Search Results for author:
Found 10 papers, 2 papers with code
Fair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparency
In this paper, we propose a fair predictive model based on multivariate adaptive regression splines(MARS) that incorporates fairness measures in the learning process.
Hyperparameter Adaptive Search for Surrogate Optimization: A Self-Adjusting Approach
Experimental results demonstrate the effectiveness of HASSO in enhancing the performance of various SO algorithms across different global optimization test problems.
Fairness and Bias in Truth Discovery Algorithms: An Experimental Analysis
In this paper, we conduct a systematic study of the bias and fairness of TD algorithms.
FairPilot: An Explorative System for Hyperparameter Tuning through the Lens of Fairness
Despite the potential benefits of machine learning (ML) in high-risk decision-making domains, the deployment of ML is not accessible to practitioners, and there is a risk of discrimination.
Explainable Predictive Modeling for Limited Spectral Data
Interpretation of the prediction outcome is beneficial for the domain experts as it ensures the transparency and faithfulness of the ML models to the domain knowledge.
Finding Representative Group Fairness Metrics Using Correlation Estimations
Using the estimated correlations, we then find a subset of representative metrics.
Auditing the Imputation Effect on Fairness of Predictive Analytics in Higher Education
In this paper, we set out to first assess the disparities in predictive modeling outcomes for college-student success, then investigate the impact of imputation techniques on the model performance and fairness using a commonly used set of metrics.
Fair Active Learning
Collecting accurate labeled data in societal applications is challenging and costly.
Fair Active Learning
Machine learning (ML) is increasingly being used in high-stakes applications impacting society.
High-dimensional Black-box Optimization Under Uncertainty
As a resolution, we present a new surrogate optimization approach by addressing two gaps in prior research -- unimportant input variables and inefficient treatment of uncertainty associated with the black-box output.
