544,685 research outputs found
Distributed Multi-Task Relationship Learning
Multi-task learning aims to learn multiple tasks jointly by exploiting their
relatedness to improve the generalization performance for each task.
Traditionally, to perform multi-task learning, one needs to centralize data
from all the tasks to a single machine. However, in many real-world
applications, data of different tasks may be geo-distributed over different
local machines. Due to heavy communication caused by transmitting the data and
the issue of data privacy and security, it is impossible to send data of
different task to a master machine to perform multi-task learning. Therefore,
in this paper, we propose a distributed multi-task learning framework that
simultaneously learns predictive models for each task as well as task
relationships between tasks alternatingly in the parameter server paradigm. In
our framework, we first offer a general dual form for a family of regularized
multi-task relationship learning methods. Subsequently, we propose a
communication-efficient primal-dual distributed optimization algorithm to solve
the dual problem by carefully designing local subproblems to make the dual
problem decomposable. Moreover, we provide a theoretical convergence analysis
for the proposed algorithm, which is specific for distributed multi-task
relationship learning. We conduct extensive experiments on both synthetic and
real-world datasets to evaluate our proposed framework in terms of
effectiveness and convergence.Comment: To appear in KDD 201
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
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning
A lot of the recent success in natural language processing (NLP) has been
driven by distributed vector representations of words trained on large amounts
of text in an unsupervised manner. These representations are typically used as
general purpose features for words across a range of NLP problems. However,
extending this success to learning representations of sequences of words, such
as sentences, remains an open problem. Recent work has explored unsupervised as
well as supervised learning techniques with different training objectives to
learn general purpose fixed-length sentence representations. In this work, we
present a simple, effective multi-task learning framework for sentence
representations that combines the inductive biases of diverse training
objectives in a single model. We train this model on several data sources with
multiple training objectives on over 100 million sentences. Extensive
experiments demonstrate that sharing a single recurrent sentence encoder across
weakly related tasks leads to consistent improvements over previous methods. We
present substantial improvements in the context of transfer learning and
low-resource settings using our learned general-purpose representations.Comment: Accepted at ICLR 201
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