paperlist.ai

AI conference papers

AI conference paper library

Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

IJCAI 2026

Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations

Safwen Naimi, Wassim Bouachir, Guillaume-Alexandre Bilodeau, Brian Mishara

Understanding and monitoring human behavior in metro stations play an important role in supporting suicide prevention efforts, where early identification of high-risk situations can enable timely intervention. This requires assessing suicide risk from a surveillance video by jointly reasoning about the behavior of each passenger, his/her spatial context, and temporal dynamics. However, this assessment using videos captured by surveillance cameras is challenging, as it demands accurate perception of human motion, understanding of platform geometry, and aggregation of heterogeneous behavioral cues over time. In this work, we formalize the task of Suicide Risk Assessment (SRA) in metro stations and introduce the first interpretable framework that addresses this challenge. Unlike approaches that focus on isolated subtasks or attempt to infer intent directly, our formulation assesses suicide risk from accumulated evidence by incorporating person tracking, activity recognition, semantic segmentation of the platform, and trajectory-driven risk heatmap modeling. By formalizing SRA as a distinct task and benchmarking a complete operational pipeline achieving 83.2% ROC-AUC on real surveillance data, this work highlights the complexity of suicide risk assessment and opens new directions for research on interpretable AI systems for social good.

IJCAI 2026

Optimizing Sensor Placement with Greedy Algorithms: A Case Study in Wildlife Camera Trapping for Spatial Capture-Recapture Population Estimation

Hannah Murray, Amrita Gupta, Arielle W. Parsons, Justin P. Suraci, Bistra Dilkina

Estimating wildlife populations is central to conservation planning, yet designing sensor deployments that produce reliable data for such estimates remains challenging. Spatial capture-recapture (SCR) models, widely used to estimate animal population sizes, are highly sensitive to sensor layout, where poor placement can substantially increase uncertainty in population estimates. We present a novel approach that formulates sensor placement for SCR as a scenario-based optimization problem under real-world resource constraints. Using collections of simulated animal capture histories spanning ecologically plausible parameter ranges, candidate sensor placements are evaluated via closed-form, SCR-derived design criteria linked to the precision of population estimates and optimized using both genetic algorithms and a greedy search strategy. We demonstrate our approach through an example American marten camera trapping study in British Columbia's South Chilcotin Mountains, achieving lower relative standard error and bias in population estimates than single-scenario and grid-based baselines. Our method was co-developed with conservation practitioners and is currently being used in real-world monitoring programs. This framework offers a general approach for designing wildlife surveys that support reliable population estimation across a range of realistic ecological scenarios.

IJCAI 2026

Clinically-Oriented Screening Model for Diabetic Retinopathy Severity Grading and Diabetic Macular Edema Detection

Sanchika Menezes, Rohan Chawla, Nawazish Shaikh, Pradeep Venkatesh, Radhika Tandon, Srinivas Rana

Diabetic retinopathy (DR) and diabetic macular edema (DME) are leading causes of preventable blindness worldwide. Automated screening tools are critical for timely detection at scale, particularly in low-resource settings where access to ophthalmologists is limited. We propose DRDME-Net, a deployment-driven joint learning framework that formulates DR grading as an ordinal regression task and DME detection via a continuous surrogate, rather than conventional classification. This design yields stable risk scores tightly aligned with operational clinical decision-making thresholds. Evaluation on facility and community cohorts demonstrates that DRDME-Net achieves strong performance across severity boundaries. Insights from an initial feasibility pilot further demonstrate its scalability in real-world workflows. These results highlight the potential of DRDME-Net to expand equitable access to timely detection, reduce preventable vision loss, and provide a practical template for integrating AI into population screening initiatives.

IJCAI 2026

Improving Survey Participation in Low-Literacy Populations Through Value-Sensitive Conversational AI

Raj Gaurav Maurya

Collecting reliable social data from low-literacy populations remains a persistent challenge, particularly when surveys involve sensitive topics and marginalized communities. Traditional paper-based and web-based survey modalities often suffer from high attrition and incomplete responses due to literacy barriers, social pressure, and interactional discomfort. In this paper, we present findings from an initial field evaluation comparing multiple survey modalities paper-based interviews, digital web-based surveys, conversational AI (convAI) surveys, and convAI enhanced with layered value-sensitive design conducted with low-literacy women across India. Using data from 315 participants, we show that convAI significantly improves survey completion rates relative to traditional modalities, with the highest completion and lowest drop-off observed when value-sensitive and culturally aligned conversational design elements are fully integrated. These results demonstrate the importance of human-centered and value-sensitive interaction design in enabling inclusive, ethical, and scalable data collection; motivating more `AI for social good' applications.

IJCAI 2026

Quantifying Error Disparities in Population Health Models

Aaron Marker, Salvatore Giorgi, Adithya V Ganesan, Vasudha Varadarajan, Ojas Deshpande, Laura Brandt, Gabriel Odom, H. Andrew Schwartz

Many high-stakes social applications of AI, such as public health surveillance and policy planning, operate at the community- rather than individual-level. However, most AI fairness research operates at the individual- or data-level (i.e. document or image) and rely on metrics defined over discrete demographic categories rather than population-level demographic proportions. In this work, we first introduce the Bilateral Concentration Index (BCI) to quantify nonmonotonic error disparities missed by the category-based metrics used at individual or data-levels. Then we conduct a large-scale audit of sociodemographic error disparities in both lexical- and transformer-based models of county-level health outcomes, over a dataset cover billions of community-mapped messages. While all tasks had significant disparity, the size varied widely depending on the outcome and model, from BCI of 2.1% for predicting life satisfaction to 17.0% for predicting fair or poor health. We further evaluate four approaches for incorporating sociodemographic information, as potential bias mitigation strategies, finding that while demographic inclusion consistently improved predictive accuracy, it frequently amplified error disparities. The largest disparities were associated with education and income (BCI = 2.7–16.4%), often reducing accuracy for low-income, and in some cases, high-income communities. These findings highlight a critical accuracy–fairness trade-off in community-level models for public health tasks, demonstrating how seemingly beneficial modeling choices can lead to increased disparities which could disadvantage communities if model predictions are used to make policy decisions in geographic areas where this public health data is not available.

IJCAI 2026

AG-STELLA: Spatio-Temporal Learning for Water-related Agricultural Land Use Activity Mapping with AlphaEarth

Nibir Chandra Mandal, Oishee Bintey Hoque, Kyle Luong, Samarth Swarup, Kirti Rajagopalan, Mandy L Wilson, Abhijin Adiga, Madhav Marathe

Accurate mapping of agricultural land use activity, particularly long-term transition from cropland to pasture and short-term transition between cropland to fallow land, is essential for sustainable water management, drought response, and food-system resilience which directly supports United Nations Sustainable Development Goals (SDG-2 and SDG-8). However, reliable agricultural land use activity mapping is challenging due to spectral ambiguity, temporal irregularities, severe class imbalance, and limited generalization across agricultural regions. In this work, we propose AG-STELLA, a knowledge guided spatio-temporal model that (i) captures temporal changes of agricultural lands using pretrained spatio-temporal transformers; (ii) integrates geospatial context using AlphaEarth embedding; (iii) introduces a temporal transition latent space with temporal consistency constraints; (iv) employs guidance through hydroclimatic consistency; and (v) uses a land use-aware gated decoder to improve robustness across regions. Through experimentation across three water-stressed U.S. states, we show consistent gains over baseline vision and foundation models, achieving up to 27% F1-score improvement for pasture (minority class) and 16% overall. We further show the robustness across heterogeneous regions through cross-state transfer learning, where AG-STELLA consistently outperforms foundation model baselines and achieve up to 82.3% F1-score for fallow land with a 9.6% improvement over the best foundation model. Code: https://github.com/Nibir088/AG-STELLA

IJCAI 2026

Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications

Ricardo Luna Gutierrez, Sahand Ghorbanpour, Rahman Ejaz, Varchas Gopalaswamy, Riccardo Betti, Vineet Gundecha, Aarne Lees, Soumyendu Sarkar

Inertial Confinement Fusion (ICF) holds transformative promise for sustainable, near-limitless clean energy, yet remains constrained by prohibitively high costs and limited experimental opportunities. This paper presents Human-in-the-Loop Meta Bayesian Optimization (HL-MBO), a framework that integrates expert knowledge with few-shot, uncertainty-aware machine learning to accelerate discovery in data-scarce, high-stakes scientific domains. HL-MBO introduces a meta-learned surrogate model with an expert-informed acquisition function to recommend candidate experiments. To foster trust and enable informed decisions, HL-MBO also provides interpretable explanations of its suggestions. We show that HL-MBO outperforms current BO methods for ICF energy yield optimization, as well as benchmarks in molecular optimization and critical-temperature maximization for superconducting materials.

IJCAI 2026

When Can We Trust Fairness Audits? Identifying Reliability Boundaries of Third-party Audit Conclusions

Yuanhao Liu, Qi Cao, Huawei Shen

Fairness auditing aims to assess whether a model is fair, playing a critical role in identifying potential risks in deployed AI systems. In practice, due to limited access, third-party auditors often rely on self-collected datasets (e.g., via sock-puppets), which may differ from real-world deployment scenarios. Such discrepancy can lead to inconsistencies between audit conclusions on the collected data and those in actual deployment, raising concerns about the reliability of third-party audits. This motivates a critical question: When can we trust the fairness audit conclusion derived from third-party datasets? Answering this question is challenging, as the actual deployment distribution is typically inaccessible or unobservable. To tackle this, we introduce the Consistency Radius, a metric that quantifies the maximum distribution shift under which an audit conclusion based on third-party dataset remain consistent. We further propose a convex relaxation optimization-based method to estimate the radius relying solely on model responses over the audit dataset. Leveraging this framework, third-party auditors can provide their datasets to model providers and request the magnitude of distributional discrepancy relative to the deployment distribution, enabling reliable audit conclusions without requiring any direct data access.

IJCAI 2026

LLM-Enhanced Knowledge and Learning Path Understanding for Graph-based Educational Recommendation

Qingqing Liang, Shuyan Zheng, Peiwei Xia, Chunyang Wang, Xuesong Lu, Aoying Zhou

Educational recommendations empower personalized learning by suggesting suitable learning resources to learners, and the graph-based recommenders are widely adopted. Existing methods are mainly ID-based, which initialize learners and resources with trainable identifiers and optimize their representations solely from the interaction graph. As a result, the lack of semantic understanding of learning resources and learning paths hinders further improvements in recommendation accuracy. To alleviate the problem, we propose KLU4EduRec, which leverages large language models (LLMs) to understand resource knowledge and learning paths, thereby enhancing traditional graph-based educational recommenders. Specifically, for learning path understanding, we segment a learning path by detecting learning pattern drift in resource knowledge sequence, and prompt LLMs to infer learners' learning patterns within each segment. The segment-level patterns are then chronologically aggregated to represent the overall learning path. Besides, we prompt LLMs to summarize the core knowledge of learning resources from their content as complementary semantic signals. Finally, the resulting semantic representations are aligned and fused with structural representations learned by a graph-based recommender to enable more accurate recommendations. Extensive experiments show that KLU4EduRec greatly outperforms existing methods, including traditional ID-based methods and recent LLM-powered methods. A case study shows how the understanding of pattern drift in a learning path leads to more suitable recommendations. Our codes are available at https://github.com/DaSESmartEdu/KLU4EduRec.

IJCAI 2026

Improving Scientific Formula Verbalization in Large Speech Language Models for Accessible Learning

Xueyi Li, Tianqiao Liu, Zitao Liu, Teng Guo, Yongdong WU

Online learning systems provide accessible learning opportunities for blind or low-vision students. To support access to complex scientific materials, the speech models used in these systems need to deliver accurate scientific formula verbalization. While recent large speech language models (LSLMs) provide remarkable low-latency streaming capabilities, their potential for scientific formula verbalization remains underexplored. In this paper, we propose Formula-Speech, the first end-to-end LSLM designed for scientific formula verbalization. Specifically, we construct two high-quality scientific formula datasets with educational experts to align speech models with scientific formula verbalization patterns. We then adopt a lightweight and effective two-stage training framework, combining supervised fine-tuning for basic formula-to-speech alignment with reinforcement learning guided by a custom reward function to optimize for human-preferred verbalization. Experimental results show that our model significantly improves the verbalization performance of LSLMs and achieves state-of-the-art results across multiple scientific domains. Our code and datasets are available at https://github.com/ai4ed/FormulaSpeech.

IJCAI 2026

Sustainable Intelligence for the Wild: Democratizing Ecological Monitoring via Knowledge-Adaptive Edge Expert Agents

Jiaxing Li, Hao Fang, Chi Xu, Miao Zhang, Jiangchuan Liu, William I. Atlas, Katrina M. Connors, Mark A. Spoljaric

Rapid biodiversity loss underscore the urgency of effective monitoring, yet manual surveys remain resource-intensive. While on-device AI offers a scalable alternative, its performance in the wild is often challenged by environmental variability. Current methods rely heavily on cloud resource, which requires continuous uploading of field data for model retraining. This approach is unsuitable for remote deployments because it consumes limited power and network connectivity. To address these constraints, this research proposes a shift from model adaptation to knowledge adaptation. We introduce an architecture that separates visual perception from reasoning, combining a visual encoder with a dynamic knowledge base. We uses an explicit knowledge base to replace implicitly encoding expert knowledge into model parameters. This method also supports knowledge sustainability by preserving expert insights in a structured form. Through cross-disciplinary collaboration with biologists and Indigenous communities, this work advances ethical AI co-development, fostering responsible and culturally informed ecosystem management.

IJCAI 2026

Context-Aware Concept Distillation for Trustworthy Flood Prediction

Eli Levinkopf, Efrat Morin, Claudia V. Goldman

Effective flood risk management relies on accurate forecasting, yet the ”black box” nature of state-of-the-art Deep Learning models creates a barrier to trust and accountability in high-stakes public safety decisions. While existing Explainable AI (XAI) methods offer local attributions, they fail to provide the verifiable, operationally meaningful causal narratives required by disaster response authorities. To address this societal challenge, we propose Context-Aware Concept Distillation (CACD), a framework developed in collaboration with domain experts to distill opaque LSTMs into interpretable, hydrology-aware surrogate models. We introduce an unsupervised pipeline to discover a ”Hydrological Language” and a Residual Hypernetwork that dynamically modulates these concepts based on static basin characteristics. Evaluated on 5,203 basins globally, our model achieves high fidelity (Median NSE 0.70), significantly outperforming black-box baselines (e.g., Multi Layer Perceptrons) on unseen future data. By demonstrating that human-interpretable concepts are sufficient to reconstruct flood dynamics, this work balances AI accuracy with the transparency required for responsible environmental decision-making.

IJCAI 2026

An LLM-based Chain-of-Response Counter-Scam System

Heedou Kim, Mogan Gim, Donghee Choi, Soonil Bae, Hoonick Lee, Mi-Young Kim, Jaewoo Kang

The rapid evolution of online scams, driven by transnational networks and mass-produced social engineering scenarios, has exposed the speed limitations of conventional detection, necessitating tighter inter-agency coordination. While LLMs show promise in scam identification, their role in accelerating integrated response frameworks remains underexplored. We propose Counter-Scam, a unified LLM-based multi-agent framework that orchestrates end-to-end response from initial detection to crime investigation. The framework first proposes safe data guidelines, emphasizing non-public scam data and secure dataset construction via scam-specific NER. Developed with insights from 37 stakeholders to reduce delays and improve analytical efficiency, the system integrates CSRA (multi-agent mitigation), CSRT (nine role-aligned NLP tasks), and CSRD (a corpus of 185,300 scam cases and 38,587 knowledge entries). Experiments show that fine-tuned sLLMs surpass commercial models with over 10% in all CSRT tasks and a 0.24 F1 improvement in scam-specific NER. This proves the framework's capability for enabling rapid, collaborative mitigation of online scam.

IJCAI 2026

Addressing Overcommitment in the Reasoning of Gendered Economic Memes Under Multimodal Ambiguity

Kushal Kanwar, Dushyant Singh Chauhan, Kapil Rana, Gopendra Vikram Singh, Nils Lukas

Multimodal meme understanding is increasingly used to analyze socially sensitive content, yet existing models often exhibit biased behavior when interpreting economic dependence and social roles under ambiguity. Many memes express economic relationships through sparse text or symbolic visual cues, providing insufficient evidence for gendered attribution. In such underspecified settings, models tend to rely on pretraining correlations, leading to hallucinated and stereotypical economic role assignments. In this work, we study gendered economic dependence in image-text memes through the lens of contextual sufficiency and identify epistemic overcommitment—inferring roles without adequate evidence—as a primary source of bias. We propose CGER-Net, a context-grounded multimodal framework that estimates whether the input provides sufficient evidence for gendered economic reasoning and applies evidence-gated inference to enable confident attribution when cues are explicit while favoring principled abstention otherwise. We evaluate CGER-Net on EconMeme-GE, a curated dataset of image-text memes annotated as Men, Women, Neutral, or Ambiguous. Across strong contemporary multimodal baselines, CGER-Net reduces Gender Overcommitment Rate by up to 44% on ambiguous instances while maintaining comparable accuracy on unambiguous cases. Human evaluation further shows that 79% of generated rationales are judged as epistemically aligned with the available evidence. These results highlight the importance of modeling when not to infer for reliable and responsible multimodal analysis.

IJCAI 2026

Policy-Embedded Graph Expansion: Networked HIV Testing with Diffusion-Driven Network Samples

Akseli Kangaslahti, Davin Choo, Lingkai Kong, Milind Tambe, Alastair van Heerden, Cheryl Johnson

HIV is a retrovirus that attacks the human immune system and can lead to death without proper treatment. In collaboration with the WHO and the University of Witwatersrand, we study how to improve the efficiency of HIV testing with the goal of eventual deployment, directly supporting progress toward UN Sustainable Development Goal 3.3. While prior work has demonstrated the promise of intelligent algorithms for sequential, network-based HIV testing, existing approaches rely on assumptions that are impractical in our real-world implementations. Here, we study sequential testing on incrementally revealed disease networks and introduce Policy-Embedded Graph Expansion (PEGE), a novel framework that directly embeds a generative distribution over graph expansions into the decision-making policy rather than attempting explicit topological reconstruction. We further propose Dynamics-Driven Branching (DDB), a diffusion-based graph expansion model that supports decision making in PEGE and is designed for data-limited settings where forest structures arise naturally, as in our real-world referral process. Experiments on real HIV transmission networks show that the combined approach (PEGE + DDB) consistently outperforms baselines (e.g., 17.3% improvement in discounted reward and 15.4% more HIV detections with 25% of the population tested) and explore key tradeoffs that drive solution quality.

IJCAI 2026

Towards an Early Warning System for Ocean Heat Extremes Through AI-Ocean Dynamics Synergy

Zheng Jiang, Wei Wang, Gaowei Zhang, Yifei Bao, Zengzhou Hao, Lingyu Xu, Suixiang Shi, Lei Wang, Yi Wang

Ocean heat extremes, including marine heatwaves and the El Ni\~no–Southern Oscillation (ENSO), exert profound impacts on marine ecosystems and socio-economic stability. Establishing robust early warning systems is critical for proactive risk management; however, conventional predictive models often fail to generalize to the intensifying, non-stationary extremes driven by rapid global warming. This project introduces a novel AI-Ocean Dynamics synergy designed to provide an integrated early warning system. By synthesizing multi-source observations with physics-informed neural networks, it ensures predictions remain constrained by fundamental physical laws. The system forecasts event onset, intensity, duration, and spatial extent while simultaneously attributing the underlying mechanisms, such as ocean advection and air–sea heat exchange. To validate performance, we establish a specialized ocean heat extremes benchmark to assess predictive skill and attribution reliability. Furthermore, the system incorporates an incremental learning mechanism, enabling continuous adaptation to long-term climatic and environmental evolutions. This project advances the development of reliable, interpretable, and adaptive early warning systems, providing a vital tool for informed policy and maritime decision-making.

IJCAI 2026

iFire AI: AI-powered Wildfire Simulation and 3D Immersive Visualisation

Renhao Huang, Lara Clemente, Mario Flores Gonzalez, Yang Song, Greg Drummond, Gonzalo Herrera, Jason Sharples, Michael Ostwald, Maurice Pagnucco, Ali Asadipour, Dennis Del Favero

Wildfires, especially extreme wildfires, can cause prolonged damage to natural environments, human lives and global economies. To mitigate these impacts, enhancing wildfire preparedness is critical. This research proposal introduces iFire AI, a collaborative project to develop a world-leading 3D immersive visualisation system that enables users to experience and understand extreme wildfire scenarios. iFire AI utilises our advanced 360-degree immersive system AVIE, which visualises interactive landscapes and wildfire events enhanced by AI techniques. Firstly, we propose a deep learning model for wildfire behaviour modelling that generates minute-resolution wildfire progression information for immersive visualisation, supported by a Sim2Real pipeline that integrates simulated and real-world data to address data insufficiency and enhance model development and evaluation. We also explore 3D tree reconstruction using 3D Gaussian splatting, creating visually realistic and computationally efficient tree models. iFire AI can enhance users' risk perception, situational awareness and collaborative decision-making, and thereby reduce risks due to extreme wildfires and promote sustainable development.

IJCAI 2026

Column Generation for the Micro-Transit Zoning Problem

Hins Hu, Rishav Sen, Jose Paolo Talusan, Abhishek Dubey, Aron Laszka, Samitha Samaranayake

Along with the rapid development of new urban mobility options like ride-sharing over the past decade, on-demand micro-transit services stand out as a middle ground, bridging the gap between fixed-line mass transit and single-request ride-hailing, balancing ridership maximization and travel time minimization. Micro-transit adoption can have significant social impact. It improves urban sustainability, through lower energy consumption and reduced emissions, while enhancing equitable mobility access for disadvantaged communities, thanks to its lower vehicle miles per passenger, flexible schedules, and affordable pricing. However, effective operation of micro-transit services requires planning geo-fenced zones in advance, which involves solving a challenging combinatorial optimization problem. Existing approaches enumerate candidate zones first and selects a fixed number of optimal zones in the second step. In this paper, we generalize the Micro-Transit Zoning Problem (MZP) to allow a global budget rather than imposing a size limit for candidate zones. We also design a Column Generation (CG) framework to solve the problem exactly and several pricing heuristics to accelerate computation. Extensive numerical experiments across major U.S. cities demonstrate that our approach produces higher-quality solutions more efficiently and scales better in the generalized setting.

IJCAI 2026

Temporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection

Runang He, Tongya Zheng, Huiling Peng, Yuanyu Wan, Bingde Hu, Jiawei Chen, Canghong Jin, Mingli Song, Can Wang

Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors. Recently, advanced Graph Anomaly Detection (GAD) approaches applied to blockchains have faced two critical challenges: adversarial pattern evolution by malicious actors and the out-of-distribution (OOD) problem caused by varied transaction semantics on blockchains. To address these challenges, we propose a novel framework termed TEmporal Motif-aware Graph Test-Time Adaptation (TEMG-TTA). First, we comprehensively capture the 3-node temporal motif distribution of each active address using an efficient computational mechanism, enabling downstream temporal motif-aware graph learning. Second, we design a simple yet effective test-time adaptation strategy to facilitate the sharing of common patterns between training and testing graphs. Extensive experiments on 5 real-world datasets demonstrate that our proposed TEMG-TTA outperforms state-of-the-art GAD approaches by an average of 54.88%. A further case study on interpretable motif patterns reveals that TEMG-TTA explicitly characterizes the complex transaction patterns of anomalous addresses, thereby verifying the effectiveness of our technical designs. Our code is publicly available at https://github.com/LuoXishuang0712/TEMG-TTA/.

IJCAI 2026

Deep Reinforcement Learning Enhanced Semi-supervised Graph Neural Network for Credit Card Fraud Detection

Huilin He, Kun Zhu, Zewen Hu, Jie Wang, Dawei Cheng

Credit card fraud threatens global payment ecosystems, causing billions in losses and undermining public trust. Efficient fraud detection remains challenging due to surging transaction volumes and evolving tactics. While Graph Neural Networks (GNNs) excel at modeling structural relationships, they struggle in real-world scenarios characterized by label scarcity and often overlook discriminative feature-level signals, leaving rich risk signals underutilized without costly manual engineering. To address this, we propose DRESS, a Deep Reinforcement Learning (DRL) Enhanced Semi-supervised GNN framework. It employs a DRL agent to automatically capture and enhance feature-level risks, fusing them with graph-based structural risks and propagating via a gated temporal attention network for final prediction. To mitigate inefficient exploration of the DRL module, we incorporate a feature self-attention layer to weigh feature contributions to fraud detection and employ self-supervised intrinsic rewards to help optimize the DRL module efficiently. Extensive experiments on real-world datasets demonstrate that DRESS outperforms state-of-the-art methods, especially in low-label scenarios with only 2%–10% labeled samples. By empowering resource-limited institutions to combat fraud and prevent financial loss, DRESS secures the digital trust essential for inclusive growth, contributing to AI for poverty alleviation and economic development.

Keep browsing

More papers from top AI conferences

30+ top conferences, by conference, year, and topic.