We propose a new form of predictive models, Partially Interpretable Estimators (PIE), which jointly train an interpretable model and a black-box model to achieve partial model transparency while maintaining high predictive performance. Our design is motivated by prior research showing that interpretability does not require exposing all model details. Therefore, our objective is to explain the main components of the prediction, withholding complicated calculations that may not be necessary for users. PIE is designed to attribute a prediction to the contribution from individual features via a sparse linear additive model to achieve interpretability while complementing the prediction with a black-box model to boost the predictive performance. As such, the linear additive model captures the primary feature contributions, while the black-box component augments PIE’s predictive power by capturing the “nuances” of feature interactions as a refinement. Moreover, we include a sparsity constraint, allowing users to adjust the model to meet domain-specific needs of interpretability. To optimize predictive performance, we propose a coordinated training algorithm that jointly trains the two components of PIE. Experimental results show that PIE achieves accuracy comparable to state-of-the-art black-box models, with human assessments confirming that its interpretability is nearly equivalent to linear models. History: Accepted by Ram Ramesh, Data Science & Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0098 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0098 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

The software and data in this repository are a snapshot of the software and data that were used in the research reported on in the paper Combating Fake News on Social Media: An Early Detection Approach using Multimodal Adversarial Transfer Learning by Cong Wang and Chuchun Zhang and Runyu Chen.

This repository provides part of the implementation of our branch-and-price algorithm for solving the Set-Union Bin Packing Problem (SUBP). More precisely, it provides all problem-specific parts of our algorithm - most notably various algorithms for solving the column-generation pricing problem. It does not provide the general branch-and-price infrastructure, because we do not have the full rights to share this part of the software. For this project, we integrated our code with a branch-and-price framework jointly developed and maintained by several research groups. Because this code is not open source, we are unable to make the full standalone codebase public.

This software learns a dispatching and rebalancing policy for autonomous-mobility on demand systems using a structured learning enriched combinatorial optimization pipeline.

Forecasting the market risk for publicly traded companies is a critical task for market participants. Financial economics research demonstrates that the textual information contained in corporate disclosures, such as earnings conference call transcripts, can effectively predict a firm’s future risk. This finding has inspired a growing body of research focused specifically on transcript-based approaches to risk forecasting. However, earnings transcripts are typically long documents with thousands of words. Prior transcript-based risk forecasting studies that represent the entire transcript as one text sequence often fail to capture risk-relevant information and fall short in risk forecasting. In this work, we propose a novel divide-and-contrast machine learning method for predicting risks from earnings conference call transcripts. We exploit the semistructured nature of an earnings transcript and decompose it into several semantically coherent conversation units, ranging from the finest grained question–answer pair level to the coarsest grained transcript level. We then propose contrastive learning objectives as an auxiliary task to the risk forecasting objective, facilitating the learning of risk-relevant information from the earnings transcripts. We conduct experiments on a data set of U.S. market earnings call transcripts. The experimental results show that our proposed divide-and-contrast method substantially outperforms state-of-the-art methods by significantly reducing errors in risk forecasting. This paper sheds light on extracting informative insights from lengthy financial documents to support informed decision making. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0195 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0195 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

The continuous network design problem (CNDP) has been recognized as one of the most challenging issues in the field of transportation. Existing approaches to solving the CNDP are primarily heuristic without convergence guarantee or suitable for handling small networks because of the inherent nonconvexity arising from its bilevel hierarchical structure. An efficient and convergent approach for solving the CNDP on large networks has been fervently sought. In this paper, we present a novel convergent approach centered around exploiting the inherent convexity-related structure within the CNDP. We first demonstrate that the CNDP can be equivalently formulated as a difference of convex (DC) program with all involved functions being either convex functions or DC functions. Then, by exploiting the DC structure, we give a convex programming approximation for the CNDP and subsequently propose a penalized sequential convex programming approach. Finally, we show that the proposed method can yield an approximately stationary point under commonly used conditions. A numerical study is conducted on some real networks from a reputable network repository for transportation research. The numerical results demonstrate that the proposed method achieves better solutions with faster computational speed, particularly on larger networks, as compared with two heuristic approaches and two convergent approaches. History: Accepted by Russell Bent, Area Editor for Network Optimization: Algorithms and Applications. Funding: This research was supported by the National Science Foundation of China [Grants 72131007, 72140006, 12271161, 12222106, and 12326605], the Natural Science Foundation of Shanghai [Grant 22ZR1415900], and Guangdong Basic and Applied Basic Research Foundation [Grant 2022B1515020082]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0737 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0737 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

The goal of this repository is to share data and results related to the non-convex quadratically constrained program (QCP), solved using our asymptotically tight mixed-integer linear programming (MILP) approximations.

The software and data in this repository are a snapshot of the software and data that were used in the research reported on in the paper Sempervirens: A Fast Reconstruction Algorithm for Noisy and Incomplete Binary Matrix Representations of Trees. The snapshot is based on this SHA in the development repository.

Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on a black-box numerical code or real-world experiment, which we refer to as a simulation. Whereas an extensive body of research is dedicated to developing new algorithms and methods for solving these and related problems, it is challenging and time-consuming to integrate these techniques into real-world production-ready solvers. This is partly because of the diversity and complexity of modern state-of-the-art MOSO algorithms and methods and partly because of the complexity and specificity of many real-world problems and their corresponding computing environments. The complexity of this problem is only compounded when introducing potentially complex and/or domain-specific surrogate-modeling techniques, problem formulations, design spaces, and data acquisition functions. This paper carefully surveys the current state of the art in MOSO algorithms, techniques, and solvers, as well as problem types and computational environments where MOSO is commonly applied. We then present several key challenges in the design of a parallel multiobjective simulation optimization framework (ParMOO) and how they have been addressed. Finally, we provide two case studies demonstrating how customized ParMOO solvers can be quickly built and deployed to solve real-world MOSO problems. History: Accepted by Alice Smith and Ted Ralphs, Area Editors for Software Tools. Funding: This work was supported by US DOE, Office of Science, Advanced Scientific Computing Research, SciDAC Program [Grants DE-AC02-05CH11231 and DE-AC02-06CH11357]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0250 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0250 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Increasing reliability and reducing disruptions in supply networks are of increasing importance; for example, power outages in electricity distribution networks cost $35–$50 billion annually in the United States. Motivated by the operational constraints of such networks and their rapid adoption of decentralized paradigms and self-healing components, we introduce the minimum reconnection time (MRT) problem, which models reliability metrics such as the System Average Interruption Duration Index (SAIDI). MRT seeks to reduce outage time after network disruptions by programming reconnection times of different edges (i.e., switches), ensuring that the operating network is acyclic. We show that MRT is NP-hard and is a special case of the well-known (weighted) minimum sum set cover (MSSC) problem. We develop the theory of kernel-based randomized rounding approaches to give a tight polynomial-time approximation for MSSC, improving the state-of-the-art approximation factor for these instances. Further, motivated by the reliability incentive structure for utility companies and operational energy losses in distribution networks, we study minimizing energy losses and reliability metrics such as SAIDI and reconnection times simultaneously. Optimizing for any single objective at a time can create unfair duration of expected outage for industrial and residential areas. We, therefore, propose local search over spanning trees to balance these multiple objectives. We computationally validate our reconfiguration methods on the National Renewable Energy Laboratory Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios Greensboro synthetic network and show that this improves equity by a factor of four across industrial and residential areas. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: C. Hettle and D. Molzahn's research was partially supported by the National Science Foundation [Grant NSF 2112533], received by the Georgia Institute of Technology. S. Gupta’s research is supported by the National Science Foundation CAREER [Grant 2239824], received by the Massachusetts Institute of Technology. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0295 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0295 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .