Hansheng Jiang (she/her)

Journal Publications

1. Designing Surprise Bags for Surplus Foods

   Fan Zhou, Hansheng Jiang, Andrea Li^‡ (Too Good To Go), Joline Uichanco

   Manufacturing & Service Operations Management, 2026

   Abstract: Designing Surprise Bags for Surplus Foods

   Problem definition: Surplus-food marketplaces, such as Too Good To Go, enable retailers to monetize leftover inventory and reduce food waste by selling discounted “surprise bags.” Participating stores, however, face complex operational challenges. They must jointly set a reservation price and the number of bags made available before daily demand and end-of-day surplus are realized, while customers’ ex-post experiences affect future demand through the platform’s rating system. Moreover, although evenly distributing surplus across bags is intuitive and operationally convenient, it remains unclear whether bag-value differentiation can improve consumer experience and profitability. Methodology/results: We study these questions through an infinite-horizon two-stage stochastic dynamic program. In each period, the store first sets a reservation price per bag and the number of bags available and then, after observing accepted reservations and surplus, allocates surplus food across surprise bags, potentially supplementing from regular inventory. We show that this infinite-dimensional allocation problem can be reduced to a scalar choice of average bag value. Specifically, the optimal allocation is characterized by the upper concave envelope of the consumer utility function and requires at most two bag types. Building on this structural reduction, we develop linear value-function and deterministic fluid approximations that provide explicit performance guarantees and transparent implementation rules. The linear value-function approximation captures most of the dynamic-program profit improvement and produces the lowest average modeled total waste across the main grid, while the deterministic fluid approximation yields transparent steady-state comparative statics. Managerial implications: Price and the reservation cap are not interchangeable: price shifts latent requests and customer net utility, whereas the cap truncates high-request realizations before surplus is known. This division of control provides a basis for platform decision-support tools that balance retailer profit with food recovery.

   SSRNSlidesJournalPDF

2. Multi-Product Dynamic Pricing with Reference Effects Under Logit Demand

   Mengzi Amy Guo, Hansheng Jiang, ZJ Max Shen

   Manufacturing & Service Operations Management, 2025

   Abstract: Multi-Product Dynamic Pricing with Reference Effects Under Logit Demand

   Problem definition: We consider the dynamic pricing problem of multiple products under (asymmetric) reference effects over an infinite horizon. Unlike existing literature, which is mostly focused on the single-product setting, our multiproduct setting takes into account the cross-product effects among substitutes and incorporates the memory-based reference prices into the multinomial logit (MNL) demand model. Even with the single-product logit demand, the structure of the optimal pricing policy is intractable. Therefore, we focus on the long-run patterns of the optimal pricing policy and also discuss the performance of the myopic pricing policy. Methodology/results: We first provide a comprehensive characterization of the myopic pricing policy, including its solution, long-run convergence behavior, and optimality gap. For the optimal pricing policy, we show an intricate connection between its long-run dynamics and types of reference effects. We demonstrate that the presence of any gain-seeking product renders a long-run constant pricing policy suboptimal. Conversely, the constant policy (or optimal steady state) can exist in both loss-neutral and loss-averse scenarios, where we provide a sufficient condition for such existence and give the analytical expression for the optimal steady state. We further show that when pricing perfect substitutes, the true optimal policy under the multiproduct framework is more likely to yield a long-run cyclic pattern than the policy derived from the single-product framework, a phenomenon that aligns well with the periodic discounts in real-world markets. Managerial implications: This discrepancy in the long-run behaviors between multi- and single-product-based policies highlights the importance of employing the multiproduct framework and addressing the cross-product effects, as sticking to the single-product framework while managing multiple substitutes can misrepresent long-run dynamics and result in suboptimality. In the multiproduct domain, our model suggests that retailers are more likely to benefit from appropriate price variations than maintaining a constant pricing policy.

   SSRNJournalPDF

3. Intertemporal Pricing via Nonparametric Estimation: Integrating Reference Effects and Consumer Heterogeneity

   Hansheng Jiang, Junyu Cao, ZJ Max Shen

   Manufacturing & Service Operations Management, 2024

   • MSOM Data-Driven Research Challenge Competition, Finalist
   Abstract: Intertemporal Pricing via Nonparametric Estimation: Integrating Reference Effects and Consumer Heterogeneity

   We consider intertemporal pricing in the presence of reference effects and consumer heterogeneity. Our research question encompasses how to estimate heterogeneous consumer reference effects from data and how to efficiently compute the optimal pricing policy. Understanding reference effects is essential for designing pricing policies in modern retailing. Our work contributes to this area by incorporating consumer heterogeneity under arbitrary distributions. We propose a mixed logit demand model that allows arbitrary joint distributions of valuations, responsiveness to prices, and responsiveness to reference prices among consumers. We use a nonparametric estimation method to learn consumer heterogeneity from transaction data. Further, we formulate the pricing optimization as an infinite horizon dynamic programming problem and solve it by applying a modified policy iteration algorithm. Moreover, we investigate the structure of optimal pricing policies and prove the sub-optimality of constant pricing policies even when all consumers are loss-averse according to the classical definition. Our numerical studies show that our estimation and optimization framework improves the expected revenue of retailers via accounting for heterogeneity. We validate our model using real data from JD.com, a large E-commerce retailer, and find empirical evidence of consumer heterogeneity. In practice, ignoring consumer heterogeneity may lead to a significant loss of revenue. Furthermore, heterogeneous reference effect offers a strong motive for promotions and price fluctuations.

   SSRNSlidesCodeDataJournalPDF

Working Papers

1. Conformal Robustness in Prediction-Driven Decision-Making

   Lingjie Zhao^†, Hansheng Jiang, Wei Qi

   • Extended abstract accepted to Calibration for Modern AI Workshop, AISTATS 2026
   • Extended abstract accepted to ML×OR Workshop, NeurIPS 2026
   Abstract: Conformal Robustness in Prediction-Driven Decision-Making

   Modern decision-making processes are increasingly driven by highly complex predictors that leverage rich contextual information, but the forecasted quantities alone do not provide the error scale needed to compare and control downstream decision risk. We develop a conformal calibration interface that converts any context-specific point predictor into a score-based robustness representation. Given a conformal score, empirical score quantiles define coverage-calibrated uncertainty sets for Conformal Robust Optimization (ConfRO), while the same score normalizes target violations in Conformal Robust Satisficing (ConfRS). Thus, the conformal score serves as a common robustness scale: uncertainty geometry for reliability-driven decisions and fragility for aspiration-driven decisions are expressed on the same data-calibrated prediction-error scale. We establish finite-sample marginal coverage guarantees for the induced uncertainty sets, decision-efficiency bounds that expose the roles of prediction accuracy and score design, and empirical tail-risk certificates for satisficing decisions. For objective-uncertainty problems satisfying convexity and strong duality, we show that ConfRO and ConfRS are two parameterizations of the same score-calibrated decision frontier, yielding a data-driven mapping between reliability levels and aspiration targets. Synthetic experiments validate the theoretical guarantees and illustrate the reliability-target tradeoff. A large-scale e-grocery perishable-inventory case study further shows that the framework improves reliability and cost performance relative to predict-then-optimize, context-agnostic robust optimization, and neighbor-based baselines, while generating practical guidance on score geometry and calibration.

   arXivSlidesDataPDF

2. On the Nonparametric Maximum Likelihood Approach to Mixture of Regression

   Hansheng Jiang, Adityanand Guntuboyina

   Major revision at Journal of the American Statistical Association

   • IISA Best Student Paper – Theory/Methodology Track, Winner
   Abstract: On the Nonparametric Maximum Likelihood Approach to Mixture of Regression

   Mixture of regression models are useful for regression analysis in heterogeneous populations where a single regression model may not be appropriate for the entire population. We study the nonparametric maximum likelihood estimator (NPMLE) for fitting these models. The NPMLE is based on convex optimization and does not require prior specification of the number of mixture components. We establish existence of the NPMLE and prove finite-sample parametric (up to logarithmic multiplicative factors) Hellinger error bounds for the predicted density functions. We also provide an effective procedure for computing the NPMLE without ad-hoc discretization and prove a theoretical convergence rate under certain assumptions. Numerical experiments on simulated data for both discrete and non-discrete mixing distributions demonstrate the remarkable performance of our approach. We also illustrate the approach on two real data sets.

   arXivSlidesCodePDF

3. Spatial Supply Repositioning with Censored Demand Data

   Hansheng Jiang, Chunlin Sun, ZJ Max Shen

   Major revision at Manufacturing & Service Operations Management

   • CSAMSE Best Paper Competition, Second Place
   • YinzOR Student Conference Flash Talk Competition, Winner
   Abstract: Spatial Supply Repositioning with Censored Demand Data

   We consider the vehicle repositioning problem for a one-way on-demand vehicle sharing service with a fixed number of rental units distributed across the network. Due to uncertainty in both customer arrivals and vehicle returns, the service provider needs to periodically reposition the vehicles to match the supply with the demand while minimizing the total costs of repositioning labor and lost sales. The repositioning problem is critical in the successful management of on-demand one-way vehicle sharing services, and it is challenging both analytically and computationally. The optimal repositioning policy under a general $n$-location network is intractable without knowing the optimal value function. We define the best base-stock repositioning policy as a generalization of the popular inventory control policy to the vehicle repositioning problem, and we establish its asymptotic optimality in two different limiting regimes under general network structures. We develop learning methods to dynamically reposition vehicles to find the best base-stock policy with censored demand. By establishing a tight concentration inequality, we show that the one-time learning algorithm achieves a regret of $\widetilde{\bigo}\left(T^{\frac{2}{3}}\right)$, which is independent of the number of locations in the network. We furthermore develop an online stochastic gradient descent algorithm using only censored demand and prove that it achieves a regret of $\widetilde{\bigo}\left(T^{\frac{1}{2}}\right)$ under a cost structure assumption. Crucially, our online algorithm is based on a novel decomposition of cumulative costs and linear programming reformulation of the offline problem. Numerical experiments illustrate the effective performance of our approaches.

   SSRNSlidesCodePDF

4. Polylogarithmic Sparsity of Randomly Reweighted NPMLEs for Gaussian Mixtures

   Hansheng Jiang

   Abstract: Polylogarithmic Sparsity of Randomly Reweighted NPMLEs for Gaussian Mixtures

   The nonparametric maximum likelihood estimator (NPMLE) of a Gaussian location mixture maximizes the likelihood over the infinite-dimensional space of mixing distributions. The maximizing mixing distribution can be nonunique, and the classical bound on its number of atoms grows linearly with the sample size n. We show that a vanishingly small random perturbation of the likelihood yields exact polylogarithmic sparsity. The resulting randomly reweighted NPMLE maximizes a weighted likelihood whose independent weights, taken to be Gamma in our analysis, concentrate around one as n grows. With high probability, it is unique, has O{(log n / log log n)^d + log n} atoms in dimension d, nearly maximizes the ordinary likelihood, and estimates the mixture density at a Hellinger rate that is parametric up to logarithmic factors. This sparsity holds for the estimator itself, not for an approximation of it, and requires no support penalty. The proof rests on an effective-dimension principle for positive kernel mixtures: low-dimensional variation of the fitted values controls the support of every extreme point of the set of maximizers. Numerical illustrations verify that the reweighted NPMLE has Hellinger risk and support size comparable to those of the ordinary NPMLE.

   arXivPDF

Peer-Reviewed Conference Proceedings

Note: Conference papers that are superseded by their journal versions are not listed below

1. Learning in Continuous State-Space MDPs for Network Inventory Management

   Hansheng Jiang, Shunan Jiang, ZJ Max Shen

   International Conference on Artificial Intelligence and Statistics (AISTATS), 2026

   Abstract: Learning in Continuous State-Space MDPs for Network Inventory Management

   We consider online learning in infinite-horizon, average-cost Markov Decision Processes (MDPs) with multi-dimensional, continuous state spaces and censored feedback. Our model setting, motivated by network inventory management applications such as vehicle sharing, is characterized by complex, correlated state transitions and the absence of value function convexity, rendering standard analytical techniques for both MDPs and inventory control inapplicable. Our primary contribution is a integrated framework establishing and leveraging the Lipschitz property of the long-run average cost function. This insight allows us to analyze the problem through the lens of Lipschitz bandits, for which we design a provably efficient online learning algorithm that learns a near-optimal policy from censored demand data. We derive a high-probability regret bound of $O(T^{\frac{n}{n+1}} (\log T)^{\frac{1}{n+1}})$, where $n$ is the network size through new concentration inequalities for cumulative costs in MDPs with state-dependent transitions. Furthermore, we devise a matching lower bound for this learning problem, which captures the inherent dimensionality challenge.

   PosterCodeProceedingPDF

2. Simulation-Based Evaluation of Energy-Constrained Quantum–Classical Competition

   Junyu Liu, Hansheng Jiang, ZJ Max Shen

   Winter Simulation Conference (WSC), 2026

   Abstract: Simulation-Based Evaluation of Energy-Constrained Quantum–Classical Competition

   Energy cost is increasingly crucial in the modern computing industry with the wide deployment of large-scale machine learning models and language models. For the firms that provide computing services, low energy consumption is important both from the perspective of their own market growth and the government's regulations. In this paper, we study the energy benefits of quantum computing vis-a-vis classical computing. Deviating from the conventional notion of quantum advantage based solely on computational complexity, we redefine advantage in an energy efficiency context. Through a Cournot competition model constrained by energy usage, we demonstrate quantum computing firms can outperform classical counterparts in both profitability and energy efficiency at Nash equilibrium. Therefore quantum computing may represent a more sustainable pathway for the computing industry. Moreover, we discover that the energy benefits of quantum computing economies are contingent on large-scale computation. Based on real physical parameters, we further illustrate the scale of operation necessary for realizing this energy efficiency advantage.

   arXivPDF

3. Out-of-Distribution Robust Optimization

   Zhongze Cai^†, Hansheng Jiang, Xiaocheng Li

   Conference on Uncertainty in Artificial Intelligence (UAI), 2025

   Abstract: Out-of-Distribution Robust Optimization

   In this paper, we consider the contextual robust optimization problem under an out-of-distribution setting. The contextual robust optimization problem considers a risk-sensitive objective function for an optimization problem with the presence of a context vector (also known as covariates or side information) capturing related information. While the existing works mainly consider the in-distribution setting, and the resultant robustness achieved is in an out-of-sample sense, our paper studies an out-of-distribution setting where there can be a difference between the test environment and the training environment where the data are collected. We propose methods that handle this out-of-distribution setting, and the key relies on a density ratio estimation for the distribution shift. We show that additional structures such as covariate shift and label shift are not only helpful in defending distribution shift but also necessary in avoiding non-trivial solutions compared to other principled methods such as distributionally robust optimization. We also illustrate how the covariates can be useful in this procedure. Numerical experiments generate more intuitions and demonstrate that the proposed methods can help avoid over-conservative solutions.

   arXivProceedingPDF

4. Smoothness-Adaptive Dynamic Pricing with Nonparametric Demand Learning

   Zeqi Ye^†, Hansheng Jiang

   International Conference on Artificial Intelligence and Statistics (AISTATS), 2024

   Abstract: Smoothness-Adaptive Dynamic Pricing with Nonparametric Demand Learning

   We study the dynamic pricing problem where the demand function is nonparametric and H\"older smooth, and we focus on adaptivity to the unknown H\"older smoothness parameter $\beta$ of the demand function. Traditionally the optimal dynamic pricing algorithm heavily relies on the knowledge of $\beta$ to achieve a minimax optimal regret of $\widetilde{O}(T^{\frac{\beta+1}{2\beta+1}})$. However, we highlight the challenge of adaptivity in this dynamic pricing problem by proving that no pricing policy can adaptively achieve this minimax optimal regret without knowledge of $\beta$. Motivated by the impossibility result, we propose a self-similarity condition to enable adaptivity. Importantly, we show that the self-similarity condition does not compromise the problem's inherent complexity since it preserves the regret lower bound $\Omega(T^{\frac{\beta+1}{2\beta+1}})$. Furthermore, we develop a smoothness-adaptive dynamic pricing algorithm and theoretically prove that the algorithm achieves this minimax optimal regret bound without the prior knowledge $\beta$.

   arXivSlidesPosterCodeProceedingPDF

5. Quantum Computing Methods for Supply Chain Management

   Hansheng Jiang, ZJ Max Shen, Junyu Liu

   IEEE/ACM Symposium on Edge Computing (SEC), 2022

   Abstract: Quantum Computing Methods for Supply Chain Management

   Quantum computing is expected to have transformative influences on many domains, but its practical deployments on industry problems are underexplored. We focus on applying quantum computing to operations management problems in industry, and in particular, supply chain management. Many problems in supply chain management involve large state and action spaces and pose computational challenges on classic computers. We develop a quantized policy iteration algorithm to solve an inventory control problem and demonstrative its effectiveness. We also discuss in-depth the hardware requirements and potential challenges on implementing this quantum algorithm in the near term. Our simulations and experiments are powered by the IBM Qiskit and the qBraid system.

   arXivProceedingPDF

Ph.D. Dissertation

1. Operations and Data Analytics in Platforms: Theory and Applications

   Hansheng Jiang

   University of California, Berkeley, 2023

   • Supply Chain Analytics Institute Annual Conference Best Dissertation Competition, Second Place