8,989 research outputs found
Exact and efficient top-K inference for multi-target prediction by querying separable linear relational models
Many complex multi-target prediction problems that concern large target
spaces are characterised by a need for efficient prediction strategies that
avoid the computation of predictions for all targets explicitly. Examples of
such problems emerge in several subfields of machine learning, such as
collaborative filtering, multi-label classification, dyadic prediction and
biological network inference. In this article we analyse efficient and exact
algorithms for computing the top- predictions in the above problem settings,
using a general class of models that we refer to as separable linear relational
models. We show how to use those inference algorithms, which are modifications
of well-known information retrieval methods, in a variety of machine learning
settings. Furthermore, we study the possibility of scoring items incompletely,
while still retaining an exact top-K retrieval. Experimental results in several
application domains reveal that the so-called threshold algorithm is very
scalable, performing often many orders of magnitude more efficiently than the
naive approach
TDL--- A Type Description Language for Constraint-Based Grammars
This paper presents \tdl, a typed feature-based representation language and
inference system. Type definitions in \tdl\ consist of type and feature
constraints over the boolean connectives. \tdl\ supports open- and closed-world
reasoning over types and allows for partitions and incompatible types. Working
with partially as well as with fully expanded types is possible. Efficient
reasoning in \tdl\ is accomplished through specialized modules.Comment: Will Appear in Proc. COLING-9
Learning Models over Relational Data using Sparse Tensors and Functional Dependencies
Integrated solutions for analytics over relational databases are of great
practical importance as they avoid the costly repeated loop data scientists
have to deal with on a daily basis: select features from data residing in
relational databases using feature extraction queries involving joins,
projections, and aggregations; export the training dataset defined by such
queries; convert this dataset into the format of an external learning tool; and
train the desired model using this tool. These integrated solutions are also a
fertile ground of theoretically fundamental and challenging problems at the
intersection of relational and statistical data models.
This article introduces a unified framework for training and evaluating a
class of statistical learning models over relational databases. This class
includes ridge linear regression, polynomial regression, factorization
machines, and principal component analysis. We show that, by synergizing key
tools from database theory such as schema information, query structure,
functional dependencies, recent advances in query evaluation algorithms, and
from linear algebra such as tensor and matrix operations, one can formulate
relational analytics problems and design efficient (query and data)
structure-aware algorithms to solve them.
This theoretical development informed the design and implementation of the
AC/DC system for structure-aware learning. We benchmark the performance of
AC/DC against R, MADlib, libFM, and TensorFlow. For typical retail forecasting
and advertisement planning applications, AC/DC can learn polynomial regression
models and factorization machines with at least the same accuracy as its
competitors and up to three orders of magnitude faster than its competitors
whenever they do not run out of memory, exceed 24-hour timeout, or encounter
internal design limitations.Comment: 61 pages, 9 figures, 2 table
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