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1. PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Data Subset Selection.

   https://doi.org/10.1609/aaai.v36i9.21264  

   Kothawade, Suraj and ( April 2022 , Proceedings of the AAAI Conference on Artificial Intelligence)

   With ever-increasing dataset sizes, subset selection techniques are becoming increasingly important for a plethora of tasks. It is often necessary to guide the subset selection to achieve certain desiderata, which includes focusing or targeting certain data points, while avoiding others. Examples of such problems include: i) targeted learning, where the goal is to find subsets with rare classes or rare attributes on which the model is underperforming, and ii) guided summarization, where data (e.g., image collection, text, document or video) is summarized for quicker human consumption with specific additional user intent. Motivated by such applications, we present PRISM, a rich class of PaRameterIzed Submodular information Measures. Through novel functions and their parameterizations, PRISM offers a variety of modeling capabilities that enable a trade-off between desired qualities of a subset like diversity or representation and similarity/dissimilarity with a set of data points. We demonstrate how PRISM can be applied to the two real-world problems mentioned above, which require guided subset selection. In doing so, we show that PRISM interestingly generalizes some past work, therein reinforcing its broad utility. Through extensive experiments on diverse datasets, we demonstrate the superiority of PRISM over the state-of-the-art in targeted learning and in guided image-collection summarization. 

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2. Online Summarization via Submodular and Convex Optimization

   Elhamifar, E. ; De Paolis Kaluza, M. C. ( January 2017 , IEEE Conference on Computer Vision and Pattern Recognition)

   We consider the problem of subset selection in the online setting, where data arrive incrementally. Instead of storing and running subset selection on the entire dataset, we propose an incremental subset selection framework that, at each time instant, uses the previously selected set of representatives and the new batch of data in order to update the set of representatives. We cast the problem as an integer bi- nary optimization minimizing the encoding cost of the data via representatives regularized by the number of selected items. As the proposed optimization is, in general, NP-hard and non-convex, we study a greedy approach based on un- constrained submodular optimization and also propose an efficient convex relaxation. We show that, under appropriate conditions, the solution of our proposed convex algorithm achieves the global optimal solution of the non-convex problem. Our results also address the conventional problem of subset selection in the offline setting, as a special case. By extensive experiments on the problem of video summarization, we demonstrate that our proposed online subset selection algorithms perform well on real data, capturing diverse representative events in videos, while they obtain objective function values close to the offline setting. 

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3. Online Summarization via Submodular and Convex Optimization

   Elhamifar, E. ; De Paolis Kaluza, M. C. ( January 2017 , IEEE Conference on Computer Vision and Pattern Recognition)

   We consider the problem of subset selection in the online setting, where data arrive incrementally. Instead of storing and running subset selection on the entire dataset, we propose an incremental subset selection framework that, at each time instant, uses the previously selected set of representatives and the new batch of data in order to update the set of representatives. We cast the problem as an integer binary optimization minimizing the encoding cost of the data via representatives regularized by the number of selected items. As the proposed optimization is, in general, NP-hard and non-convex, we study a greedy approach based on unconstrained submodular optimization and also propose an efficient convex relaxation. We show that, under appropriate conditions, the solution of our proposed convex algorithm achieves the global optimal solution of the non-convex problem. Our results also address the conventional problem of subset selection in the offline setting, as a special case. By extensive experiments on the problem of video summarization, we demonstrate that our proposed online subset selection algorithms perform well on real data, capturing diverse representative events in videos, while they obtain objective function values close to the offline setting. 

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4. Image classification and training with severe data loss

   https://doi.org/10.1117/12.2633172  

   Marquard, Dillon ; Wright, Kyle ; Marcia, Roummel F. ( October 2022 , SPIE Optical Engineering + Applications, 2022)

   Zelinski, Michael E. ; Taha, Tarek M. ; Howe, Jonathan (Ed.)

   Image classification forms an important class of problems in machine learning and is widely used in many realworld applications, such as medicine, ecology, astronomy, and defense. Convolutional neural networks (CNNs) are machine learning techniques designed for inputs with grid structures, e.g., images, whose features are spatially correlated. As such, CNNs have been demonstrated to be highly effective approaches for many image classification problems and have consistently outperformed other approaches in many image classification and object detection competitions. A particular challenge involved in using machine learning for classifying images is measurement data loss in the form of missing pixels, which occurs in settings where scene occlusions are present or where the photodetectors in the imaging system are partially damaged. In such cases, the performance of CNN models tends to deteriorate or becomes unreliable even when the perturbations to the input image are small. In this work, we investigate techniques for improving the performance of CNN models for image classification with missing data. In particular, we explore training on a variety of data alterations that mimic data loss for producing more robust classifiers. By optimizing the categorical cross-entropy loss function, we demonstrate through numerical experiments on the MNIST dataset that training with these synthetic alterations can enhance the classification accuracy of our CNN models. 

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5. FINS Auditing Framework: Group Fairness for Subset Selection

   https://doi.org/10.1145/3514094.3534160  

   Cachel, K. ; Rundensteiner, E. ( August 2022 , AAAI/ACM Conference on AI, Ethics, and Society (AEIS))

   Subset selection is an integral component of AI systems that is increasingly affecting people’s livelihoods in applications ranging from hiring, healthcare, education, to financial decisions. Subset selections powered by AI-based methods include top- analytics, data summarization, clustering, and multi-winner voting. While group fairness auditing tools have been proposed for classification systems, these state-of-the-art tools are not directly applicable to measuring and conceptualizing fairness in selected subsets. In this work, we introduce the first comprehensive auditing framework, FINS, to support stakeholders in interpretably quantifying group fairness across a diverse range of subset-specific fairness concerns. FINS offers a family of novel measures that provide a flexible means to audit group fairness for fairness goals ranging from item-based, score-based, and a combination thereof. FINS provides one unified easy-to-understand interpretation across these different fairness problems. Further, we develop guidelines through the FINS Fair Subset Chart, that supports auditors in determining which measures are relevant to their problem context and fairness objectives. We provide a comprehensive mapping between each fairness measure and the belief system (i.e., worldview) that is encoded within its measurement of fairness. Lastly, we demonstrate the interpretability and efficacy of FINS in supporting the identification of real bias with case studies using AirBnB listings and voter records. 

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