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\sessionHeader{Thu2A: Graphs, Trees, Sequences and Strings II
}{Albert Bifet}{Chem, LT1}
\timeTitlePeople{14:00--14:25}{An Efficiently Computable Support Measure for Frequent Subgraph Pattern Mining
}{Yuyi Wang and Jan Ramon}

\abst{Graph support measures are functions measuring how frequently a given subgraph pattern occurs in a given database graph. 
An important class of support measures relies on overlap graphs. 
A major advantage of the overlap graph based approaches is that they combine anti-monotonicity 
with counting occurrences of a pattern which are independent according to certain criteria.
However, existing overlap graph based support measures are expensive to compute.

In this paper, we propose a new support measure which is based on a new notion of independence.
% exploits the cliques we have already found in overlap graphs.
We show that our measure is the solution to a linear program which is usually sparse, 
and using interior point methods
%it
 can be computed efficiently.  
We show experimentally that for large networks, in contrast to 
earlier overlap graph based proposals, 
pattern mining based on our support measure is feasible.}


\timeTitlePeople{14:25--14:50}{Efficient Graph Kernels by Randomization
}{Marion Neumann, Novi Patricia, Roman Garnett and Kristian Kersting}

\abst{Learning from complex data is becoming increasingly important, 
and graph kernels have recently evolved into a rapidly developing 
branch of learning on structured data. However, previously proposed kernels
rely on having discrete node label information.
In this paper, we explore the power of continuous node-level features for propagation-based graph kernels.
Specifically, propagation kernels exploit node label distributions from propagation schemes like label propagation,  
which naturally enables the construction of graph kernels for partially labeled graphs. 
In order to efficiently extract graph features from continuous node label distributions, 
and in general from continuous vector-valued node attributes, 
we utilize randomized techniques, which easily allow for deriving similarity measures based on propagated information.
We show that propagation kernels utilizing locality-sensitive hashing reduce the runtime of existing
graph kernels by several orders of magnitude.
We evaluate the performance of various propagation kernels on real-world bioinformatics and image benchmark datasets.}


\timeTitlePeople{14:50--15:15}{Graph Mining for Object Tracking in Videos
}{Fabien Diot, Elisa Fromont, Baptiste Jeudy, Emmanuel Marilly and Olivier Martinot}

\abst{This paper shows a concrete example of the use of graph mining for
tracking objects in videos with moving cameras and without any
contextual information on the objects to track. To make the mining
algorithm efficient, we benefit from a video representation based on
dynamic (evolving through time) planar graphs. We then define a number
of constraints to efficiently find our so-called spatio-temporal graph
patterns. Those patterns are linked through an occurrences graph to
allow us to tackle occlusion or graph features instability problems in
the video. Experiments on synthetic and real videos show that our
method is effective and allows us to find relevant patterns for our
tracking application.}


\timeTitlePeople{15:15--15:40}{Hypergraph Learning with Hyperedge Expansion
}{Li Pu and Boi Faltings}

\abst{We propose a new formulation called \emph{hyperedge expansion} (HE) for hypergraph learning.
The HE expansion transforms the hypergraph into a directed graph on the hyperedge level.
Compared to the existing works (e.g.\ star expansion or normalized hypergraph cut),
the learning results with HE expansion would be less sensitive to the vertex distribution among clusters,
especially in the case that cluster sizes are unbalanced.
Because of the special structure of the auxiliary directed graph, the linear eigenvalue problem of the Laplacian can be transformed into a
quadratic eigenvalue problem, which has some special properties suitable for semi-supervised learning and clustering problems.
We show in the experiments that the new algorithms based on the HE expansion achieves statistically significant gains in classification performance 
and good scalability for the co-occurrence data.}


\sessionend
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\sessionHeader{Thu2B: Classification}{Alessandro Moschitti}{Chem, LT2}
\timeTitlePeople{14:00--14:25}{A Note on Extending Generalization Bounds for Binary Large-Margin Classifiers to Multiple Classes}{\"{U}r\"{u}n Dogan, Tobias Glasmachers and Christian Igel}

\abst{A generic way to extend generalization bounds for binary
  large-margin classifiers to large-margin multi-category classifiers
  is presented. The simple proceeding leads to surprisingly tight
  bounds showing the same $\tilde\Orderx(d^2)$ scaling in the number
  $d$ of classes as state-of-the-art results. The approach is
  exemplified by extending a textbook bound based on Rademacher
  complexity, which leads to a multi-class bound depending on the sum
  of the margin violations of the classifier.}

\timeTitlePeople{14:25--14:50}{Extension of the Rocchio Classification Method to Multi-modal Categorization of Documents in Social Media}{Amin Mantrach and Jean-Michel Renders}

\abst{Most of the approaches in multi-view categorization use early fusion,
late fusion or co-training strategies. We propose here a novel
classification method that is able to efficiently capture the
interactions across the different modes. This method is a multi-modal
extension of the Rocchio classification algorithm -- very popular in
the Information Retrieval community. The extension consists of
simultaneously maintaining different ``centroid'' representations for
each class, in particular ``cross-media'' centroids that correspond to
pairs of modes. To classify new data points, different scores are
derived from similarity measures between the new data point and these
different centroids; a global classification score is finally obtained
by suitably aggregating the individual scores. This method outperforms
the multi-view logistic regression approach (using either the early
fusion or the late fusion strategies) on a social media corpus -
namely the \textrm{ENRON} email collection - on two very different
categorization tasks (folder classification and recipient
prediction).}

%paper489
\timeTitlePeople{14:50--15:15}{Label-Noise Robust Logistic Regression and Its Applications}{Jakramate Bootkrajang and Ata Kab\'{a}n}

\abst{The classical problem of learning a classifier relies on a set of
labelled examples, without ever questioning the correctness of the
provided label assignments. However, there is an increasing
realisation that labelling errors are not uncommon in real situations.
In this paper we consider a label-noise robust version of the logistic
regression and multinomial logistic regression classifiers and develop
the following contributions: (i) We derive efficient multiplicative
updates to estimate the label flipping probabilities, and we give a
proof of convergence for our algorithm.
%(ii) By decomposing and rewriting the model's objective we give a new
%interpretation by which we can understand its ability to counteract
%the negative effect of mislabelling as a result of an intrinsic
%re-weighting mechanism.
(ii) We develop a novel sparsity-promoting regularisation approach
which allows us to tackle challenging high dimensional noisy settings.
(iii) Finally, we throughly evaluate the performance of our approach
in synthetic experiments and we demonstrate several real applications
including gene expression analysis, class topology discovery and
learning from crowdsourcing data.  }

%paper 
\timeTitlePeople{15:15--15:40}{Sentiment Classification with Supervised Sequence Embedding}{Dmitriy Bespalov, Yanjun Qi, Bing Bai and Ali Shokoufandeh}

\abst{In this paper, we introduce a novel approach for modeling $n$-grams
  in a latent space learned from supervised signals. The proposed
  procedure uses only unigram features to model short phrases
  ($n$-grams) in the latent space. The phrases are then combined to
  form document-level latent representation for a given text, where
  position of an $n$-gram in the document is used to compute
  corresponding combining weight.  The resulting two-stage supervised
  embedding is then coupled with a classifier to form an end-to-end
  system that we apply to the large-scale sentiment classification
  task.  The proposed model does not require feature selection to
  retain effective features during pre-processing, and its parameter
  space grows linearly with size of $n$-gram.  We present comparative
  evaluations of this method using two large-scale datasets for
  sentiment classification in online reviews (Amazon and TripAdvisor).
  The proposed method outperforms standard baselines that rely on
  bag-of-words representation populated with $n$-gram features.}


\sessionend


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\sessionHeader{Thu2C: Multi-Task and Transfer Learning II
}{George Paliouras}{Chem, LT3}
\timeTitlePeople{14:00--14:25}{Discriminative Factor Alignment across Heterogeneous Feature Space
}{Fangwei Hu, Tianqi Chen, Nathan N.\ Liu, Qiang Yang and Yong Yu}

\abst{Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from related domains for learning. While most of the existing works in this area are only focused on using the source data with the same representational structure as the target data, in this paper, we push this boundary further by extending transfer between text and images.

We integrate documents , tags and images to build a heterogeneous transfer learning factor alignment model and apply it to improve the performance of tag recommendation. Many algorithms for tag recommendation have been proposed, but many of them have problem; the algorithm may not perform well under cold start conditions or for items from the long tail of the tag frequency distribution. However, with the help of documents, our algorithm handles these problems and generally outperforms other tag recommendation methods, especially the non-transfer factor alignment model.}


\timeTitlePeople{14:25--14:50}{Learning to Perceive Two-Dimensional Displays Using Probabilistic Grammars
}{Nan Li, William W.\ Cohen and Kenneth R.\ Koedinger}

\abst{People learn to read and understand various displays (e.g., tables on webpages and software user interfaces) every day. How do humans learn to process such displays?  Can computers be efficiently taught to understand and use such displays? In this paper, we use statistical learning to model how humans learn to perceive visual displays. We extend an existing probabilistic context-free grammar learner to support learning within a two-dimensional space by incorporating spatial and temporal information. Experimental results in both synthetic domains and real world domains show that the proposed learning algorithm is effective in acquiring user interface layout. Furthermore, we evaluate the effectiveness of the proposed algorithm
within an intelligent tutoring agent, {\it SimStudent}, by integrating the
learned display representation into the agent. Experimental results in
learning complex problem solving skills in three domains show that the
learned display representation is as good as one created by a human
expert, in that skill learning using the learned representation is as
effective as using a manually created representation.}


\timeTitlePeople{14:50--15:15}{Sparse Gaussian Processes for Multi-task Learning
}{Yuyang Wang and Roni Khardon}

\abst{Multi-task learning models using Gaussian processes (GP)
have been recently developed and successfully applied in various
applications. The main difficulty with this approach is the
computational cost of inference using the union of examples from all
tasks.  The paper investigates this problem for the grouped
mixed-effect GP model where each individual response is given by a
fixed-effect, taken from one of a set of unknown groups, plus a random
individual effect function that captures variations among individuals.
Such models have been widely used in previous work but no sparse
solutions have been developed. The paper presents the first sparse
solution for such problems, showing how the sparse approximation can
be obtained by maximizing a variational lower bound on the marginal
likelihood, generalizing ideas from single-task Gaussian processes to
handle the mixed-effect model as well as grouping.  Experiments using
artificial and real data validate the approach showing that it can
recover the performance of inference with the full sample, that it
outperforms baseline methods, and that it outperforms state of the art
sparse solutions for other multi-task GP formulations.}


\timeTitlePeople{15:15--15:40}{Transfer Spectral Clustering
}{Wenhao Jiang and Fu-lai Chung}

\abst{Transferring knowledge from auxiliary datasets has been proved useful in machine learning tasks. Its
adoption in clustering however is still limited. Despite of its superior performance, spectral
clustering has not yet been incorporated with knowledge transfer or transfer learning. In this
paper, we make such an attempt and propose a new algorithm called transfer spectral clustering
(TSC). It involves not only  the data manifold information of the clustering task but also the
feature manifold information shared between related clustering tasks. Furthermore, it makes use of
co-clustering  to achieve and control the knowledge transfer among tasks. As demonstrated by the
experimental results, TSC can greatly improve the clustering performance by effectively using
auxiliary unlabeled data when compared with other state-of-the-art clustering algorithms.}


\sessionend

