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Found 20 papers, 5 papers with code
Multi-objective evolutionary GAN for tabular data synthesis
However, there are unique challenges in tabular data compared to images, eg tabular data may contain both continuous and discrete variables and conditional sampling, and, critically, the data should possess high utility and low disclosure risk (the risk of re-identifying a population unit or learning something new about them), providing an opportunity for multi-objective (MO) optimization.
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach
In particular, by adapting permutation feature importance, partial dependence plots, and individual conditional expectation plots, we demonstrate that novel insights into model behaviour may be obtained and that these methods can be used to measure the impact of features on both the entropy of the predictive distribution and the log-likelihood of the ground truth labels under that distribution.
Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite
Specifically, we want to understand the performance difference between BBOB and SBOX-COST as a function of two initialization methods and six constraint-handling strategies all tested with modular CMA-ES.
Applying Ising Machines to Multi-objective QUBOs
In this work, we extend the adaptive method based on averages in two ways: (i)~we extend the adaptive method of deriving scalarisation weights for problems with two or more objectives, and (ii)~we use an alternative measure of distance to improve performance.
A Study of Scalarisation Techniques for Multi-Objective QUBO Solving
These solvers are then applied to QUBO formulations of combinatorial optimisation problems.
Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata
The paper presents a framework to measure the utility and disclosure risk of synthetic data by comparing it to samples of the original data of varying sample fractions, thereby identifying the sample fraction which has equivalent utility and risk to the synthetic data.
Cooperative Multi-Agent Search on Endogenously-Changing Fitness Landscapes
We use a multi-agent system to model how agents (representing firms) may collaborate and adapt in a business 'landscape' where some, more influential, firms are given the power to shape the landscape of other firms.
Combining Topic Modeling with Grounded Theory: Case Studies of Project Collaboration
This paper proposes an Artificial Intelligence (AI) Grounded Theory for management studies.
Efficient Approximation of Expected Hypervolume Improvement using Gauss-Hermite Quadrature
Using the predictive densities, we can compute the expected hypervolume improvement (EHVI) due to a solution.
Multi-objective QUBO Solver: Bi-objective Quadratic Assignment
We present the first attempt to extend the algorithm supporting a commercial QUBO solver as a multi-objective solver that is not based on scalarisation.
A Data-Driven Framework for Identifying Investment Opportunities in Private Equity
The core activity of a Private Equity (PE) firm is to invest into companies in order to provide the investors with profit, usually within 4-7 years.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Are Evolutionary Algorithms Safe Optimizers?
We consider a type of constrained optimization problem, where the violation of a constraint leads to an irrevocable loss, such as breakage of a valuable experimental resource/platform or loss of human life.
SonOpt: Sonifying Bi-objective Population-Based Optimization Algorithms
The benefits of data sonification have been shown for various non-optimization related monitoring tasks.
Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study
Generative Adversarial Networks (GANs) are gaining increasing attention as a means for synthesising data.
Towards a fairer reimbursement system for burn patients using cost-sensitive classification
To improve homogeneity in resource usage and severity, we propose a data-driven model and the inclusion of patient-level costing.
What if we Increase the Number of Objectives? Theoretical and Empirical Implications for Many-objective Optimization
The presence of many objectives typically introduces a number of challenges that affect the choice/design of optimization algorithms.
Managing Manufacturing and Delivery of Personalised Medicine: Current and Future Models
To accelerate technology adoption in this domain, we characterize pertinent practical challenges in a PM supply chain and then capture them in a holistic mathematical model ready for optimisation.
Heterogeneous Objectives: State-of-the-Art and Future Research
Multiobjective optimization problems with heterogeneous objectives are defined as those that possess significantly different types of objective function components (not just incommensurable in units or scale).
HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis
Here, we argue that synthetic datasets must continue to play an important role in the evaluation of clustering algorithms, but that this necessitates constructing benchmarks that appropriately cover the diverse set of properties that impact clustering algorithm performance.
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art
Safe learning and optimization deals with learning and optimization problems that avoid, as much as possible, the evaluation of non-safe input points, which are solutions, policies, or strategies that cause an irrecoverable loss (e. g., breakage of a machine or equipment, or life threat).
