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1. An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts

   Jew, Brandon; Li, Jiajin; Sankararaman, Sriram; Sul, Jae-Hoon (January 2021, 21st International Workshop on Algorithms in Bioinformatics, {WABI})

   Linear mixed models (LMMs) can be applied in the meta-analyses of responses from individuals across multiple contexts, increasing power to detect associations while accounting for confounding effects arising from within-individual variation. However, traditional approaches to fitting these models can be computationally intractable. Here, we describe an efficient and exact method for fitting a multiple-context linear mixed model. Whereas existing exact methods may be cubic in their time complexity with respect to the number of individuals, our approach for multiple-context LMMs (mcLMM) is linear. These improvements allow for large-scale analyses requiring computing time and memory magnitudes of order less than existing methods. As examples, we apply our approach to identify expression quantitative trait loci from large-scale gene expression data measured across multiple tissues as well as joint analyses of multiple phenotypes in genomewide association studies at biobank scale. 

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2. An Efficient Linear Mixed Model Framework for Meta-Analytic Association Studies Across Multiple Contexts

   Brandon Jew, Jiajin Li (January 2021, Proceedings of the 21st International Workshop on Algorithms in Bioinformatics (WABI 2021))

   Alessandra Carbone, Mohammed El-Kebir (Ed.)

   Linear mixed models (LMMs) can be applied in the meta-analyses of responses from individuals across multiple contexts, increasing power to detect associations while accounting for confounding effects arising from within-individual variation. However, traditional approaches to fitting these models can be computationally intractable. Here, we describe an efficient and exact method for fitting a multiple-context linear mixed model. Whereas existing exact methods may be cubic in their time complexity with respect to the number of individuals, our approach for multiple-context LMMs (mcLMM) is linear. These improvements allow for large-scale analyses requiring computing time and memory magnitudes of order less than existing methods. As examples, we apply our approach to identify expression quantitative trait loci from large-scale gene expression data measured across multiple tissues as well as joint analyses of multiple phenotypes in genome-wide association studies at biobank scale. 

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3. Scalable Algorithms for Learning High-Dimensional Linear Mixed Models

   Zilong Tan, Kimberly Roche (January 2018, Proceedings of the Thirty-Fourth Conference on Uncertainty in Artificial Intelligence)

   Linear mixed models (LMMs) are used extensively to model observations that are not independent. Parameter estimation for LMMs can be computationally prohibitive on big data. State-of-the-art learning algorithms require computational complexity which depends at least linearly on the dimension p of the covariates, and often use heuristics that do not offer theoretical guarantees. We present scalable algorithms for learning high-dimensional LMMs with sublinear computational complexity dependence on p. Key to our approach are novel dual estimators which use only kernel functions of the data, and fast computational techniques based on the subsampled randomized Hadamard transform. We provide theoretical guarantees for our learning algorithms, demonstrating the robustness of parameter estimation. Finally, we complement the theory with experiments on large synthetic and real data. 

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4. SubgroupTE: Advancing Treatment Effect Estimation with Subgroup Identification

   https://doi.org/10.1145/3718097  

   Lee, Seungyeon; Liu, Ruoqi; Song, Wenyu; Li, Lang; Zhang, Ping (March 2025, ACM Transactions on Intelligent Systems and Technology)

   Precise estimation of treatment effects is crucial for accurately evaluating the intervention. While deep learning models have exhibited promising performance in learning counterfactual representations for treatment effect estimation (TEE), a major limitation in most of these models is that they often overlook the diversity of treatment effects across potential subgroups that have varying treatment effects and characteristics, treating the entire population as a homogeneous group. This limitation restricts the ability to precisely estimate treatment effects and provide targeted treatment recommendations. In this paper, we propose a novel treatment effect estimation model, named SubgroupTE, which incorporates subgroup identification in TEE. SubgroupTE identifies heterogeneous subgroups with different responses and more precisely estimates treatment effects by considering subgroup-specific treatment effects in the estimation process. In addition, we introduce an expectation–maximization (EM)-based training process that iteratively optimizes estimation and subgrouping networks to improve both estimation and subgroup identification. Comprehensive experiments on the synthetic and semi-synthetic datasets demonstrate the outstanding performance of SubgroupTE compared to the existing works for treatment effect estimation and subgrouping models. Additionally, a real-world study demonstrates the capabilities of SubgroupTE in enhancing targeted treatment recommendations for patients with opioid use disorder (OUD) by incorporating subgroup identification with treatment effect estimation. 

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5. On the Robustness of Large Multimodal Models Against Image Adversarial Attacks

   Cui, Xuanming; Aparcedo, Alejandro; Jang, Young Kyun; Lim, Ser-Nam (June 2024, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))

   Recent advances in instruction tuning have led to the development of State-of-the-Art Large Multimodal Models (LMMs). Given the novelty of these models the impact of visual adversarial attacks on LMMs has not been thoroughly examined. We conduct a comprehensive study of the robustness of various LMMs against different adversarial attacks evaluated across tasks including image classification image captioning and Visual Question Answer (VQA). We find that in general LMMs are not robust to visual adversarial inputs. However our findings suggest that context provided to the model via prompts--such as questions in a QA pair--helps to mitigate the effects of visual adversarial inputs. Notably the LMMs evaluated demonstrated remarkable resilience to such attacks on the ScienceQA task with only an 8.10% drop in performance compared to their visual counterparts which dropped 99.73%. We also propose a new approach to real-world image classification which we term query decomposition. By incorporating existence queries into our input prompt we observe diminished attack effectiveness and improvements in image classification accuracy. This research highlights a previously under explored facet of LMM robustness and sets the stage for future work aimed at strengthening the resilience of multimodal systems in adversarial environments. 

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