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1. Revenue-Utility Tradeoff in Assortment Optimization Under the Multinomial Logit Model with Totally Unimodular Constraints

   https://doi.org/10.1287/mnsc.2020.3657  

   Sumida, Mika; Gallego, Guillermo; Rusmevichientong, Paat; Topaloglu, Huseyin; Davis, James (May 2021, Management Science)

   We examine the revenue–utility assortment optimization problem with the goal of finding an assortment that maximizes a linear combination of the expected revenue of the firm and the expected utility of the customer. This criterion captures the trade-off between the firm-centric objective of maximizing the expected revenue and the customer-centric objective of maximizing the expected utility. The customers choose according to the multinomial logit model, and there is a constraint on the offered assortments characterized by a totally unimodular matrix. We show that we can solve the revenue–utility assortment optimization problem by finding the assortment that maximizes only the expected revenue after adjusting the revenue of each product by the same constant. Finding the appropriate revenue adjustment requires solving a nonconvex optimization problem. We give a parametric linear program to generate a collection of candidate assortments that is guaranteed to include an optimal solution to the revenue–utility assortment optimization problem. This collection of candidate assortments also allows us to construct an efficient frontier that shows the optimal expected revenue–utility pairs as we vary the weights in the objective function. Moreover, we develop an approximation scheme that limits the number of candidate assortments while ensuring a prespecified solution quality. Finally, we discuss practical assortment optimization problems that involve totally unimodular constraints. In our computational experiments, we demonstrate that we can obtain significant improvements in the expected utility without incurring a significant loss in the expected revenue. This paper was accepted by Omar Besbes, revenue management and market analytics. 

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2. DualGFL: Federated Learning with a Dual-Level Coalition-Auction Game

   https://doi.org/10.1609/aaai.v39i15.33746  

   Chen, Xiaobing; Zhou, Xiangwei; Zhang, Songyang; Sun, Mingxuan (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   Despite some promising results in federated learning using game-theoretical methods, most existing studies mainly employ a one-level game in either a cooperative or competitive environment, failing to capture the complex dynamics among participants in practice. To address this issue, we propose DualGFL, a novel federated learning framework with a dual-level game in cooperative-competitive environments. DualGFL includes a lower-level hedonic game where clients form coalitions and an upper-level multi-attribute auction game where coalitions bid for training participation.At the lower-level DualGFL, we introduce a new auction-aware utility function and propose a Pareto-optimal partitioning algorithm to find a Pareto-optimal partition based on clients' preference profiles.At the upper-level DualGFL, we formulate a multi-attribute auction game with resource constraints and derive equilibrium bids to maximize coalitions' winning probabilities and profits. A greedy algorithm is proposed to maximize the utility of the central server.Extensive experiments on real-world datasets demonstrate DualGFL's effectiveness in improving both server utility and client utility. 

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3. Streaming submodular maximization with differential privacy

   Chaturvedi, Anamay; Nguyen, Huy; Nguyen, Thy D (July 2023, Proceedings of Machine Learning Research)

   In this work, we study the problem of privately maximizing a submodular function in the streaming setting. Extensive work has been done on privately maximizing submodular functions in the general case when the function depends upon the private data of individuals. However, when the size of the data stream drawn from the domain of the objective function is large or arrives very fast, one must privately optimize the objective within the constraints of the streaming setting. We establish fundamental differentially private baselines for this problem and then derive better trade-offs between privacy and utility for the special case of decomposable submodular functions. A submodular function is decomposable when it can be written as a sum of submodular functions; this structure arises naturally when each summand function models the utility of an individual and the goal is to study the total utility of the whole population as in the well-known Combinatorial Public Projects Problem. Finally, we complement our theoretical analysis with experimental corroboration. 

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4. Aerodynamic Analysis of the Utility Truck With the Morphing Boom Equipment

   https://doi.org/10.1115/FEDSM2022-88368  

   Patel, Parth Y.; Jannoi, Thannathorn; Zou, Wenhui; Vantsevich, Vladimir; Koomullil, Roy (September 2022, Aerodynamic Analysis of the Utility Truck With the Morphing Boom Equipment)

   Global climate change has affected the human race for decades. As a result, severe weather changes and more substantial hurricane impact have become a typical scenario. Utility trucks with the morphing boom equipment are the first responders to access these disaster areas in bad weather conditions and restore the damages caused by the disaster. The stability of the utility trucks while driving in a heavy wind scenario is an essential aspect for the safety of the rescue crew, and aerodynamic forces caused by the wind flow constitute a significant factor that influences the stability of the utility truck. In this paper, the aerodynamic performance of the utility truck is modeled using the incompressible unsteady Reynolds Averaged Navier Stokes (URANS) model. The Ahmed body, a well-recognized benchmark test case used by the computational fluid dynamics (CFD) community for the aerodynamic model validation of automobiles, is used to validate this aerodynamic model. The validated aerodynamic model investigates the impact of heavy wind on the utility truck with the morphing boom equipment. The visualization of the flow field around the utility truck with the force and moment coefficients at various side slip angles are presented in this paper. 

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5. Approximating Functions with Approximate Privacy for Applications in Signal Estimation and Learning

   https://doi.org/10.3390/e25050825  

   Tasnim, Naima; Mohammadi, Jafar; Sarwate, Anand D.; Imtiaz, Hafiz (May 2023, Entropy)

   Large corporations, government entities and institutions such as hospitals and census bureaus routinely collect our personal and sensitive information for providing services. A key technological challenge is designing algorithms for these services that provide useful results, while simultaneously maintaining the privacy of the individuals whose data are being shared. Differential privacy (DP) is a cryptographically motivated and mathematically rigorous approach for addressing this challenge. Under DP, a randomized algorithm provides privacy guarantees by approximating the desired functionality, leading to a privacy–utility trade-off. Strong (pure DP) privacy guarantees are often costly in terms of utility. Motivated by the need for a more efficient mechanism with better privacy–utility trade-off, we propose Gaussian FM, an improvement to the functional mechanism (FM) that offers higher utility at the expense of a weakened (approximate) DP guarantee. We analytically show that the proposed Gaussian FM algorithm can offer orders of magnitude smaller noise compared to the existing FM algorithms. We further extend our Gaussian FM algorithm to decentralized-data settings by incorporating the CAPE protocol and propose capeFM. Our method can offer the same level of utility as its centralized counterparts for a range of parameter choices. We empirically show that our proposed algorithms outperform existing state-of-the-art approaches on synthetic and real datasets. 

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