18 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
Discriminative structural approaches for enzyme active-site prediction
<p>Abstract</p> <p>Background</p> <p>Predicting enzyme active-sites in proteins is an important issue not only for protein sciences but also for a variety of practical applications such as drug design. Because enzyme reaction mechanisms are based on the local structures of enzyme active-sites, various template-based methods that compare local structures in proteins have been developed to date. In comparing such local sites, a simple measurement, RMSD, has been used so far.</p> <p>Results</p> <p>This paper introduces new machine learning algorithms that refine the similarity/deviation for comparison of local structures. The similarity/deviation is applied to two types of applications, single template analysis and multiple template analysis. In the single template analysis, a single template is used as a query to search proteins for active sites, whereas a protein structure is examined as a query to discover the possible active-sites using a set of templates in the multiple template analysis.</p> <p>Conclusions</p> <p>This paper experimentally illustrates that the machine learning algorithms effectively improve the similarity/deviation measurements for both the analyses.</p
Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI
Motor-imagery-based brain-computer interfaces (BCIs) commonly use
the common spatial pattern filter (CSP) as preprocessing step before feature
extraction and classification. The CSP method is a supervised algorithm
and therefore needs subject-specific training data for calibration,
which is very time consuming to collect. In order to reduce the amount
of calibration data that is needed for a new subject, one can apply multitask (from now on called multisubject) machine learning techniques to the preprocessing phase. Here, the
goal of multisubject learning is to learn a spatial filter for a new subject
based on its own data and that of other subjects. This paper outlines
the details of the multitask CSP algorithm and shows results on two data
sets. In certain subjects a clear improvement can be seen, especially when
the number of training trials is relatively low
Conic Optimization Theory: Convexification Techniques and Numerical Algorithms
Optimization is at the core of control theory and appears in several areas of
this field, such as optimal control, distributed control, system
identification, robust control, state estimation, model predictive control and
dynamic programming. The recent advances in various topics of modern
optimization have also been revamping the area of machine learning. Motivated
by the crucial role of optimization theory in the design, analysis, control and
operation of real-world systems, this tutorial paper offers a detailed overview
of some major advances in this area, namely conic optimization and its emerging
applications. First, we discuss the importance of conic optimization in
different areas. Then, we explain seminal results on the design of hierarchies
of convex relaxations for a wide range of nonconvex problems. Finally, we study
different numerical algorithms for large-scale conic optimization problems.Comment: 18 page
Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection
Customer lifetime value (LTV) prediction is essential for mobile game
publishers trying to optimize the advertising investment for each user
acquisition based on the estimated worth. In mobile games, deploying
microtransactions is a simple yet effective monetization strategy, which
attracts a tiny group of game whales who splurge on in-game purchases. The
presence of such game whales may impede the practicality of existing LTV
prediction models, since game whales' purchase behaviours always exhibit varied
distribution from general users. Consequently, identifying game whales can open
up new opportunities to improve the accuracy of LTV prediction models. However,
little attention has been paid to applying game whale detection in LTV
prediction, and existing works are mainly specialized for the long-term LTV
prediction with the assumption that the high-quality user features are
available, which is not applicable in the UA stage. In this paper, we propose
ExpLTV, a novel multi-task framework to perform LTV prediction and game whale
detection in a unified way. In ExpLTV, we first innovatively design a deep
neural network-based game whale detector that can not only infer the intrinsic
order in accordance with monetary value, but also precisely identify high
spenders (i.e., game whales) and low spenders. Then, by treating the game whale
detector as a gating network to decide the different mixture patterns of LTV
experts assembling, we can thoroughly leverage the shared information and
scenario-specific information (i.e., game whales modelling and low spenders
modelling). Finally, instead of separately designing a purchase rate estimator
for two tasks, we design a shared estimator that can preserve the inner task
relationships. The superiority of ExpLTV is further validated via extensive
experiments on three industrial datasets