IJCAI 2024 Papers

Discontinuation rates vary by family planning method and across socio-economic contexts. Understanding these variations and their causes is paramount for developing and implementing policies aimed at curbing discontinuation rates. Randomized controlled trials (RCTs) are ideal for obtaining this information, but this design can be extremely expensive and logistically complex. The ongoing collection of comprehensive data sets, such as Demographic and Health Surveys (DHS data), when combined with machine learning methods, present an alternative and relatively cost-effective means of evidence gathering for policy development. Here, we use causal inference to estimate the effect of injectable contraceptive use on discontinuation over the 12-month period that follows its adoption. To that aim, we use retrospective observational data from seven sub-Saharan African countries captured by the DHS’ Contraceptive Calendar. We use machine learning methods to characterize data regions that share common covariate support. We find that the use of injectables increased the risk of discontinuation in four of the seven countries analyzed. Consistent with existing literature, we find that concerns with the side-effects of injectables appear to be the most frequent reason for discontinuation. However, these risks decreased after adjusting for socio-economic factors. As risk estimates may not apply uniformly within populations, we characterized the sub-populations for robust estimations by their geographical region, level of unmet needs, marital status, level of education, and age of first sex.

While traditional search systems have mostly been satisfactorily relying on lexical based sparse retrievers such as BM25, recent research advances in neural models, the current day large language models (LLMs) hold good promise for practical search applications as well. In this work, we discuss a collaboration between IBM and National Library of Australia to upgrade an existing search application (referred to as NLA) over terabytes of Australian Web Archive data and serving thousands of daily users. We posit and demonstrate both empirically and through qualitative user studies that LLMs and neural models can indeed provide good gains, when combined effectively with traditional search. We believe this demonstration will show the unique challenges associated with real world practical deployments and also offer valuable insights into how to effectively upgrade legacy search applications in the era of LLMs.

SHAP scores represent one of the most widely used methods of explainability by feature attribution, as illustrated by the explainable AI tool SHAP. A number of recent works studied the computational complexity of the exact computation of SHAP scores, covering a comprehensive range of families of classifiers. This paper refines some of the existing complexity claims, including families of classifiers for which the computation of SHAP scores is computationally hard and those for which there exist polynomial-time algorithms.

Graph Neural Network (GNN) is powerful in graph embedding learning, but its performance has been shown to be heavily degraded under adversarial attacks. Deep graph structure learning (GSL) is proposed to defend attack by jointly learning graph structure and graph embedding, typically in node classification task. Label supervision is expensive in real-world applications, and thus unsupervised GSL is more challenging and still remains less studied. To fulfill this gap, this paper proposes a new unsupervised GSL method, i.e., unsupervised property GNN (UPGNN). UPGNN first refines graph structure by exploring properties of low rank, sparsity, feature smoothness. UPGNN employs graph mutual information loss to learn graph embedding by maximizing its correlation with refined graph. The proposed UPGNN learns graph structure and embedding without label supervision, and thus can be applied various downstream tasks. We further propose Accelerated UPGNN (AUPGNN) to reduce computational complexity, providing a efficient alternative to UPGNN. Our extensive experiments on node classification and clustering demonstrate the effectiveness of the proposed method over the state-of-the-arts especially under heavy perturbation.

Reconstruction-based methods have been commonly used for unsupervised anomaly detection, in which a normal image is reconstructed and compared with the given test image to detect and locate anomalies. Recently, diffusion models have shown promising applications for anomaly detection due to their powerful generative ability. However, these models lack strict mathematical support for normal image reconstruction and unexpectedly suffer from low reconstruction quality. To address these issues, this paper proposes a novel and highly-interpretable method named Masked Diffusion Posterior Sampling (MDPS). In MDPS, the problem of normal image reconstruction is mathematically modeled as multiple diffusion posterior sampling for normal images based on the devised masked noisy observation model and the diffusion-based normal image prior under Bayesian framework. Using a metric designed from pixel-level and perceptual-level perspectives, MDPS can effectively compute the difference map between each normal posterior sample and the given test image. Anomaly scores are obtained by averaging all difference maps for multiple posterior samples. Exhaustive experiments on MVTec and BTAD datasets demonstrate that MDPS can achieve state-of-the-art performance in normal image reconstruction quality as well as anomaly detection and localization.

In critical care settings, where precise and timely interventions are crucial for health outcomes, evaluating disparities in patient outcomes is important. Current approaches often fall short in comprehensively understanding and evaluating the impact of respiratory support interventions on individuals affected by social determinants of health. Attributes such as gender, race, and age are commonly assessed and essential, but provide only a partial view of the complexities faced by diverse populations. In this study, we focus on two clinically motivated tasks: prolonged mechanical ventilation and successful weaning. We also perform fairness audits on the models' predictions across demographic groups and social determinants of health to better understand the health inequities in respiratory interventions in the intensive care unit. We also release a temporal benchmark dataset, verified by clinical experts, to enable benchmarking of clinical respiratory intervention tasks.

Machine unlearning provides users with the right to remove their privacy data from a well-trained model. Existing approaches of machine unlearning mainly focus on exploring data removing within supervised learning (SL) tasks. However, weakly supervised learning (WSL) is more applicable to real-world scenarios since collecting WSL data is less laborious than collecting fully supervised data. In this paper, we first propose a machine unlearning approach for WSL by updating the model parameters. Motivated by the uniform distributions of untrained model predictions, we derive a formulated target to force the model's predictions of removed data to be indistinguishable. This encourages the model to forget its ability to recognize features of data slated for unlearning. Moreover, we employ formulated targets to transform the classification unlearning into the convex regression, which can significantly reduce computational cost and avoid extra information storage during the training process. Additionally, we discuss how to design a target to ensure the models' predictions of removed data being indistinguishable in different learning scenarios, e.g., SL or WSL. As the flexibility in formulating targets, the proposed approach effectively deals with the WSL problem while still excels in SL models. Empirical studies show the superiority of the proposed approach.

In recent years, Federated Learning (FL) has garnered significant attention as a distributed machine learning paradigm. To facilitate the implementation of the "right to be forgotten," the concept of federated machine unlearning (FMU) has also emerged. However, current FMU approaches often involve additional time-consuming steps and may not offer comprehensive unlearning capabilities, which renders them less practical in real FL scenarios. In this paper, we introduce FedAU, an innovative and efficient FMU framework aimed at overcoming these limitations. Specifically, FedAU incorporates a lightweight auxiliary unlearning module into the learning process and employs a straightforward linear operation to facilitate unlearning. This approach eliminates the requirement for extra time-consuming steps, rendering it well-suited for FL. Furthermore, FedAU exhibits remarkable versatility. It not only enables multiple clients to carry out unlearning tasks concurrently but also supports unlearning at various levels of granularity, including individual data samples, specific classes, and even at the client level. We conducted extensive experiments on MNIST, CIFAR10, and CIFAR100 datasets to evaluate the performance of FedAU. The results demonstrate that FedAU effectively achieves the desired unlearning effect while maintaining model accuracy.

Although multi-view unsupervised feature selection (MUFS) is an effective technology for reducing dimensionality in machine learning, existing methods cannot directly deal with incomplete multi-view data where some samples are missing in certain views. These methods should first apply predetermined values to impute missing data, then perform feature selection on the complete dataset. Separating imputation and feature selection processes fails to capitalize on the potential synergy where local structural information gleaned from feature selection could guide the imputation, thereby improving the feature selection performance in turn. Additionally, previous methods only focus on leveraging samples' local structure information, while ignoring the intrinsic locality of the feature space. To tackle these problems, a novel MUFS method, called UNified view Imputation and Feature selectIon lEaRning (UNIFIER), is proposed. UNIFIER explores the local structure of multi-view data by adaptively learning similarity-induced graphs from both the sample and feature spaces. Then, UNIFIER dynamically recovers the missing views, guided by the sample and feature similarity graphs during the feature selection procedure. Furthermore, the half-quadratic minimization technique is used to automatically weight different instances, alleviating the impact of outliers and unreliable restored data. Comprehensive experimental results demonstrate that UNIFIER outperforms other state-of-the-art methods.

Recently, unsupervised salient object detection (USOD) has gained increasing attention due to its annotation-free nature. However, current methods mainly focus on specific tasks such as RGB and RGB-D, neglecting the potential for task migration. In this paper, we propose a unified USOD framework for generic USOD tasks. Firstly, we propose a Progressive Curriculum Learning-based Saliency Distilling (PCL-SD) mechanism to extract saliency cues from a pre-trained deep network. This mechanism starts with easy samples and progressively moves towards harder ones, to avoid initial interference caused by hard samples. Afterwards, the obtained saliency cues are utilized to train a saliency detector, and we employ a Self-rectify Pseudo-label Refinement (SPR) mechanism to improve the quality of pseudo-labels. Finally, an adapter-tuning method is devised to transfer the acquired saliency knowledge, leveraging shared knowledge to attain superior transferring performance on the target tasks. Extensive experiments on five representative SOD tasks confirm the effectiveness and feasibility of our proposed method. Code and supplement materials are available at https://github.com/I2-Multimedia-Lab/A2S-v3.

Most existing RGB-T tracking networks extract modality features in a separate manner, which lacks interaction and mutual guidance between modalities. This limits the network's ability to adapt to the diverse dual-modality appearances of targets and the dynamic relationships between the modalities. Additionally, the three-stage fusion tracking paradigm followed by these networks significantly restricts the tracking speed. To overcome these problems, we propose a unified single-stage Transformer RGB-T tracking network, namely USTrack, which unifies the above three stages into a single ViT (Vision Transformer) backbone through joint feature extraction, fusion and relation modeling. With this structure, the network can not only extract the fusion features of templates and search regions under the interaction of modalities, but also significantly improve tracking speed through the single-stage fusion tracking paradigm. Furthermore, we introduce a novel feature selection mechanism based on modality reliability to mitigate the influence of invalid modalities for final prediction. Extensive experiments on three mainstream RGB-T tracking benchmarks show that our method achieves the new state-of-the-art while achieving the fastest tracking speed of 84.2FPS. Code is available at https://github.com/xiajianqiang/USTrack.

Face Recognition (FR) systems can suffer from physical (i.e., print photo) and digital (i.e., DeepFake) attacks. However, previous related work rarely considers both situations at the same time. This implies the deployment of multiple models and thus more computational burden. The main reasons for this lack of an integrated model are caused by two factors: (1) The lack of a dataset including both physical and digital attacks which the same ID covers the real face and all attack types; (2) Given the large intra-class variance between these two attacks, it is difficult to learn a compact feature space to detect both attacks simultaneously. To address these issues, we collect a Unified physical-digital Attack dataset, called UniAttackData. The dataset consists of 1,800 participations of 2 and 12 physical and digital attacks, respectively, resulting in a total of 28,706 videos. Then, we propose a Unified Attack Detection framework based on Vision-Language Models (VLMs), namely UniAttackDetection, which includes three main modules: the Teacher-Student Prompts (TSP) module, focused on acquiring unified and specific knowledge respectively; the Unified Knowledge Mining (UKM) module, designed to capture a comprehensive feature space; and the Sample-Level Prompt Interaction (SLPI) module, aimed at grasping sample-level semantics. These three modules seamlessly form a robust unified attack detection framework. Extensive experiments on UniAttackData and three other datasets demonstrate the superiority of our approach for unified face attack detection. Dataset link: https://sites.google.com/view/face-anti-spoofing-challenge/dataset-download/uniattackdatacvpr2024

The current approaches for fake news detection are mainly devoted to extracting candidate evidence from comments (or external articles) and establishing interactive reasoning with the news itself to verify the falsehood of the news. However, they still have several drawbacks: 1) The interaction object is coarse-grained, which mainly drives the entire news to participate in interaction, but ignores the learning of potential suspicious segments in news; 2) The reasoning ways are relatively single, making it difficult to explore the various possible correlations between news and candidate evidence. To this end, we propose Unified Evidence Enhancement Inference framework (UEEI) to discover and infer high-quality evidence to reveal the false parts of news for detection. Specifically, UEEI first promotes the interaction fusion between comments and news from the perspectives of semantic and emotion, thereby learning potential suspicious fragments in news. Then, the model constructs entity-level and relationship-level retrievals to screen sufficient candidate evidence from external sources. Finally, we measure coherence between suspicious fragments and candidate evidence by multi-view reasoning, and further infer explainable evidence that discovers the false parts of news. Experiments on three public datasets confirm the effectiveness and interpretability of our UEEI.

3D open-vocabulary scene understanding aims to recognize arbitrary novel categories beyond the base label space. However, existing works not only fail to fully utilize all the available modal information in the 3D domain but also lack sufficient granularity in representing the features of each modality. In this paper, we propose a unified multimodal 3D open-vocabulary scene understanding network, namely UniM-OV3D, aligning point clouds with image, language and depth. To better integrate global and local features of the point clouds, we design a hierarchical point cloud feature extraction module that learns fine-grained feature representations. Further, to facilitate the learning of coarse-to-fine point-semantic representations from captions, we propose the utilization of hierarchical 3D caption pairs, capitalizing on geometric constraints across various viewpoints of 3D scenes. Extensive experimental results have demonstrated the effectiveness and superiority of our method in open-vocabulary semantic and instance segmentation, which achieves state-of-the-art performance on both indoor and outdoor benchmarks such as ScanNet, ScanNet200, S3IDS and nuScenes. Code is available at https://github.com/hithqd/UniM-OV3D.

Extreme weather can lead to weather-induced disasters. These have a profound impact on communities worldwide, causing loss of life, damage to properties and infrastructure, and disruption of daily activities. In alignment with the United Nations Sustainable Development Goals, addressing the increasing frequency and severity of these events, exacerbated by climate change, is imperative. Exploring public perception and responses to weather disasters becomes crucial for policymakers to formulate effective strategies that not only mitigate the impacts but also contribute to the goal of ensuring sustainable and resilient communities. Social media, as a pervasive and real-time communication platform, has gathered a large amount of public opinion. In this work, we analyze public perception towards weather disasters based on tweets and metaphors. Metaphor, as a linguistic device, plays a pivotal role in unraveling cognitive processes and understanding how individuals perceive and make sense of concepts. We focus on tweets related to four distinct types of weather disasters i.e., floods, hurricanes, tornadoes, and wildfires, aiming to extract nuanced insights regarding public perceptions, concerns, and attitudes towards these specific events. We also deliver constructive recommendations, based on the insights.

Active learning is known to be a well-motivated algorithm that aims to maximize model performance with relatively small data, but it introduces sampling bias due to active selection. To adjust the bias, current literature utilizes corrective weights in a supervised learning approach. However, those methods consider only a small amount of actively sampled data and thus estimation efficiency can be improved using unsampled data together. In this paper, we develop an actively improved augmented estimation equation (AI-AEE) based on corrective weights as well as imputation models that allow us to leverage unlabeled data. The asymptotic distribution of the proposed estimator as the solution to the AI-AEE is derived, and an optimal sampling scheme to minimize the asymptotic mean squared error of the estimator is proposed. We then propose a general practical algorithm for training prediction models in the active and semi-supervised learning framework. The superiority of our method is demonstrated on synthetic and real data examples.

Speaker recognition technologies have been successfully applied in diverse domains, benefiting from the advance of deep learning. Nevertheless, current efforts are still subject to the lack of labeled data. Such issues have been attempted in computer vision, through semi-supervised learning (SSL) that assigns pseudo labels for unlabeled data, undertaking the role of labeled ones. Through our empirical evaluations, the state-of-the-art SSL methods show unsatisfactory performance in speaker recognition tasks, due to the imbalance between the quantity and quality of pseudo labels. Therefore, in this work, we propose a two-stage SSL framework, with the aim to address the data scarcity challenge. We first construct an initial contrastive learning network, where the encoder outputs the embedding representation of utterances. Furthermore, we construct an iterative holistic semi-supervised learning network that involves a clustering strategy to assign pseudo labels, and a gated label learning (GLL) strategy to further select reliable pseudo-label data. Systematical evaluations show that our proposed framework achieves superior performance in speaker recognition than the state-of-the-art methods, matching the performance of supervised learning.

Facility location problems have been studied in settings like hospital placement or the competition between stores. In some cases, a central authority coordinates facility placements to optimize metrics like the coverage of an area or emergency response time. In many cases, however, facilities are placed by multiple rational agents to maximize their utility, e.g., the number of clients they attract. In previous research, these games feature simplistic client behavior independent of other clients' strategic choices, e.g., visiting the closest facility. Our goal is to understand what happens if clients also act selfishly, resulting in a two-stage game consisting of strategic facility and client agents. In three recent publications, we investigated such two-stage models for clients that optimize their waiting times. We showed the existence and gave algorithms for (approximate) subgame perfect equilibria, a common extension of Nash equilibria for sequential games. To learn more about this domain, we intend to investigate further natural client behaviors and eventually create a more general model or hierarchy of two-sided facility location games. With this, we aim to make predictions in real-world settings, e.g., the placement of renewable energy infrastructure.

We initiate the study of a novel problem in mechanism design without money, which we term Truthful Interval Covering (TIC). An instance of TIC consists of a set of agents each associated with an individual interval on a line, and the objective is to decide where to place a covering interval to minimize the total social or egalitarian cost of the agents, which is determined by the intersection of this interval with their individual ones. This fundamental problem can model situations of provisioning a public good, such as the use of power generators to prevent or mitigate load shedding in developing countries. In the strategic version of the problem, the agents wish to minimize their individual costs, and might misreport the position and/or length of their intervals to achieve that. Our goal is to design truthful mechanisms to prevent such strategic misreports and achieve good approximations to the best possible social or egalitarian cost. We consider the fundamental setting of known intervals with equal lengths and provide tight bounds on the approximation ratios achieved by truthful deterministic mechanisms. For the social cost, we also design a randomized truthful mechanism that outperforms all possible deterministic ones. Finally, we highlight a plethora of natural extensions of our model for future work, as well as some natural limitations of those settings.