Nazanin Nezami

The exploration–exploitation tradeoff poses a significant challenge in surrogate optimization for expensive black-box functions, particularly when dealing with batch evaluation settings. Despite efforts to develop batch sampling techniques, they often fall short of sufficiently prioritizing diversity within the selected batch. In this article, we propose a fundamentally novel approach called Determinantal Point Processes (DPP)-Based Surrogate Optimization (DPPSO), which serves as a consolidated framework. DPPSO introduces a novel discretization scheme and sampling algorithm that fuses exploration and exploitation objectives by harnessing the power of DPP decomposition. An essential aspect of this project is the development of effective scoring functions to incorporate the quality of the sampled points in the decomposition. We provide theoretical guarantees achieving lower bounds on the probability of convergence. We demonstrate the effectiveness of DPPSO across different benchmarks, comparing its performance against various baseline methods.

Optimizing costly black-box functions within a constrained evaluation budget presents significant challenges in many real-world applications. Surrogate Optimization (SO) is a common resolution, yet its proprietary nature introduced by the complexity of surrogate models and the sampling core (e.g., acquisition functions) often leads to a lack of explainability and transparency. While existing literature has primarily concentrated on enhancing convergence to global optima, the practical interpretation of newly proposed strategies remains underexplored, especially in batch evaluation settings. In this paper, we propose \emph{Inclusive} Explainability Metrics for Surrogate Optimization (IEMSO), a comprehensive set of model-agnostic metrics designed to enhance the transparency, trustworthiness, and explainability of the SO approaches. Through these metrics, we provide both intermediate and post-hoc explanations to practitioners before and after performing expensive evaluations to gain trust. We consider four primary categories of metrics, each targeting a specific aspect of the SO process: Sampling Core Metrics, Batch Properties Metrics, Optimization Process Metrics, and Feature Importance. Our experimental evaluations demonstrate the significant potential of the proposed metrics across different benchmarks.

The education sector has been quick to recognize the power of predictive analytics to enhance student success rates. However, there are challenges to widespread adoption, including the lack of accessibility and the potential perpetuation of inequalities. These challenges present in different stages of modeling, including data preparation, model development, and evaluation. These steps can introduce additional bias to the system if not appropriately performed. Substantial incompleteness in responses is a common problem in nationally representative education data at a large scale. This can lead to missing data and can potentially impact the representativeness and accuracy of the results. While many education-related studies address the challenges of missing data, little is known about the impact of handling missing values on the fairness of predictive outcomes in practice. In this paper, we aim to assess the disparities in predictive modeling outcomes for college student success and investigate the impact of imputation techniques on model performance and fairness using various notions. We conduct a prospective evaluation to provide a less biased estimation of future performance and fairness than an evaluation of historical data. Our comprehensive analysis of a real large-scale education dataset reveals key insights on modeling disparities and the impact of imputation techniques on the fairness of the predictive outcome under different testing scenarios. Our results indicate that imputation introduces bias if the testing set follows the historical distribution. However, if the injustice in society is addressed and, consequently, the upcoming batch of observations is equalized, the model would be less biased.

Surrogate Optimization (SO) algorithms have shown promise for optimizing expensive black-box functions. However, their performance is heavily influenced by hyperparameters related to sampling and surrogate fitting, which poses a challenge to their widespread adoption. We investigate the impact of hyperparameters on various SO algorithms and propose a Hyperparameter Adaptive Search for SO (HASSO) approach. HASSO is not a hyperparameter tuning algorithm, but a generic self-adjusting SO algorithm that dynamically tunes its own hyperparameters while concurrently optimizing the primary objective function, without requiring additional evaluations. The aim is to improve the accessibility, effectiveness, and convergence speed of SO algorithms for practitioners. Our approach identifies and modifies the most influential hyperparameters specific to each problem and SO approach, reducing the need for manual tuning without significantly increasing the computational burden. Experimental results demonstrate the effectiveness of HASSO in enhancing the performance of various SO algorithms across different global optimization test problems.

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. To establish trust and acceptance of ML in such domains, democratizing ML tools and fairness consideration are crucial. In this paper, we introduce FairPilot, an interactive system designed to promote the responsible development of ML models by exploring a combination of various models, different hyperparameters, and a wide range of fairness definitions. We emphasize the challenge of selecting the ``best" ML model and demonstrate how FairPilot allows users to select a set of evaluation criteria and then displays the Pareto frontier of models and hyperparameters as an interactive map. FairPilot is the first system to combine these features, offering a unique opportunity for users to responsibly choose their model.

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 pipeline. Given various notions of fairness defined in the literature, investigating the correlation and interaction among metrics is vital for addressing unfairness. Practitioners and data scientists should be able to comprehend each metric and examine their impact on one another given the context, use case, and regulations. Exploring the combinatorial space of different metrics for such examination is burdensome. To alleviate the burden of selecting fairness notions for consideration, we propose a framework that estimates the correlation among fairness notions. Our framework consequently identifies a set of diverse and semantically distinct metrics as representative for a given context. We propose a Monte-Carlo sampling technique for computing the correlations between fairness metrics by indirect and efficient perturbation in the model space. Using the estimated correlations, we then find a subset of representative metrics. The paper proposes a generic method that can be generalized to any arbitrary set of fairness metrics. We showcase the validity of the proposal using comprehensive experiments on real-world benchmark datasets.

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 sampling techniques, there is still a lack of comprehensive investigations of the existing research across the vast scope of BBO problems. We first classify BBO problems into two categories: engineering design and algorithm design optimization and discuss their challenges. We then critically discuss and analyze the adaptive model-based sampling techniques focusing on key acquisition functions. We elaborate on the shortcomings of the variance-based sampling techniques for engineering design problems. Moreover, we provide in-depth insights on the impact of the discretization schemes on the performance of acquisition functions. We emphasize the importance of dynamic discretization for distance-based exploration and introduce EEPA+, an improved variant of a previously proposed Pareto-based sampling technique. Our empirical analyses reveal the effectiveness of variance-based techniques for algorithm design and distance-based methods for engineering design optimization problems.