2 citations · 7 across the 9 of their papers we have counts for
10 papers
Multi-Agent LLMs for Adaptive Acquisition in Bayesian Optimization
Andrea Carbonati, Mohammadsina Almasi, Hadis Anahideh
The exploration-exploitation trade-off is central to sequential decision-making and black-box optimization, yet how Large Language Models (LLMs) reason about and manage this trade-…
Resolving Predictive Multiplicity for the Rashomon Set
Parian Haghighat, Hadis Anahideh, Cynthia Rudin
The existence of multiple, equally accurate models for a given predictive task leads to predictive multiplicity, where a Rashomon set of models achieve similar accuracy but diverge…
Building Trust in Black-box Optimization: A Comprehensive Framework for Explainability
Nazanin Nezami, Hadis Anahideh
Optimizing costly black-box functions within a constrained evaluation budget presents significant challenges in many real-world applications. Surrogate Optimization (SO) is a commo…
Hyperparameter Adaptive Search for Surrogate Optimization: A Self-Adjusting Approach
Nazanin Nezami, Hadis Anahideh
Surrogate Optimization (SO) algorithms have shown promise for optimizing expensive black-box functions. However, their performance is heavily influenced by hyperparameters related…
Fairness and Bias in Truth Discovery Algorithms: An Experimental Analysis
Simone Lazier, Saravanan Thirumuruganathan, Hadis Anahideh
Machine learning (ML) based approaches are increasingly being used in a number of applications with societal impact. Training ML models often require vast amounts of labeled data,…
FairPilot: An Explorative System for Hyperparameter Tuning through the Lens of Fairness
Francesco Di Carlo, Nazanin Nezami, Hadis Anahideh +1
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 discri…
An Empirical Review of Model-based Adaptive Sampling for Global Optimization of Expensive Black-box Functions
Nazanin Nezami, Hadis Anahideh
This paper reviews the state-of-the-art model-based adaptive sampling approaches for single-objective black-box optimization (BBO). While BBO literature includes various promising…
Explainable Predictive Modeling for Limited Spectral Data
Frantishek Akulich, Hadis Anahideh, Manaf Sheyyab +1
Feature selection of high-dimensional labeled data with limited observations is critical for making powerful predictive modeling accessible, scalable, and interpretable for domain…
Auditing the Imputation Effect on Fairness of Predictive Analytics in Higher Education
Hadis Anahideh, Parian Haghighat, Nazanin Nezami +1
Colleges and universities use predictive analytics in a variety of ways to increase student success rates. Despite the potential for predictive analytics, two major barriers exist…
Finding Representative Group Fairness Metrics Using Correlation Estimations
Hadis Anahideh, Nazanin Nezami, Abolfazl Asudeh
It is of critical importance to be aware of the historical discrimination embedded in the data and to consider a fairness measure to reduce bias throughout the predictive modeling…