloud computing, there has been considerable recent interest in
the data-mining-as-a-service paradigm. Users lacking
in expertise or computational resources can outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises issues about {\em result integrity}: how can the data owner verify that the mining results returned by the server are correct? 
%The challenge is to design integrity verification methods that are not only effective, but also require substantially less computational effort than computing the mining result from scratch, because the benefits of outsourcing would be vanished otherwise. 
In this paper, we present AUDIO, an integrity auditing framework for the
specific task of distance-based outlier mining outsourcing. It provides
efficient and practical verification approaches to check both completeness
and correctness of the mining results. 
The key idea of our approach
is to insert a small amount of artificial tuples into the outsourced data; the
artificial tuples will produce artificial outliers and non-outliers that do
not exist in the original dataset. The server's answer is verified by
analyzing the presence of artificial outliers/non-outliers, obtaining a
probabilistic guarantee of correctness and completeness of the mining result. Our empirical results show the effectiveness and efficiency of our method. }

\timeTitlePeople{16:35--17:00}{Differentially Private Projected Histograms: Construction and Use for Prediction}{Staal A.\ Vinterbo}

\abst{Privacy concerns are among the major barriers to efficient secondary
use of information and data on humans. Differential privacy is a relatively
recent measure that has received much attention in machine learning as it
quantifies individual risk using a strong cryptographically
motivated notion of privacy. 
At the core of differential privacy lies the concept of information
dissemination through a randomized process. One way of adding the
needed randomness to any process is to pre-randomize the input. This
can yield lower quality results than other more specialized
approaches, but can be an attractive alternative when $i.$ there does 
not exist a specialized differentially private alternative, or when
$ii.$ multiple processes applied in parallel can use the same
pre-randomized input.  

A simple way to do input randomization is to compute perturbed
histograms, which essentially are noisy multiset membership
functions. Unfortunately, computation of perturbed 
histograms is only efficient when the data stems from a
low-dimensional discrete space. The restriction to discrete spaces
can be mitigated by discretization; Lei presented in 2011 an analysis of
discretization in the context of M-estimators. Here we address the
restriction regarding the dimensionality of the data. In particular
we present a differentially private approximation algorithm for
selecting features that preserve conditional frequency densities,
and use this to project data prior to computing differentially private
histograms. The resulting projected histograms can be used as
machine learning input and include the
necessary randomness for differential privacy. We empirically
validate the use of differentially private projected histograms for
learning binary and multinomial logistic regression models using
four real world data sets.}


\timeTitlePeople{17:00--17:25}{Fairness-Aware Classifier with Prejudice Remover Regularizer}{Toshihiro Kamishima, Shotaro Akaho, Hideki Asoh and Jun Sakuma}

\abst{With the spread of data mining technologies and the accumulation of social data, such technologies and data are being used for determinations that seriously affect individuals' lives.
For example, credit scoring is frequently determined based on the records of past credit data together with statistical prediction techniques.
Needless to say, such determinations must be nondiscriminatory and fair in sensitive features, such as race, gender, religion, and so on.
%
Several researchers have recently begun to attempt the development of analysis techniques that are aware of social fairness or discrimination.
They have shown that simply avoiding the use of sensitive features is insufficient for eliminating biases in determinations, due to the indirect influence of sensitive information.
In this paper, we first discuss three causes of unfairness in machine learning.
We then propose a regularization approach that is applicable to any prediction algorithm with probabilistic discriminative models.
We further apply this approach to logistic regression and empirically show its effectiveness and efficiency.}

\sessionend

