16,502 research outputs found
A bagging SVM to learn from positive and unlabeled examples
We consider the problem of learning a binary classifier from a training set
of positive and unlabeled examples, both in the inductive and in the
transductive setting. This problem, often referred to as \emph{PU learning},
differs from the standard supervised classification problem by the lack of
negative examples in the training set. It corresponds to an ubiquitous
situation in many applications such as information retrieval or gene ranking,
when we have identified a set of data of interest sharing a particular
property, and we wish to automatically retrieve additional data sharing the
same property among a large and easily available pool of unlabeled data. We
propose a conceptually simple method, akin to bagging, to approach both
inductive and transductive PU learning problems, by converting them into series
of supervised binary classification problems discriminating the known positive
examples from random subsamples of the unlabeled set. We empirically
demonstrate the relevance of the method on simulated and real data, where it
performs at least as well as existing methods while being faster
Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking
Deep neural networks have become a primary tool for solving problems in many
fields. They are also used for addressing information retrieval problems and
show strong performance in several tasks. Training these models requires large,
representative datasets and for most IR tasks, such data contains sensitive
information from users. Privacy and confidentiality concerns prevent many data
owners from sharing the data, thus today the research community can only
benefit from research on large-scale datasets in a limited manner. In this
paper, we discuss privacy preserving mimic learning, i.e., using predictions
from a privacy preserving trained model instead of labels from the original
sensitive training data as a supervision signal. We present the results of
preliminary experiments in which we apply the idea of mimic learning and
privacy preserving mimic learning for the task of document re-ranking as one of
the core IR tasks. This research is a step toward laying the ground for
enabling researchers from data-rich environments to share knowledge learned
from actual users' data, which should facilitate research collaborations.Comment: SIGIR 2017 Workshop on Neural Information Retrieval
(Neu-IR'17)}{}{August 7--11, 2017, Shinjuku, Tokyo, Japa
Weakly supervised segment annotation via expectation kernel density estimation
Since the labelling for the positive images/videos is ambiguous in weakly
supervised segment annotation, negative mining based methods that only use the
intra-class information emerge. In these methods, negative instances are
utilized to penalize unknown instances to rank their likelihood of being an
object, which can be considered as a voting in terms of similarity. However,
these methods 1) ignore the information contained in positive bags, 2) only
rank the likelihood but cannot generate an explicit decision function. In this
paper, we propose a voting scheme involving not only the definite negative
instances but also the ambiguous positive instances to make use of the extra
useful information in the weakly labelled positive bags. In the scheme, each
instance votes for its label with a magnitude arising from the similarity, and
the ambiguous positive instances are assigned soft labels that are iteratively
updated during the voting. It overcomes the limitations of voting using only
the negative bags. We also propose an expectation kernel density estimation
(eKDE) algorithm to gain further insight into the voting mechanism.
Experimental results demonstrate the superiority of our scheme beyond the
baselines.Comment: 9 pages, 2 figure
RankMerging: A supervised learning-to-rank framework to predict links in large social network
Uncovering unknown or missing links in social networks is a difficult task
because of their sparsity and because links may represent different types of
relationships, characterized by different structural patterns. In this paper,
we define a simple yet efficient supervised learning-to-rank framework, called
RankMerging, which aims at combining information provided by various
unsupervised rankings. We illustrate our method on three different kinds of
social networks and show that it substantially improves the performances of
unsupervised metrics of ranking. We also compare it to other combination
strategies based on standard methods. Finally, we explore various aspects of
RankMerging, such as feature selection and parameter estimation and discuss its
area of relevance: the prediction of an adjustable number of links on large
networks.Comment: 43 pages, published in Machine Learning Journa
Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks
How can we estimate the importance of nodes in a knowledge graph (KG)? A KG
is a multi-relational graph that has proven valuable for many tasks including
question answering and semantic search. In this paper, we present GENI, a
method for tackling the problem of estimating node importance in KGs, which
enables several downstream applications such as item recommendation and
resource allocation. While a number of approaches have been developed to
address this problem for general graphs, they do not fully utilize information
available in KGs, or lack flexibility needed to model complex relationship
between entities and their importance. To address these limitations, we explore
supervised machine learning algorithms. In particular, building upon recent
advancement of graph neural networks (GNNs), we develop GENI, a GNN-based
method designed to deal with distinctive challenges involved with predicting
node importance in KGs. Our method performs an aggregation of importance scores
instead of aggregating node embeddings via predicate-aware attention mechanism
and flexible centrality adjustment. In our evaluation of GENI and existing
methods on predicting node importance in real-world KGs with different
characteristics, GENI achieves 5-17% higher NDCG@100 than the state of the art.Comment: KDD 2019 Research Track. 11 pages. Changelog: Type 3 font removed,
and minor updates made in the Appendix (v2
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