OpenRaiser/Intern-Atlas huggingface dataset:
ComputAgeBench: Epigenetic Aging Clocks Benchmark
Authors: Dmitrii Kriukov, Evgeniy Efimov, Ekaterina Kuzmina, Anastasiia Dudkovskaia, Ekaterina E. Khrameeva, Dmitry V. Dylov
Venue: KDD
Year: 2025

The success of clinical trials of longevity drugs relies heavily on identifying integrative health and aging biomarkers, such as biological age. Epigenetic aging clocks predict the biological age of an individual using their DNA methylation profiles, commonly retrieved from blood samples. However, there is no standardized methodology to validate and compare epigenetic clock models as yet. We propose ComputAgeBench, a unifying framework that comprises such a methodology and a dataset for comprehensive benchmarking of different clinically relevant aging clocks. Our methodology exploits the core idea that reliable aging clocks must be able to distinguish between healthy individuals and those with aging-accelerating conditions. Specifically, we collected and harmonized 66 public datasets of blood DNA methylation, covering 19 such conditions across different ages, and tested 13 published clock models. Additionally, we compiled 46 separate datasets to facilitate the training of new aging clocks. We believe our work will bring the fields of aging biology and machine learning closer together for the research on reliable biomarkers of health and aging. Code https://github.com/ComputationalAgingLab/ComputAge Dataset https://huggingface.co/datasets/computage/computage_bench
Neurophysiologically Realistic Environment for Comparing Adaptive Deep Brain Stimulation Algorithms in Parkinson's Disease
Authors: Ekaterina Kuzmina, Dmitrii Kriukov, Mikhail Lebedev, Dmitry V. Dylov
Venue: KDD
Year: 2025

Adaptive deep brain stimulation (aDBS) has emerged as a promising treatment for Parkinson's disease (PD). In aDBS, a surgically placed electrode sends dynamically altered stimuli to the brain based on neurophysiological feedback: an invasive gadget that limits the amount of data one could collect for optimizing the control offline. As a consequence, a plethora of synthetic models of PD and those of the control algorithms have been proposed. Herein, we introduce the first neurophysiologically realistic benchmark for comparing said models. Specifically, our methodology covers not only conventional basal ganglia circuit dynamics and pathological oscillations, but also captures 15 previously dismissed physiological attributes, such as signal instabilities and noise, neural drift, electrode conductance changes and individual variability - all modeled as spatially distributed and temporally registered features via beta-band activity in the brain and a feedback. Furthermore, we purposely built our framework as a structured environment for training and evaluating deep reinforcement learning (RL) algorithms, opening new possibilities for optimizing aDBS control strategies and inviting the machine learning community to contribute to the emerging field of intelligent neurostimulation interfaces. Code repository: https://github.com/NevVerVer/DBS-Gym
LLMLight: Large Language Models as Traffic Signal Control Agents
Authors: Siqi Lai, Zhao Xu, Weijia Zhang, Hao Liu, Hui Xiong
Venue: KDD
Year: 2025

Traffic Signal Control (TSC) is a crucial component in urban traffic management, aiming to optimize road network efficiency and reduce congestion. Traditional TSC methods, primarily based on transportation engineering and reinforcement learning (RL), often struggle with generalization abilities across varied traffic scenarios and lack interpretability. This paper presents LLMLight, a novel framework employing Large Language Models (LLMs) as decision-making agents for TSC. Specifically, the framework begins by instructing the LLM with a knowledgeable prompt detailing real-time traffic conditions. Leveraging the advanced generalization capabilities of LLMs, LLMLight engages a reasoning and decision-making process akin to human intuition for effective traffic control. Moreover, we build LightGPT, a specialized backbone LLM tailored for TSC tasks. By learning nuanced traffic patterns and control strategies, LightGPT enhances the LLMLight framework cost-effectively. Extensive experiments conducted on ten real-world and synthetic datasets, along with evaluations by fifteen human experts, demonstrate the exceptional effectiveness, generalization ability, and interpretability of LLMLight with LightGPT, outperforming nine baseline methods and ten advanced LLMs. Our project is available at https://github.com/usail-hkust/LLMTSCS.
Simple yet Effective Graph Distillation via Clustering
Authors: Yurui Lai, Taiyan Zhang, Renchi Yang
Venue: KDD
Year: 2025

Despite plentiful successes achieved by graph representation learning in various domains, the training of graph neural networks (GNNs) still remains tenaciously challenging due to the tremendous computational overhead needed for sizable graphs in practice. Recently, graph data distillation (GDD), which seeks to distill large graphs into compact and informative ones, has emerged as a promising technique to enable efficient GNN training. However, most existing GDD works rely on heuristics that align model gradients or representation distributions on condensed and original graphs, leading to compromised result quality, expensive training for distilling large graphs, or both. Motivated by this, this paper presents an efficient and effective GDD approach, ClustGDD. Under the hood, ClustGDD resorts to synthesizing the condensed graph and node attributes through fast and theoretically-grounded clustering that minimizes the within-cluster sum of squares and maximizes the homophily on the original graph. The fundamental idea is inspired by our empirical and theoretical findings unveiling the connection between clustering and empirical condensation quality using Fréchet Inception Distance, a well-known quality metric for synthetic images. Furthermore, to mitigate the adverse effects caused by the homophily-based clustering, ClustGDD refines the nodal attributes of the condensed graph with a small augmentation learned via class-aware graph sampling and consistency loss. Our extensive experiments exhibit that GNNs trained over condensed graphs output by ClustGDD consistently achieve superior or comparable performance to state-of-the-art GDD methods in terms of node classification on five benchmark datasets, while being orders of magnitude faster.
Fourth Workshop on Personalization and Recommendations in Search (PaRiS)
Authors: Sudarshan Lamkhede, Moumita Bhattacharya
Venue: KDD
Year: 2025

With the proliferation of personal computing devices and a large number of logged-in experiences, search has evolved to a stage with many different product scenarios where personalization plays a crucial role for relevance, quality, and user satisfaction. The purpose of this workshop is to have a forum where the latest research and advancements specifically on Personalization and Recommendations in Search PaRiS can be discussed in conjunction with KDD 2025. This will be the fourth instance of this workshop. We held three very successful instances of this workshop at the SIGIR 2024 PaRiS-2024, WebConf 2023 PaRiS-2023 and WSDM 2022. This year we will especially focus on applications of LLM and Generative AI to enable personalization and recommendations netflix-search in the context of search, for example, conversational assistants, while continuing to use this workshop for discussing other advances and applications in the context of personalized search and recommendations in the context of search.
Benchmarking and Advancing Large Language Models for Local Life Services
Authors: Xiaochong Lan, Jie Feng, Jiahuan Lei, Xinlei Shi, Yong Li
Venue: KDD
Year: 2025

Large language models (LLMs) have exhibited remarkable capabilities and achieved significant breakthroughs across various domains, leading to their widespread adoption in recent years. Building on this progress, we investigate their potential in the realm of local life services. In this study, we establish a comprehensive benchmark and systematically evaluate the performance of diverse LLMs across a wide range of tasks relevant to local life services. To further enhance their effectiveness, we explore two key approaches: model fine-tuning and agent-based workflows. Our findings reveal that even a relatively compact 7B model can attain performance levels comparable to a much larger 72B model, effectively balancing inference cost and model capability. This optimization greatly enhances the feasibility and efficiency of deploying LLMs in real-world online services, making them more practical and accessible for local life applications. Available resources are at https://github.com/tsinghua-fib-lab/LocalEval.
REDEEMing Modality Information Loss: Retrieval-Guided Conditional Generation for Severely Modality Missing Learning
Authors: Jian Lang, Rongpei Hong, Zhangtao Cheng, Ting Zhong, Yong Wang, Fan Zhou
Venue: KDD
Year: 2025

Traditional multimodal learning approaches often assume that all modalities are available during both the training and inference phases. However, this assumption is often impractical in real-world scenarios due to challenges such as sensor failures, data corruption, or privacy concerns. While recent efforts focus on enhancing the robustness of pre-trained Multimodal Transformers (MTs) under missing modality conditions, mainstream work in this field often overlook reconstructing the missing modalities and rely on static, sample-agnostic prompt-tuning techniques, undermining their efficacy in severe modality missing scenarios. To address these limitations, we propose REDEEM, a novel REtrieval-guiDEd conditional gEnerative fraMework that largely alleviates the modality missing problems on pre-trained MTs. REDEEM consists of a new adaptive retrieval mechanism to identify relevant instances for both modality-complete and -incomplete samples. It then conditions on the remaining modalities and utilizes the retrieved data as experts to effectively recover the missing ones in modality-incomplete instances through a within-modal reconstruction manner. Finally, REDEEM generates sample-aware inter-modal prompts from the retrieved instances to guide MTs in tackling severe modality missing challenges. Comprehensive experiments on three diverse multimodal classification benchmarks demonstrate that REDEEM significantly outperforms competitive baselines.
Range-limited Augmentation for Few-shot Learning in Tabular Data with Comprehensive Benchmark
Authors: Kyungeun Lee, Moonjung Eo, Hye-Seung Cho, Min-Kook Suh, Seoyoon Kim, Ye Seul Sim, Suhee Yoon, Sanghyu Yoon, Woohyung Lim
Venue: KDD
Year: 2025

Few-shot learning is crucial for tabular data, where the high cost of annotation often limits the availability of labeled samples. Despite its importance in real-world applications such as healthcare and finance, few-shot learning in tabular domains has received limited attention. To address this, we introduce range-limited augmentation, a novel augmentation strategy for contrastive learning that perturbs numerical features within predefined feature-specific ranges. Unlike conventional augmentations, which may result in false positive pairs during contrastive learning, our approach ensures semantic consistency by restricting augmentations to ranges. A quantitative analysis confirms that range-limited augmentation better preserves task-relevant information compared to existing augmentation techniques. Additionally, we propose FeSTa (Few-Shot Tabular classification benchmark), the first large-scale benchmark designed to systematically evaluate few-shot learning methods in tabular data. FeSTa includes 50 datasets and 32 algorithms spanning supervised, unsupervised, self-supervised, semi-supervised, and foundation models. Experiments on FeSTa show that range-limited augmentation consistently ranks among the top methods, achieving an average rank of 2.6 out of 32 in 1-shot classification, despite not relying on large-scale pretraining or complex architectures. The benchmark code is available in https://github.com/kyungeun-lee/festa.git.
Logical Anomaly Detection with Text-based Logic via Component-Aware Contrastive Language-Image Training
Authors: Seungeon Lee, Soopil Kim, Sion An, Sang-Chul Lee, Sang Hyun Park
Venue: KDD
Year: 2025

AI-based automatic visual inspection systems have been extensively researched to streamline various industrial products' labor-intensive anomaly detection processes. Despite significant advancements, detecting logical anomalies remains challenging due to the multitude of rules governing the assembly of multiple components to create a normal product. Existing methods have relied solely on image information for anomaly detection, resulting in limited accuracy as they fail to account for these diverse complex rules. Instead, humans detect anomalies by comparing the image with pre-defined logic which can be clearly expressed with natural language. Inspired by the human decision process, we propose a logical anomaly detection model that leverages text-based logic like human reasoning. With user-defined rules (i.e., positive rules) and logically distinct negative rules, we train the model using component-aware contrastive learning that increases the similarity between images and positive rules while decreasing the similarity with negative rules. However, accurately comparing textual and visual features is challenging due to multiple components, each governed by different rules, within a single image. To address this, we developed a zero-shot related region detection technique, which guides the model's focus on components relevant to each rule. We evaluated the proposed model on three public datasets and achieved state-of-the-art results in a few-shot logical anomaly detection task. Our findings highlight the potential of integrating vision-language models to enhance logical anomaly detection and utilizing text-based logic in complex industrial settings.
CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship
Authors: Yeon-Chang Lee, Jaehyun Lee, Michiharu Yamashita, Dongwon Lee, Sang-Wook Kim
Venue: KDD
Year: 2025

The problem of career trajectory prediction (CTP) aims to predict one's future employer or job position. While several CTP methods have been developed for this problem, we posit that none of these methods (1) jointly considers the mutual ternary dependency between three key units (i.e., user, position, and company) of a career and (2) captures the characteristic shifts of key units in career over time, leading to an inaccurate understanding of the job movement patterns in the labor market. To address the above challenges, we propose a novel solution, named as CAPER, that solves the challenges via sophisticated temporal knowledge graph (TKG) modeling. It enables the utilization of a graph-structured knowledge base with rich expressiveness, effectively preserving the changes in job movement patterns. Furthermore, we devise an extrapolated career reasoning task on TKG for a realistic evaluation. The experiments on a real-world career trajectory dataset demonstrate that CAPER consistently and significantly outperforms four baselines, two recent TKG reasoning methods, and five state-of-the-art CTP methods in predicting one's future companies and positions--i.e., on average, yielding 6.80% and 34.58% more accurate predictions, respectively. The codebase of CAPER is available at https://github.com/Bigdasgit/CAPER.
3rd Workshop on Causal Inference and Machine Learning in Practice
Authors: Jeong-Yoon Lee, Jing Pan, Yifeng Wu, Totte Harinen, Paul Lo, Zhenyu Zhao, Huigang Chen, Sichao Yin, Roland Stevenson, Jingshen Wang, Yingfei Wang, Chu Wang, Zeyu Zheng
Venue: KDD
Year: 2025

The 3rd Workshop on Causal Inference and Machine Learning in Practice at KDD 2025 aims to bring together researchers, industry professionals, and practitioners to explore the application of causal inference within machine learning models. As causal machine learning techniques gain traction across industries, practical challenges related to trustworthiness, robustness, and fairness remain at the forefront. This workshop will provide a forum to discuss methodologies for evaluating causal models in real-world scenarios and explore innovative applications that integrate causal inference with generative AI (GenAI) and large language models (LLMs). Topics of interest include using GenAI and LLMs to facilitate causal inference tasks and leveraging causal inference techniques for evaluating and improving GenAI/LLM models. Building on the success of the previous workshop editions at KDD 2023 and KDD 2024, which attracted over 200 and 250 participants, respectively, this workshop will continue fostering collaboration between academia and industry. Through invited talks, contributed papers, and interactive discussions, we will address key challenges and opportunities at the intersection of causal inference and machine learning. As the field continues to evolve, this workshop serves as a crucial platform for knowledge exchange and innovation, driving forward the application of causal techniques in machine learning and AI.
Understanding and Tackling Over-Dilution in Graph Neural Networks
Authors: Junhyun Lee, Veronika Thost, Bumsoo Kim, Jaewoo Kang, Tengfei Ma
Venue: KDD
Year: 2025

Message Passing Neural Networks (MPNNs) hold a key position in machine learning on graphs, but they struggle with unintended behaviors, such as over-smoothing and over-squashing, due to irregular data structures. The observation and formulation of these limitations have become foundational in constructing more informative graph representations. In this paper, we delve into the limitations of MPNNs, focusing on aspects that have previously been overlooked. Our observations reveal that even within a single layer, the information specific to an individual node can become significantly diluted. To delve into this phenomenon in depth, we present the concept of Over-dilution and formulate it with two dilution factors: intra-node dilution for attribute-level and inter-node dilution for node-level representations. We also introduce a transformer-based solution that alleviates over-dilution and complements existing node embedding methods like MPNNs. Our findings provide new insights and contribute to the development of informative representations. The implementation and supplementary materials are publicly available at https://github.com/LeeJunHyun/NATR.
PARSIFAL: Private and Robust Sign Federated Learning
Authors: Runze Lei, Pinghui Wang, Juxiang Zeng, Chenxu Wang, Hongbin Pei, Junzhou Zhao
Venue: KDD
Year: 2025

Federated learning (FL) is a popular collaborative training paradigm in which data owners offer gradients instead of private data to model owners for model training to protect data privacy. However, it faces security threats from two sides: dishonest model owners may extract sensitive information about private data from gradients; meanwhile, adversaries may pretend to be data owners and poison the model by sending malicious gradients. We propose a novel FL protocol, PARSIFAL, to address privacy leakage and model poisoning threats. A poisoning detection module is designed based on a novel sketch structure. This module efficiently detects potential malicious gradients that are dissimilar to the majority of benign gradients. PARSIFAL also contains a robust aggregation module based on sign gradients to mitigate the influence of poisoning gradients on aggregation results. Meanwhile, all processes of our PARSIFAL are protected by privacy protocols, mainly based on secret sharing, to guarantee that malicious detection and aggregation processes will not leak sensitive information. Experimental results show that PARSIFAL improves poisoning defense performance by up to 28% compared with recent baselines.
Divergent Paths: Separating Homophilic and Heterophilic Learning for Enhanced Graph-level Representations
Authors: Han Lei, Jiaxing Xu, Xia Dong, Yiping Ke
Venue: KDD
Year: 2025

Graph Convolutional Networks (GCNs) are predominantly tailored for graphs displaying homophily, where similar nodes connect, but often fail on heterophilic graphs. The strategy of adopting distinct approaches to learn from homophilic and heterophilic components in node-level tasks has been widely discussed and proven effective both theoretically and experimentally. However, in graph-level tasks, research on this topic remains notably scarce. Addressing this gap, our research conducts an analysis on graphs with nodes' category ID available, distinguishing intra-category and inter-category components as embodiment of homophily and heterophily, respectively. We find while GCNs excel at extracting information within categories, they frequently capture noise from inter-category components. Consequently, it is crucial to employ distinct learning strategies for intra- and inter-category elements. To alleviate this problem, we separately learn the intra- and inter-category parts by a combination of an intra-category convolution (IntraNet) and an inter-category high-pass graph convolution (InterNet). Our IntraNet is supported by sophisticated graph preprocessing steps and a novel category-based graph readout function. For the InterNet, we utilize a high-pass filter to amplify the node disparities, enhancing the recognition of details in the high-frequency components. The proposed approach, DivGNN, combines the IntraNet and InterNet with a gated mechanism and substantially improves classification performance on graph-level tasks, surpassing traditional GNN baselines in effectiveness.
ECGrecover: A Deep Learning Approach for Electrocardiogram Signal Completion
Authors: Alex Lence, Federica Granese, Ahmad Fall, Blaise Hanczar, Joe-Elie Salem, Jean-Daniel Zucker, Edi Prifti
Venue: KDD
Year: 2025

In this work, we address the challenge of reconstructing the complete 12-lead ECG signal from its incomplete parts. We focus on two main scenarios: (i) reconstructing missing signal segments within an ECG lead and (ii) recovering entire leads from signal in another unique lead. Two emerging clinical applications emphasize the relevance of our work. The first is the increasing need to digitize paper-stored ECGs for utilization in AI-based applications, often limited to digital 12 lead 10s ECGs. The second is the widespread use of wearable devices that record ECGs but typically capture only one or a few leads. In both cases, a non-negligible amount of information is lost or not recorded. Our approach aims to recover this missing signal. We propose ECGrecover, a U-Net neural network model trained on a novel composite objective function to address the reconstruction problem. This function incorporates both spatial and temporal features of the ECG by combining the distance in amplitude and sycnhronization through time between the reconstructed and the real digital signals. We used real-life ECG datasets and through comprehensive assessments compared ECGrecover with three state-of-the-art methods based on generative adversarial networks (EKGAN, Pix2Pix) as well as the CopyPaste strategy. The results demonstrated that ECGrecover consistently outperformed state-of-the-art methods in standard distortion metrics as well as in preserving critical ECG characteristics, particularly the P, QRS, and T wave coordinates.