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Local search: A guide for the information retrieval practitioner
There are a number of combinatorial optimisation problems in information retrieval in which the use of local search methods are worthwhile. The purpose of this paper is to show how local search can be used to solve some well known tasks in information retrieval (IR), how previous research in the field is piecemeal, bereft of a structure and methodologically flawed, and to suggest more rigorous ways of applying local search methods to solve IR problems. We provide a query based taxonomy for analysing the use of local search in IR tasks and an overview of issues such as fitness functions, statistical significance and test collections when conducting experiments on combinatorial optimisation problems. The paper gives a guide on the pitfalls and problems for IR practitioners who wish to use local search to solve their research issues, and gives practical advice on the use of such methods. The query based taxonomy is a novel structure which can be used by the IR practitioner in order to examine the use of local search in IR
Sampling-Based Query Re-Optimization
Despite of decades of work, query optimizers still make mistakes on
"difficult" queries because of bad cardinality estimates, often due to the
interaction of multiple predicates and correlations in the data. In this paper,
we propose a low-cost post-processing step that can take a plan produced by the
optimizer, detect when it is likely to have made such a mistake, and take steps
to fix it. Specifically, our solution is a sampling-based iterative procedure
that requires almost no changes to the original query optimizer or query
evaluation mechanism of the system. We show that this indeed imposes low
overhead and catches cases where three widely used optimizers (PostgreSQL and
two commercial systems) make large errors.Comment: This is the extended version of a paper with the same title and
authors that appears in the Proceedings of the ACM SIGMOD International
Conference on Management of Data (SIGMOD 2016
Enhancing High-dimensional Bayesian Optimization by Optimizing the Acquisition Function Maximizer Initialization
Bayesian optimization (BO) is widely used to optimize black-box functions. It
works by first building a surrogate for the objective and quantifying the
uncertainty in that surrogate. It then decides where to sample by maximizing an
acquisition function defined by the surrogate model. Prior approaches typically
use randomly generated raw samples to initialize the acquisition function
maximizer. However, this strategy is ill-suited for high-dimensional BO. Given
the large regions of high posterior uncertainty in high dimensions, a randomly
initialized acquisition function maximizer is likely to focus on areas with
high posterior uncertainty, leading to overly exploring areas that offer little
gain. This paper provides the first comprehensive empirical study to reveal the
importance of the initialization phase of acquisition function maximization. It
proposes a better initialization approach by employing multiple heuristic
optimizers to leverage the knowledge of already evaluated samples to generate
initial points to be explored by an acquisition function maximizer. We evaluate
our approach on widely used synthetic test functions and real-world
applications. Experimental results show that our techniques, while simple, can
significantly enhance the standard BO and outperforms state-of-the-art
high-dimensional BO techniques by a large margin in most test cases
MOODY: An ontology-driven framework for standardizing multi-objective evolutionary algorithms
The application of semantic technologies, particularly ontologies, in the realm of multi-objective evolutionary algorithms is overlook despite their effectiveness in knowledge representation. In this paper, we introduce MOODY, an ontology specifically tailored to formalize these kinds of algorithms, encompassing their respective parameters, and multi-objective optimization problems based on a characterization of their search space landscapes. MOODY is designed to be particularly applicable in automatic algorithm configuration, which involves the search of the parameters of an optimization algorithm to optimize its performance. In this context, we observe a notable absence of standardized components, parameters, and related considerations, such as problem characteristics and algorithm configurations. This lack of standardization introduces difficulties in the selection of valid component combinations and in the re-use of algorithmic configurations between different algorithm implementations. MOODY offers a means to infuse semantic annotations into the configurations found by automatic tools, enabling efficient querying of the results and seamless integration across diverse sources through their incorporation into a knowledge graph. We validate our proposal by presenting four case studies.Funding for open Access charge: Universidad de Málaga / CBUA.
This work has been partially funded by the Spanish Ministry of Science and Innovation via Grant PID2020-112540RB-C41 (AEI/FEDER, UE) and the Andalusian PAIDI program with grant P18-RT-2799. JosĂ© F. Aldana-MartĂn is supported by Grant PRE2021-098594 (Spanish Ministry of Science, Innovation and Universities)
OPT-GAN: Black-Box Global Optimization via Generative Adversarial Nets
Black-box optimization (BBO) algorithms are concerned with finding the best
solutions for problems with missing analytical details. Most classical methods
for such problems are based on strong and fixed a priori assumptions, such as
Gaussianity. However, the complex real-world problems, especially when the
global optimum is desired, could be very far from the a priori assumptions
because of their diversities, causing unexpected obstacles to these methods. In
this study, we propose a generative adversarial net-based broad-spectrum global
optimizer (OPT-GAN) which estimates the distribution of optimum gradually, with
strategies to balance exploration-exploitation trade-off. It has potential to
better adapt to the regularity and structure of diversified landscapes than
other methods with fixed prior, e.g. Gaussian assumption or separability.
Experiments conducted on BBO benchmarking problems and several other benchmarks
with diversified landscapes exhibit that OPT-GAN outperforms other traditional
and neural net-based BBO algorithms.Comment: M. Lu and S. Ning contribute equally. Submitted to IEEE transactions
on Neural Networks and Learning System
Parallel Multi-Objective Hyperparameter Optimization with Uniform Normalization and Bounded Objectives
Machine learning (ML) methods offer a wide range of configurable
hyperparameters that have a significant influence on their performance. While
accuracy is a commonly used performance objective, in many settings, it is not
sufficient. Optimizing the ML models with respect to multiple objectives such
as accuracy, confidence, fairness, calibration, privacy, latency, and memory
consumption is becoming crucial. To that end, hyperparameter optimization, the
approach to systematically optimize the hyperparameters, which is already
challenging for a single objective, is even more challenging for multiple
objectives. In addition, the differences in objective scales, the failures, and
the presence of outlier values in objectives make the problem even harder. We
propose a multi-objective Bayesian optimization (MoBO) algorithm that addresses
these problems through uniform objective normalization and randomized weights
in scalarization. We increase the efficiency of our approach by imposing
constraints on the objective to avoid exploring unnecessary configurations
(e.g., insufficient accuracy). Finally, we leverage an approach to parallelize
the MoBO which results in a 5x speed-up when using 16x more workers.Comment: Preprint with appendice
B2Opt: Learning to Optimize Black-box Optimization with Little Budget
The core challenge of high-dimensional and expensive black-box optimization
(BBO) is how to obtain better performance faster with little function
evaluation cost. The essence of the problem is how to design an efficient
optimization strategy tailored to the target task. This paper designs a
powerful optimization framework to automatically learn the optimization
strategies from the target or cheap surrogate task without human intervention.
However, current methods are weak for this due to poor representation of
optimization strategy. To achieve this, 1) drawing on the mechanism of genetic
algorithm, we propose a deep neural network framework called B2Opt, which has a
stronger representation of optimization strategies based on survival of the
fittest; 2) B2Opt can utilize the cheap surrogate functions of the target task
to guide the design of the efficient optimization strategies. Compared to the
state-of-the-art BBO baselines, B2Opt can achieve multiple orders of magnitude
performance improvement with less function evaluation cost. We validate our
proposal on high-dimensional synthetic functions and two real-world
applications. We also find that deep B2Opt performs better than shallow ones
Nature Inspired Evolutionary Swarm Optimizers for Biomedical Image and Signal Processing -- A Systematic Review
The challenge of finding a global optimum in a solution search space with
limited resources and higher accuracy has given rise to several optimization
algorithms. Generally, the gradient-based optimizers converge to the global
solution very accurately, but they often require a large number of iterations
to find the solution. Researchers took inspiration from different natural
phenomena and behaviours of many living organisms to develop algorithms that
can solve optimization problems much quicker with high accuracy. These
algorithms are called nature-inspired meta-heuristic optimization algorithms.
These can be used for denoising signals, updating weights in a deep neural
network, and many other cases. In the state-of-the-art, there are no systematic
reviews available that have discussed the applications of nature-inspired
algorithms on biomedical signal processing. The paper solves that gap by
discussing the applications of such algorithms in biomedical signal processing
and also provides an updated survey of the application of these algorithms in
biomedical image processing. The paper reviews 28 latest peer-reviewed relevant
articles and 26 nature-inspired algorithms and segregates them into thoroughly
explored, lesser explored and unexplored categories intending to help readers
understand the reliability and exploration stage of each of these algorithms
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