5,951 research outputs found
Client-server multi-task learning from distributed datasets
A client-server architecture to simultaneously solve multiple learning tasks
from distributed datasets is described. In such architecture, each client is
associated with an individual learning task and the associated dataset of
examples. The goal of the architecture is to perform information fusion from
multiple datasets while preserving privacy of individual data. The role of the
server is to collect data in real-time from the clients and codify the
information in a common database. The information coded in this database can be
used by all the clients to solve their individual learning task, so that each
client can exploit the informative content of all the datasets without actually
having access to private data of others. The proposed algorithmic framework,
based on regularization theory and kernel methods, uses a suitable class of
mixed effect kernels. The new method is illustrated through a simulated music
recommendation system
Uncovering Causality from Multivariate Hawkes Integrated Cumulants
We design a new nonparametric method that allows one to estimate the matrix
of integrated kernels of a multivariate Hawkes process. This matrix not only
encodes the mutual influences of each nodes of the process, but also
disentangles the causality relationships between them. Our approach is the
first that leads to an estimation of this matrix without any parametric
modeling and estimation of the kernels themselves. A consequence is that it can
give an estimation of causality relationships between nodes (or users), based
on their activity timestamps (on a social network for instance), without
knowing or estimating the shape of the activities lifetime. For that purpose,
we introduce a moment matching method that fits the third-order integrated
cumulants of the process. We show on numerical experiments that our approach is
indeed very robust to the shape of the kernels, and gives appealing results on
the MemeTracker database
The ABACOC Algorithm: a Novel Approach for Nonparametric Classification of Data Streams
Stream mining poses unique challenges to machine learning: predictive models
are required to be scalable, incrementally trainable, must remain bounded in
size (even when the data stream is arbitrarily long), and be nonparametric in
order to achieve high accuracy even in complex and dynamic environments.
Moreover, the learning system must be parameterless ---traditional tuning
methods are problematic in streaming settings--- and avoid requiring prior
knowledge of the number of distinct class labels occurring in the stream. In
this paper, we introduce a new algorithmic approach for nonparametric learning
in data streams. Our approach addresses all above mentioned challenges by
learning a model that covers the input space using simple local classifiers.
The distribution of these classifiers dynamically adapts to the local (unknown)
complexity of the classification problem, thus achieving a good balance between
model complexity and predictive accuracy. We design four variants of our
approach of increasing adaptivity. By means of an extensive empirical
evaluation against standard nonparametric baselines, we show state-of-the-art
results in terms of accuracy versus model size. For the variant that imposes a
strict bound on the model size, we show better performance against all other
methods measured at the same model size value. Our empirical analysis is
complemented by a theoretical performance guarantee which does not rely on any
stochastic assumption on the source generating the stream
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