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Journal articles on the topic 'Robust Representations'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

Yang, Shuo, Tianyu Guo, Yunhe Wang, and Chang Xu. "Adversarial Robustness through Disentangled Representations." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 3145–53. http://dx.doi.org/10.1609/aaai.v35i4.16424.

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Despite the remarkable empirical performance of deep learning models, their vulnerability to adversarial examples has been revealed in many studies. They are prone to make a susceptible prediction to the input with imperceptible adversarial perturbation. Although recent works have remarkably improved the model's robustness under the adversarial training strategy, an evident gap between the natural accuracy and adversarial robustness inevitably exists. In order to mitigate this problem, in this paper, we assume that the robust and non-robust representations are two basic ingredients entangled i

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Kuo, Yen-Ling. "Learning Representations for Robust Human-Robot Interaction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 20 (2024): 22673. http://dx.doi.org/10.1609/aaai.v38i20.30289.

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For robots to robustly and flexibly interact with humans, they need to acquire skills to use across scenarios. One way to enable the generalization of skills is to learn representations that are useful for downstream tasks. Learning a representation for interactions requires an understanding of what (e.g., objects) as well as how (e.g., actions, controls, and manners) to interact with. However, most existing language or visual representations mainly focus on objects. To enable robust human-robot interactions, we need a representation that is not just grounded at the object level but to reason

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Iddianozie, Chidubem, and Gavin McArdle. "Towards Robust Representations of Spatial Networks Using Graph Neural Networks." Applied Sciences 11, no. 15 (2021): 6918. http://dx.doi.org/10.3390/app11156918.

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The effectiveness of a machine learning model is impacted by the data representation used. Consequently, it is crucial to investigate robust representations for efficient machine learning methods. In this paper, we explore the link between data representations and model performance for inference tasks on spatial networks. We argue that representations which explicitly encode the relations between spatial entities would improve model performance. Specifically, we consider homogeneous and heterogeneous representations of spatial networks. We recognise that the expressive nature of the heterogene

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Chateauneuf, Alain, Xiangyu Qu, Caroline Ventura та Vassili Vergopoulos. "Robust α-maxmin representations". Journal of Mathematical Economics 114 (жовтень 2024): 103045. http://dx.doi.org/10.1016/j.jmateco.2024.103045.

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Vu, Hung, Tu Dinh Nguyen, Trung Le, Wei Luo, and Dinh Phung. "Robust Anomaly Detection in Videos Using Multilevel Representations." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5216–23. http://dx.doi.org/10.1609/aaai.v33i01.33015216.

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Detecting anomalies in surveillance videos has long been an important but unsolved problem. In particular, many existing solutions are overly sensitive to (often ephemeral) visual artifacts in the raw video data, resulting in false positives and fragmented detection regions. To overcome such sensitivity and to capture true anomalies with semantic significance, one natural idea is to seek validation from abstract representations of the videos. This paper introduces a framework of robust anomaly detection using multilevel representations of both intensity and motion data. The framework consists

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Ho, Edward Kei Shiu, and Lai Wan Chan. "Analyzing Holistic Parsers: Implications for Robust Parsing and Systematicity." Neural Computation 13, no. 5 (2001): 1137–70. http://dx.doi.org/10.1162/08997660151134361.

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Holistic parsers offer a viable alternative to traditional algorithmic parsers. They have good generalization performance and are robust inherently. In a holistic parser, parsing is achieved by mapping the connectionist representation of the input sentence to the connectionist representation of the target parse tree directly. Little prior knowledge of the underlying parsing mechanism thus needs to be assumed. However, it also makes holistic parsing difficult to understand. In this article, an analysis is presented for studying the operations of the confluent pre-order parser (CPP). In the anal

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Yang, Qing, Jun Chen, and Najla Al-Nabhan. "Data representation using robust nonnegative matrix factorization for edge computing." Mathematical Biosciences and Engineering 19, no. 2 (2021): 2147–78. http://dx.doi.org/10.3934/mbe.2022100.

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<abstract> <p>As a popular data representation technique, Nonnegative matrix factorization (NMF) has been widely applied in edge computing, information retrieval and pattern recognition. Although it can learn parts-based data representations, existing NMF-based algorithms fail to integrate local and global structures of data to steer matrix factorization. Meanwhile, semi-supervised ones ignore the important role of instances from different classes in learning the representation. To solve such an issue, we propose a novel semi-supervised NMF approach via joint graph regularization a

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Parlett, Beresford N., and Inderjit S. Dhillon. "Relatively robust representations of symmetric tridiagonals." Linear Algebra and its Applications 309, no. 1-3 (2000): 121–51. http://dx.doi.org/10.1016/s0024-3795(99)00262-1.

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Medina, Josep R., and Carlos R. Sanchez‐Carratala. "Robust AR Representations of Ocean Spectra." Journal of Engineering Mechanics 117, no. 12 (1991): 2926–30. http://dx.doi.org/10.1061/(asce)0733-9399(1991)117:12(2926).

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Higashi, Masatake, Fuyuki Torihara, Nobuhiro Takeuchi, Toshio Sata, Tsuyoshi Saitoh, and Mamoru Hosaka. "Robust algorithms for face-based representations." Computer-Aided Design 29, no. 2 (1997): 135–46. http://dx.doi.org/10.1016/s0010-4485(96)00042-5.

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Rostami, Mohammad. "Internal Robust Representations for Domain Generalization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 15451. http://dx.doi.org/10.1609/aaai.v37i13.26818.

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Model generalization under distributional changes remains a significant challenge for machine learning. We present consolidating the internal representation of the training data in a model as a strategy of improving model generalization.

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Shi, Weipeng, Wenhu Qin, and Allshine Chen. "Towards Robust Semantic Segmentation of Land Covers in Foggy Conditions." Remote Sensing 14, no. 18 (2022): 4551. http://dx.doi.org/10.3390/rs14184551.

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When conducting land cover classification, it is inevitable to encounter foggy conditions, which degrades the performance by a large margin. Robustness may be reduced by a number of factors, such as aerial images of low quality and ineffective fusion of multimodal representations. Hence, it is crucial to establish a reliable framework that can robustly understand remote sensing image scenes. Based on multimodal fusion and attention mechanisms, we leverage HRNet to extract underlying features, followed by the Spectral and Spatial Representation Learning Module to extract spectral-spatial repres

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Rezayi, Saed. "Learning Better Representations Using Auxiliary Knowledge." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16133–34. http://dx.doi.org/10.1609/aaai.v37i13.26927.

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Representation Learning is the core of Machine Learning and Artificial Intelligence as it summarizes input data points into low dimensional vectors. This low dimensional vectors should be accurate portrayals of the input data, thus it is crucial to find the most effective and robust representation possible for given input as the performance of the ML task is dependent on the resulting representations. In this summary, we discuss an approach to augment representation learning which relies on external knowledge. We briefly describe the shortcoming of the existing techniques and describe how an a

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Klatzky, Roberta L., and Nicholas A. Giudice. "The planar mosaic fails to account for spatially directed action." Behavioral and Brain Sciences 36, no. 5 (2013): 554–55. http://dx.doi.org/10.1017/s0140525x13000435.

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AbstractHumans' spatial representations enable navigation and reaching to targets above the ground plane, even without direct perceptual support. Such abilities are inconsistent with an impoverished representation of the third dimension. Features that differentiate humans from most terrestrial animals, including raised eye height and arms dedicated to manipulation rather than locomotion, have led to robust metric representations of volumetric space.

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Benda, Natalie C., and Ann M. Bisantz. "Prototypical Work Situations: A Robust, Flexible Means for Representing Activity in a Work Domain." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (2019): 337–41. http://dx.doi.org/10.1177/1071181319631089.

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Representing the results is a key component in the analysis of cognitive work. Many structures have been developed for representing the results of Cognitive Work Analysis, but the representation of activity through “prototypical work situations” is less commonly utilized. Prototypical work situations, initially described by Rasmussen, convey summaries of actual activities that represent the key properties of work in a domain. This study illustrates the utility of prototypical work situation representations through a demonstrative case example. Specifically, representations of prototypical work

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Mehrmann, V., and P. Van Dooren. "Optimal robustness of passive discrete-time systems." IMA Journal of Mathematical Control and Information 37, no. 4 (2020): 1248–69. http://dx.doi.org/10.1093/imamci/dnaa013.

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Abstract We study different representations of a given rational transfer function that represents a passive (or positive real) discrete-time system. When the system is subject to perturbations, passivity or stability may be lost. To make the system robust, we use the freedom in the representation to characterize and construct optimally robust representations in the sense that the distance to non-passivity is maximized with respect to an appropriate matrix norm. We link this construction to the solution set of certain linear matrix inequalities defining passivity of the transfer function. We pr

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Giese, Martin A. "Mirror representations innate versus determined by experience: A viewpoint from learning theory." Behavioral and Brain Sciences 37, no. 2 (2014): 201–2. http://dx.doi.org/10.1017/s0140525x13002306.

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AbstractFrom the viewpoint of pattern recognition and computational learning, mirror neurons form an interesting multimodal representation that links action perception and planning. While it seems unlikely that all details of such representations are specified by the genetic code, robust learning of such complex representations likely requires an appropriate interplay between plasticity, generalization, and anatomical constraints of the underlying neural architecture.

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Liu, Qiyuan, Qi Zhou, Rui Yang, and Jie Wang. "Robust Representation Learning by Clustering with Bisimulation Metrics for Visual Reinforcement Learning with Distractions." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8843–51. http://dx.doi.org/10.1609/aaai.v37i7.26063.

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Recent work has shown that representation learning plays a critical role in sample-efficient reinforcement learning (RL) from pixels. Unfortunately, in real-world scenarios, representation learning is usually fragile to task-irrelevant distractions such as variations in background or viewpoint. To tackle this problem, we propose a novel clustering-based approach, namely Clustering with Bisimulation Metrics (CBM), which learns robust representations by grouping visual observations in the latent space. Specifically, CBM alternates between two steps: (1) grouping observations by measuring their b

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Kikumoto, Atsushi, and Ulrich Mayr. "Conjunctive representations that integrate stimuli, responses, and rules are critical for action selection." Proceedings of the National Academy of Sciences 117, no. 19 (2020): 10603–8. http://dx.doi.org/10.1073/pnas.1922166117.

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People can use abstract rules to flexibly configure and select actions for specific situations, yet how exactly rules shape actions toward specific sensory and/or motor requirements remains unclear. Both research from animal models and human-level theories of action control point to the role of highly integrated, conjunctive representations, sometimes referred to as event files. These representations are thought to combine rules with other, goal-relevant sensory and motor features in a nonlinear manner and represent a necessary condition for action selection. However, so far, no methods exist

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Zhang, Binghui, Sayedeh Leila Noorbakhsh, Yun Dong, Yuan Hong, and Binghui Wang. "Learning Robust and Privacy-Preserving Representations via Information Theory." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 21 (2025): 22363–71. https://doi.org/10.1609/aaai.v39i21.34392.

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Machine learning models are vulnerable to both security attacks (e.g., adversarial examples) and privacy attacks (e.g., private attribute inference). We take the first step to mitigate both the security and privacy attacks, and maintain task utility as well. Particularly, we propose an information-theoretic framework to achieve the goals through the lens of representation learning, i.e., learning representations that are robust to both adversarial examples and attribute inference adversaries. We also derive novel theoretical results under our framework, e.g., the inherent trade-off between adv

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Yue, Zhihan, Yujing Wang, Juanyong Duan, et al. "TS2Vec: Towards Universal Representation of Time Series." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 8 (2022): 8980–87. http://dx.doi.org/10.1609/aaai.v36i8.20881.

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This paper presents TS2Vec, a universal framework for learning representations of time series in an arbitrary semantic level. Unlike existing methods, TS2Vec performs contrastive learning in a hierarchical way over augmented context views, which enables a robust contextual representation for each timestamp. Furthermore, to obtain the representation of an arbitrary sub-sequence in the time series, we can apply a simple aggregation over the representations of corresponding timestamps. We conduct extensive experiments on time series classification tasks to evaluate the quality of time series repr

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James, M. R., M. C. Smith, and G. Vinnicombe. "Gap Metrics, Representations, and Nonlinear Robust Stability." SIAM Journal on Control and Optimization 43, no. 5 (2005): 1535–82. http://dx.doi.org/10.1137/s0363012901393067.

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23

Martins, P., P. Carvalho, and C. Gatta. "Context-aware features and robust image representations." Journal of Visual Communication and Image Representation 25, no. 2 (2014): 339–48. http://dx.doi.org/10.1016/j.jvcir.2013.10.006.

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24

Skočaj, Danijel, Aleš Leonardis, and Horst Bischof. "Weighted and robust learning of subspace representations." Pattern Recognition 40, no. 5 (2007): 1556–69. http://dx.doi.org/10.1016/j.patcog.2006.09.019.

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25

Beattie, Christopher A., Volker Mehrmann, and Paul Van Dooren. "Robust port-Hamiltonian representations of passive systems." Automatica 100 (February 2019): 182–86. http://dx.doi.org/10.1016/j.automatica.2018.11.013.

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Hu, Chun-Yi, Nicholas M. Patrikalakis, and Xiuzi Ye. "Robust interval solid modelling Part I: representations." Computer-Aided Design 28, no. 10 (1996): 807–17. http://dx.doi.org/10.1016/0010-4485(96)00013-9.

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Skočaj, Danijel, and Aleš Leonardis. "Incremental and robust learning of subspace representations." Image and Vision Computing 26, no. 1 (2008): 27–38. http://dx.doi.org/10.1016/j.imavis.2005.07.028.

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Roschewitz, Mélanie, Fabio De Sousa Ribeiro, Tian Xia, Galvin Khara, and Ben Glocker. "Robust image representations with counterfactual contrastive learning." Medical Image Analysis 105 (October 2025): 103668. https://doi.org/10.1016/j.media.2025.103668.

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Kountzakis, Christos E., and Damiano Rossello. "Risk Measures’ Duality on Ordered Linear Spaces." Mathematics 12, no. 8 (2024): 1165. http://dx.doi.org/10.3390/math12081165.

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The aim of this paper is to provide a dual representation of convex and coherent risk measures in partially ordered linear spaces with respect to the algebraic dual space. An algebraic robust representation is deduced by weak separation of convex sets by functionals, which are assumed to be only linear; thus, our framework does not require any topological structure of the underlying spaces, and our robust representations are found without any continuity requirement for the risk measures. We also use such extensions to the representation of acceptability indices.

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Silva, Samuel Henrique, Arun Das, Adel Aladdini, and Peyman Najafirad. "Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification on Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 968–79. http://dx.doi.org/10.1609/icwsm.v16i1.19350.

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Advances in Artificial Intelligence (AI) have made it possible to automate human-level visual search and perception tasks on the massive sets of image data shared on social media on a daily basis. However, AI-based automated filters are highly susceptible to deliberate image attacks that can lead to content misclassification of cyberbulling, child sexual abuse material (CSAM), adult content, and deepfakes. One of the most effective methods to defend against such disturbances is adversarial training, but this comes at the cost of generalization for unseen attacks and transferability across mode

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Wahutu, J. Siguru. "‘In the case of Africa in general, there is a tendency to exaggerate’: representing mass atrocity in Africa." Media, Culture & Society 39, no. 6 (2017): 919–29. http://dx.doi.org/10.1177/0163443717692737.

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Based on an analysis of print media and journalists’ interviews, this article examines the representation of atrocity and mass violence in Africa. It specifically focuses on the atrocities in Darfur and Rwanda and compares African and Western coverage of them. It argues that since representations (just as the knowledge that anchors them) are highly dependent on one’s social location, it is necessary to understand multiple representations of the same atrocity. Although the literature on representation of Africa has been critical of Western representations of Africa, this article argues that inc

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Gao, Hang, Jiangmeng Li, Wenwen Qiang, et al. "Robust Causal Graph Representation Learning against Confounding Effects." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 7624–32. http://dx.doi.org/10.1609/aaai.v37i6.25925.

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The prevailing graph neural network models have achieved significant progress in graph representation learning. However, in this paper, we uncover an ever-overlooked phenomenon: the pre-trained graph representation learning model tested with full graphs underperforms the model tested with well-pruned graphs. This observation reveals that there exist confounders in graphs, which may interfere with the model learning semantic information, and current graph representation learning methods have not eliminated their influence. To tackle this issue, we propose Robust Causal Graph Representation Lear

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Espinosa Zarlenga, Mateo, Pietro Barbiero, Zohreh Shams, et al. "Towards Robust Metrics for Concept Representation Evaluation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 11791–99. http://dx.doi.org/10.1609/aaai.v37i10.26392.

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Recent work on interpretability has focused on concept-based explanations, where deep learning models are explained in terms of high-level units of information, referred to as concepts. Concept learning models, however, have been shown to be prone to encoding impurities in their representations, failing to fully capture meaningful features of their inputs. While concept learning lacks metrics to measure such phenomena, the field of disentanglement learning has explored the related notion of underlying factors of variation in the data, with plenty of metrics to measure the purity of such factor

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Chen, Feiqiong, Guopeng Li, Shuaihui Wang, and Zhisong Pan. "Multiview Clustering via Robust Neighboring Constraint Nonnegative Matrix Factorization." Mathematical Problems in Engineering 2019 (November 23, 2019): 1–10. http://dx.doi.org/10.1155/2019/6084382.

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Many real-world datasets are described by multiple views, which can provide complementary information to each other. Synthesizing multiview features for data representation can lead to more comprehensive data description for clustering task. However, it is often difficult to preserve the locally real structure in each view and reconcile the noises and outliers among views. In this paper, instead of seeking for the common representation among views, a novel robust neighboring constraint nonnegative matrix factorization (rNNMF) is proposed to learn the neighbor structure representation in each v

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Bowman, Sean, Kostas Daniilidis, and George Pappas. "Robust Object-Level Semantic Visual SLAM Using Semantic Keypoints." Field Robotics 2, no. 1 (2022): 513–24. http://dx.doi.org/10.55417/fr.2022018.

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Simultaneous Localization and Mapping (SLAM) has traditionally relied on representing the environment as low-level, geometric features, such as points, lines, and planes. Recent advances in object recognition capabilities, however, as well as demand for environment representations that facilitate higher-level autonomy, have motivated an object-based Semantic SLAM. We present a Semantic SLAM algorithm that directly incorporates a sparse representation of objects into a factor-graph SLAM optimization, resulting in a system that is efficient, robust to varying object shapes and environments, and

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Nguyễn, Tuấn, Nguyen Hai Hao, Dang Le Dinh Trang, Nguyen Van Tuan, and Cao Van Loi. "Robust anomaly detection methods for contamination network data." Journal of Military Science and Technology, no. 79 (May 19, 2022): 41–51. http://dx.doi.org/10.54939/1859-1043.j.mst.79.2022.41-51.

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Recently, latent representation models, such as Shrink Autoencoder (SAE), have been demonstrated as robust feature representations for one-class learning-based network anomaly detection. In these studies, benchmark network datasets that are processed in laboratory environments to make them completely clean are often employed for constructing and evaluating such models. In real-world scenarios, however, we can not guarantee 100% to collect pure normal data for constructing latent representation models. Therefore, this work aims to investigate the characteristics of the latent representation of

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Cook, Svetlana V., and Kira Gor. "Lexical access in L2." Mental Lexicon 10, no. 2 (2015): 247–70. http://dx.doi.org/10.1075/ml.10.2.04coo.

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Previous research on phonological priming in a Lexical Decision Task (LDT) has demonstrated that second language (L2) learners do not show inhibition typical for native (L1) speakers that results from lexical competition, but rather a reversed effect – facilitation (Gor, Cook, & Jackson, 2010). The present study investigates the source of the reversed priming effect and addresses two possible causes: a deficit in lexical representations and a processing constraint. Twenty-three advanced learners of Russian participated in two experiments. The monolingual Russian LDT task with priming addre

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Choi, Jaewoong, Daeha Kim, and Byung Cheol Song. "Style-Guided and Disentangled Representation for Robust Image-to-Image Translation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 463–71. http://dx.doi.org/10.1609/aaai.v36i1.19924.

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Recently, various image-to-image translation (I2I) methods have improved mode diversity and visual quality in terms of neural networks or regularization terms. However, conventional I2I methods relies on a static decision boundary and the encoded representations in those methods are entangled with each other, so they often face with ‘mode collapse’ phenomenon. To mitigate mode collapse, 1) we design a so-called style-guided discriminator that guides an input image to the target image style based on the strategy of flexible decision boundary. 2) Also, we make the encoded representations include

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Apostolico, A., and A. Fraenkel. "Robust transmission of unbounded strings using Fibonacci representations." IEEE Transactions on Information Theory 33, no. 2 (1987): 238–45. http://dx.doi.org/10.1109/tit.1987.1057284.

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Tong, Frank, and Ken Nakayama. "Robust representations for faces: Evidence from visual search." Journal of Experimental Psychology: Human Perception and Performance 25, no. 4 (1999): 1016–35. http://dx.doi.org/10.1037/0096-1523.25.4.1016.

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Mancini, Massimiliano, Samuel Rota Bulo, Elisa Ricci, and Barbara Caputo. "Learning Deep NBNN Representations for Robust Place Categorization." IEEE Robotics and Automation Letters 2, no. 3 (2017): 1794–801. http://dx.doi.org/10.1109/lra.2017.2705282.

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Hu, Xing, Shiqiang Hu, Jinhua Xie, and Shiyou Zheng. "Robust and efficient anomaly detection using heterogeneous representations." Journal of Electronic Imaging 24, no. 3 (2015): 033021. http://dx.doi.org/10.1117/1.jei.24.3.033021.

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Sheng, Bin, Bowen Liu, Ping Li, Hongbo Fu, Lizhuang Ma, and Enhua Wu. "Accelerated robust Boolean operations based on hybrid representations." Computer Aided Geometric Design 62 (May 2018): 133–53. http://dx.doi.org/10.1016/j.cagd.2018.03.021.

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Wong, Alexander, and Jeff Orchard. "Robust Multimodal Registration Using Local Phase-Coherence Representations." Journal of Signal Processing Systems 54, no. 1-3 (2008): 89–100. http://dx.doi.org/10.1007/s11265-008-0202-x.

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Liu, J., B. C. Vemuri, and J. L. Marroquin. "Local frequency representations for robust multimodal image registration." IEEE Transactions on Medical Imaging 21, no. 5 (2002): 462–69. http://dx.doi.org/10.1109/tmi.2002.1009382.

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Schwarz, Baruch. "Why Can Intermediate Abstractions Help Acquire Robust Representations?" Interactive Learning Environments 5, no. 1 (1998): 181–203. http://dx.doi.org/10.1080/1049482980050112.

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Li, Siyuan, Xun Wang, Rongchang Zuo, et al. "Robust Visual Imitation Learning with Inverse Dynamics Representations." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13609–18. http://dx.doi.org/10.1609/aaai.v38i12.29265.

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Imitation learning (IL) has achieved considerable success in solving complex sequential decision-making problems. However, current IL methods mainly assume that the environment for learning policies is the same as the environment for collecting expert datasets. Therefore, these methods may fail to work when there are slight differences between the learning and expert environments, especially for challenging problems with high-dimensional image observations. However, in real-world scenarios, it is rare to have the chance to collect expert trajectories precisely in the target learning environmen

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Dai, Wengui, and Yujun Wang. "Web Semantic-Based Robust Graph Contrastive Learning for Recommendation via Invariant Learning." International Journal on Semantic Web and Information Systems 20, no. 1 (2024): 1–15. http://dx.doi.org/10.4018/ijswis.337962.

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The use of contrastive learning (CL) in recommendation has advanced significantly. Recently, some works use perturbations in the embedding space to obtain enhanced views of nodes. This makes the representation distribution of nodes more even and then improve recommendation effectiveness. In this article, the authors provide an explanation on the role of added noises in the embedding space from the perspective of invariant learning and feature selection. Guided by this thinking, the authors devise a more reasonable method for generating random noises and put forward web semantic based robust gr

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Hu, Dou, Lingwei Wei, Wei Zhou, and Songlin Hu. "An Information-theoretic Multi-task Representation Learning Framework for Natural Language Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 16 (2025): 17276–86. https://doi.org/10.1609/aaai.v39i16.33899.

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This paper proposes a new principled multi-task representation learning framework (InfoMTL) to extract noise-invariant sufficient representations for all tasks. It ensures sufficiency of shared representations for all tasks and mitigates the negative effect of redundant features, which can enhance language understanding of pre-trained language models (PLMs) under the multi-task paradigm. Firstly, a shared information maximization principle is proposed to learn more sufficient shared representations for all target tasks. It can avoid the insufficiency issue arising from representation compressi

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Wang, Leilei, Si Shi, Fei Ma, Fei Richard Yu, Pengteng Li, and Ying Tiffany He. "Subgraph Invariant Learning Towards Large-Scale Graph Node Classification." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21144–52. https://doi.org/10.1609/aaai.v39i20.35412.

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Graph Neural Networks (GNNs) have shown efficacy in graph node classification, but face computational challenges on large-scale graphs. Although existing graph reduction methods address these issues, they still require high computational resources and fail to prioritize robust performance on out-of-distribution data. To tackle these challenges, we introduce the subgraph invariant learning paradigm, inspired by the small-world phenomenon. This approach enables models trained on specific subgraphs to generalize across diverse subgraphs, reducing computational demands, and enhancing scalability.

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