arXiv subjects

Nurşen Aydın

Publications and source records attributed to Nurşen Aydın.

4 recordsLinked to original sources

arXiv2026

Guided Data Generation for Understanding Model Behavior

We propose a method for generating distributions over the input space as an inspection tool for understanding trained models. Our framework poses questions of the form ``which inputs would make a trained model exhibit a specified behavior?'' and encodes each question through a guidance function. The generated data provide insights into how the models behave. To showcase our framework, we pose queries such as generating distributions of data where a specified label would be predicted by the model, where two distinct models would disagree, where the output is sensitive to parameter perturbations, and where predictions would be risky. Our method can be applied with a variety of classification and regression tasks and on a range of model types.

cs.LG↗

arXiv2026

Dynamic Markdown Strategies for Perishable Products: Waste Reduction versus Profitability

Freshness is a key attribute of perishable goods, particularly in the food sector, and strongly influences consumer purchasing behavior. Retailers often prioritize high freshness to remain competitive, yet maintaining this standard under demand uncertainty increases the likelihood of unsold inventory and waste. Retailers use markdowns to mitigate this risk, but the timing and depth of discounts must be carefully coordinated to avoid lost revenue or excess waste. This paper studies how retailers can jointly optimize order and markdown pricing decisions to balance profitability and waste reduction for perishable products within an integrated decision-making framework. The joint ordering-markdown pricing problem is formulated as a stochastic dynamic programming model that accounts for the demand cannibalization between regular price and markdown sales. The conflicting objectives of profit maximization and waste reduction are captured through a weighted objective function that reflects the retailer's trade-off preferences. To manage the complexity of the model, we propose an efficient solution approach and conduct numerical experiments to assess the performance of alternative markdown policies. Our results show that implementing multiple early price reductions with successive price reductions can simultaneously increase revenue and reduce waste. Our findings generate several key managerial insights: (i) firms can tailor their markdown policies according to their strategic priorities of profit maximization, waste reduction, or a balanced combination of both, (ii) early and well-timed markdowns based on available inventory levels allow firms to simultaneously attract quality-seeking customers and price-sensitive segments, (iii) implementing a second or multiple markdowns can further reduce waste while also increasing revenue.

math.OC↗

arXiv2026

Generating Input Distributions for Explaining Portfolio Optimization Pipelines

We propose a predict-optimize-explain framework that uses gradient-based sample generation to interpret various portfolio models by identifying macroeconomic conditions that induce specified portfolio outcomes. Unlike traditional feature-importance methods, this approach directly probes decision pipelines (predictive models coupled with portfolio optimization) by constructing economically meaningful what-if questions. We focus on four such questions: under what macroeconomic conditions a predict-then-optimize pipeline closes or reverses its return gap with a predict-and-optimize pipeline; what conditions lead a pipeline to diversify rather than concentrate its allocation; when a pipeline trained on calm markets overtakes one trained through crises; and what conditions would let a pipeline match a benchmark return. These examples illustrate how our framework uncovers key behavioral differences between various decision pipelines. Beyond these cases, the proposed framework is flexible and can support a wide range of probing questions tailored to specific portfolio objectives. Our findings highlight the value of integrating prediction, optimization, and explanation to produce more robust and transparent portfolio strategies.

math.OC↗

arXiv2026

Explainable Optimization: A Call for Interdisciplinary Action

Operations research and management science models support decisions that affect patients, workers, citizens, and public institutions. Decision-makers, such as clinicians approving surgical schedules, planners allocating disaster relief resources, or managers designing workforce rotations, increasingly require clear and actionable justifications that bridge the gap between mathematical optimization outputs and the intuitive reasoning stakeholders need to trust, contest, and implement recommended decisions. Yet the field has traditionally evaluated optimization models through computational criteria such as feasibility, optimality, scalability, and solution time, while treating explanation as a secondary concern. Mathematical transparency, provided through access to objectives, constraints, shadow prices, or sensitivity reports, does not automatically offer the forms of justification that stakeholders need to understand, trust, contest, or implement optimization-based decisions. This paper calls for the development of explainable optimization (XOpt) as a distinct interdisciplinary area that moves beyond algorithmic efficiency and incorporates behavioral, cognitive, and pragmatic perspectives to address this explanatory deficit.

math.OC↗