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1. Hierarchical Grouping of Simple Visual Scenes

   Rice, P. J.; Mao, L.; Zhu, A.; Wu, E.' & (January 2023, Proceedings of the Forty-Fifth Annual Meeting of the Cognitive Science Society)

   Human visual grouping processes consolidate independent visual objects into grouped visual features on the basis of shared characteristics; these visual features can themselves be grouped, resulting in a hierarchical representation of visual grouping information. This “grouping hierarchy“ promotes ef- ficient attention in the support of goal-directed behavior, but improper grouping of elements of a visual scene can also re- sult in critical behavioral errors. Understanding of how visual object/features characteristics such as size and form influences perception of hierarchical visual groups can further theory of human visual grouping behavior and contribute to effective in- terface design. In the present study, participants provided free- response groupings of a set of stimuli that contained consistent structural relationships between a limited set of visual features. These grouping patterns were evaluated for relationships be- tween specific characteristics of the constituent visual features and the distribution of features across levels of the indicated grouping hierarchy. We observed that while the relative size of the visual features differentiated groupings across levels of the grouping hierarchy, the form of visual objects and features was more likely to distinguish separate groups within a partic- ular level of hierarchy. These consistent relationships between visual feature characteristics and placement within a grouping hierarchy can be leveraged to advance computational theories of human visual grouping behavior, which can in turn be ap- plied to effective design for interfaces such as voter ballots. 

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2. Hierarchical Grouping of Simple Visual Scenes

   Rice, P. J.; Mao, L.; Zhu, A.; Wu, E.' & (January 2023, Proceedings of the Forty-Fifth Annual Meeting of the Cognitive Science Society)

   Human visual grouping processes consolidate independent visual objects into grouped visual features on the basis of shared characteristics; these visual features can themselves be grouped, resulting in a hierarchical representation of visual grouping information. This “grouping hierarchy“ promotes ef- ficient attention in the support of goal-directed behavior, but improper grouping of elements of a visual scene can also re- sult in critical behavioral errors. Understanding of how visual object/features characteristics such as size and form influences perception of hierarchical visual groups can further theory of human visual grouping behavior and contribute to effective in- terface design. In the present study, participants provided free- response groupings of a set of stimuli that contained consistent structural relationships between a limited set of visual features. These grouping patterns were evaluated for relationships be- tween specific characteristics of the constituent visual features and the distribution of features across levels of the indicated grouping hierarchy. We observed that while the relative size of the visual features differentiated groupings across levels of the grouping hierarchy, the form of visual objects and features was more likely to distinguish separate groups within a partic- ular level of hierarchy. These consistent relationships between visual feature characteristics and placement within a grouping hierarchy can be leveraged to advance computational theories of human visual grouping behavior, which can in turn be ap- plied to effective design for interfaces such as voter ballots. 

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3. Information‐incorporated sparse hierarchical cancer heterogeneity analysis

   https://doi.org/10.1002/sim.10071  

   Han, Wei; Zhang, Sanguo; Ma, Shuangge; Ren, Mingyang (May 2024, Statistics in Medicine)

   Cancer heterogeneity analysis is essential for precision medicine. Most of the existing heterogeneity analyses only consider a single type of data and ignore the possible sparsity of important features. In cancer clinical practice, it has been suggested that two types of data, pathological imaging and omics data, are commonly collected and can produce hierarchical heterogeneous structures, in which the refined sub‐subgroup structure determined by omics features can be nested in the rough subgroup structure determined by the imaging features. Moreover, sparsity pursuit has extraordinary significance and is more challenging for heterogeneity analysis, because the important features may not be the same in different subgroups, which is ignored by the existing heterogeneity analyses. Fortunately, rich information from previous literature (for example, those deposited in PubMed) can be used to assist feature selection in the present study. Advancing from the existing analyses, in this study, we propose a novel sparse hierarchical heterogeneity analysis framework, which can integrate two types of features and incorporate prior knowledge to improve feature selection. The proposed approach has satisfactory statistical properties and competitive numerical performance. A TCGA real data analysis demonstrates the practical value of our approach in analyzing data heterogeneity and sparsity. 

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4. Optimal Generalized Decision Trees via Integer Programming,

   Gunluk, O. (January 2021, Journal of global optimization)

   Decision trees have been a very popular class of predictive models for decades due to their interpretability and good performance on categorical features. However, they are not always robust and tend to overfit the data. Additionally, if allowed to grow large, they lose interpretability. In this paper, we present a mixed integer programming formulation to construct optimal decision trees of a prespecified size. We take the special structure of categorical features into account and allow combinatorial decisions (based on subsets of values of features) at each node. Our approach can also handle numerical features via thresholding. We show that very good accuracy can be achieved with small trees using moderately-sized training sets. The optimization problems we solve are tractable with modern solvers. 

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5. Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

   https://doi.org/10.1145/3461702.3462597  

   Kommiya Mothilal, Ramaravind; Mahajan, Divyat; Tan, Chenhao; Sharma, Amit (May 2021, Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society)

   null (Ed.)

   Feature attributions and counterfactual explanations are popular approaches to explain a ML model. The former assigns an importance score to each input feature, while the latter provides input examples with minimal changes to alter the model's predictions. To unify these approaches, we provide an interpretation based on the actual causality framework and present two key results in terms of their use. First, we present a method to generate feature attribution explanations from a set of counterfactual examples. These feature attributions convey how important a feature is to changing the classification outcome of a model, especially on whether a subset of features is necessary and/or sufficient for that change, which attribution-based methods are unable to provide. Second, we show how counterfactual examples can be used to evaluate the goodness of an attribution-based explanation in terms of its necessity and sufficiency. As a result, we highlight the complimentary of these two approaches. Our evaluation on three benchmark datasets --- Adult-Income, LendingClub, and German-Credit --- confirms the complimentary. Feature attribution methods like LIME and SHAP and counterfactual explanation methods like Wachter et al. and DiCE often do not agree on feature importance rankings. In addition, by restricting the features that can be modified for generating counterfactual examples, we find that the top-k features from LIME or SHAP are often neither necessary nor sufficient explanations of a model's prediction. Finally, we present a case study of different explanation methods on a real-world hospital triage problem. 

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