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1. Recurrent Networks for Guided Multi-Attention Classification

   Dai, X; Kong, X; Guo, T; Lee, J; Liu, X; Moore, C (January 2020, The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining)

   null (Ed.)

   Attention-based image classification has gained increasing popularity in recent years. State-of-the-art methods for attention-based classification typically require a large training set and operate under the assumption that the label of an image depends solely on a single object (i.e., region of interest) in the image. However, in many real-world applications (e.g., medical imaging), it is very expensive to collect a large training set. Moreover, the label of each image is usually determined jointly by multiple regions of interest (ROIs). Fortunately, for such applications, it is often possible to collect the locations of the ROIs in each training image. In this paper, we study the problem of guided multi-attention classification, the goal of which is to achieve high accuracy under the dual constraints of (1) small sample size, and (2) multiple ROIs for each image. We propose a model, called Guided Attention Recurrent Network (GARN), for multi-attention classification. Different from existing attention-based methods, GARN utilizes guidance information regarding multiple ROIs thus allowing it to work well even when sample size is small. Empirical studies on three different visual tasks show that our guided attention approach can effectively boost model performance for multi-attention image classification. 

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2. A Two-Stage Classification for Dealing with Unseen Clusters in the Testing Data

   https://doi.org/10.6339/24-JDS1140  

   Lee, Jung Wun; Harel, Ofer (January 2024, Journal of Data Science)

   Classification is an important statistical tool that has increased its importance since the emergence of the data science revolution. However, a training data set that does not capture all underlying population subgroups (or clusters) will result in biased estimates or misclassification. In this paper, we introduce a statistical and computational solution to a possible bias in classification when implemented on estimated population clusters. An unseen-cluster problem denotes the case in which the training data does not contain all underlying clusters in the population. Such a scenario may occur due to various reasons, such as sampling errors, selection bias, or emerging and disappearing population clusters. Once an unseen-cluster problem occurs, a testing observation will be misclassified because a classification rule based on the sample cannot capture a cluster not observed in the training data (sample). To overcome such issues, we suggest a two-stage classification method to ameliorate the unseen-cluster problem in classification. We suggest a test to identify the unseen-cluster problem and demonstrate the performance of the two-stage tailored classifier using simulations and a public data example. 

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3. Fair and Robust Classification Under Sample Selection Bias

   https://doi.org/10.1145/3459637.3482104  

   Du, Wei; Wu, Xintao (October 2021, 30th ACM International Conference on Information & Knowledge Management)

   To address the sample selection bias between the training and test data, previous research works focus on reweighing biased training data to match the test data and then building classification models on there weighed raining data. However, how to achieve fairness in the built classification models is under-explored. In this paper, we propose a framework for robust and fair learning under sample selection bias. Our framework adopts there weighing estimation approach for bias correction and the minimax robust estimation approach for achieving robustness on prediction accuracy. Moreover, during the minimax optimization, the fairness is achieved under the worst case, which guarantees the model’s fairness on test data. We further develop two algorithms to handle sample selection bias when test data is both available and unavailable. 

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4. Active learning to classify macromolecular structures in situ for less supervision in cryo-electron tomography

   https://doi.org/10.1093/bioinformatics/btab123  

   Du, Xuefeng; Wang, Haohan; Zhu, Zhenxi; Zeng, Xiangrui; Chang, Yi-Wei; Zhang, Jing; Xing, Eric; Xu, Min (February 2021, Bioinformatics)

   Xu, Jinbo (Ed.)

   Abstract Motivation Cryo-Electron Tomography (cryo-ET) is a 3D bioimaging tool that visualizes the structural and spatial organization of macromolecules at a near-native state in single cells, which has broad applications in life science. However, the systematic structural recognition and recovery of macromolecules captured by cryo-ET are difficult due to high structural complexity and imaging limits. Deep learning-based subtomogram classification has played critical roles for such tasks. As supervised approaches, however, their performance relies on sufficient and laborious annotation on a large training dataset. Results To alleviate this major labeling burden, we proposed a Hybrid Active Learning (HAL) framework for querying subtomograms for labeling from a large unlabeled subtomogram pool. Firstly, HAL adopts uncertainty sampling to select the subtomograms that have the most uncertain predictions. This strategy enforces the model to be aware of the inductive bias during classification and subtomogram selection, which satisfies the discriminativeness principle in AL literature. Moreover, to mitigate the sampling bias caused by such strategy, a discriminator is introduced to judge if a certain subtomogram is labeled or unlabeled and subsequently the model queries the subtomogram that have higher probabilities to be unlabeled. Such query strategy encourages to match the data distribution between the labeled and unlabeled subtomogram samples, which essentially encodes the representativeness criterion into the subtomogram selection process. Additionally, HAL introduces a subset sampling strategy to improve the diversity of the query set, so that the information overlap is decreased between the queried batches and the algorithmic efficiency is improved. Our experiments on subtomogram classification tasks using both simulated and real data demonstrate that we can achieve comparable testing performance (on average only 3% accuracy drop) by using less than 30% of the labeled subtomograms, which shows a very promising result for subtomogram classification task with limited labeling resources. Availability and implementation https://github.com/xulabs/aitom. Supplementary information Supplementary data are available at Bioinformatics online. 

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5. Chasing Fairness in Graphs: A GNN Architecture Perspective

   https://doi.org/10.1609/aaai.v38i19.30115  

   Jiang, Zhimeng; Han, Xiaotian; Fan, Chao; Liu, Zirui; Zou, Na; Mostafavi, Ali; Hu, Xia (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   There has been significant progress in improving the performance of graph neural networks (GNNs) through enhancements in graph data, model architecture design, and training strategies. For fairness in graphs, recent studies achieve fair representations and predictions through either graph data pre-processing (e.g., node feature masking, and topology rewiring) or fair training strategies (e.g., regularization, adversarial debiasing, and fair contrastive learning). How to achieve fairness in graphs from the model architecture perspective is less explored. More importantly, GNNs exhibit worse fairness performance compared to multilayer perception since their model architecture (i.e., neighbor aggregation) amplifies biases. To this end, we aim to achieve fairness via a new GNN architecture. We propose Fair Message Passing (FMP) designed within a unified optimization framework for GNNs. Notably, FMP explicitly renders sensitive attribute usage in forward propagation for node classification task using cross-entropy loss without data pre-processing. In FMP, the aggregation is first adopted to utilize neighbors' information and then the bias mitigation step explicitly pushes demographic group node presentation centers together.In this way, FMP scheme can aggregate useful information from neighbors and mitigate bias to achieve better fairness and prediction tradeoff performance. Experiments on node classification tasks demonstrate that the proposed FMP outperforms several baselines in terms of fairness and accuracy on three real-world datasets. The code is available at https://github.com/zhimengj0326/FMP. 

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