OpenRaiser/Intern-Atlas huggingface dataset:
Are Your Fairness Metrics Accurate? A Semi-Supervised Approach to Improving Fairness Estimates Under Sample Selection Bias
Authors: M. Clara De Paolis Kaluza, Thulasi Tholeti, Yile Chen, Ricardo Baeza-Yates, Predrag Radivojac, Shantanu Jain
Venue: KDD
Year: 2025

A key challenge impeding the widespread deployment of machine learning is overcoming the impact of statistical biases in the data. Models trained on unrepresentative data can perform worse than anticipated and differentially affect cross-sections of the population. Therefore, evaluating and vetting models based on an appropriate notion of fairness is often indispensable, making accurate estimation of fairness metrics a critical step to safeguard against deployment of unfair algorithms. It is often assumed that a fairness metric computed from the observed data is accurate. However, in presence of selection bias, also referred to as distributional shifts, fairness metric estimates too can have systematic application-specific errors. In this work we demonstrate this phenomenon and, relying on access to an unbiased unlabeled data, derive a semi-supervised approach to mitigate estimation errors emerging from the biased labeled data. Specifically, we introduce a novel selection bias model called ''sub-class-conditional invariance'' (SCC-invariance), that offers a flexible framework to effectively capture distributional shifts in the real-world data, particularly compared to traditional models such as label shift and covariate shift. Assuming a finite Gaussian mixture form for each class-conditional distribution, we then derive an Expectation-Maximization algorithm to estimate model parameters and correction weights necessary for computing unbiased estimates. We focus on three widely used fairness metrics--equal opportunity, predictive equality, and predictive parity--and demonstrate the effectiveness of our approach in improving their estimates on synthetic data. Finally, we apply our bias mitigation approach to clinical genetics and study the fairness of pathogenicity predictors across ancestral groups.
HiDF: A Human-Indistinguishable Deepfake Dataset
Authors: Chaewon Kang, Seoyoon Jeong, Jonghyun Lee, Daejin Choi, Simon S. Woo, Jinyoung Han
Venue: KDD
Year: 2025

The rapid development and prevalence of generative AI have made it easy for people to create high-quality deepfake images and videos, but their abuses have also increased exponentially. To mitigate potential social disruption, it is crucial to quickly detect the authenticity of each deepfake content hidden in a sea of information. While researchers have worked on developing deep learning-based methods, the deepfake datasets utilized in these studies are far from the real world in terms of their qualities; most popular deepfake datasets are human-distinguishable. To address this problem, we present a novel deepfake dataset, HiDF, a high-quality and human-indistinguishable deepfake dataset consisting of 62K images and 8K videos. HiDF is a meticulously curated dataset that includes diverse subjects that have undergone rigorous quality checks. A comparison of the quality between HiDF and existing deepfake datasets demonstrates that HiDF is human-indistinguishable. Hence, it can be a valuable benchmark dataset for deepfake detection tasks. Data and code (https://github.com/DSAIL-SKKU/HiDF) are publicly available for future deepfake detection research.
CausalBench: Causal Learning Research Streamlined
Authors: Ahmet Kapkiç, Pratanu Mandal, Abhinav Gorantla, Shu Wan, Ertugrul Çoban, Paras Sheth, Huan Liu, K. Selçuk Candan
Venue: KDD
Year: 2025

Recent advances in causal machine learning introduced a plethora of new causal discovery and causal inference models to tackle decision support problems. Yet, these models exhibit different performance when they train on different data, and even different hardware/software platforms, making it challenging for users to select the appropriate setup pertinent to their specific problem instance. The situation is complicated by the fact that, until recently, the field lacked a unified, publicly available, and configurable platform that supports all major causal inference tasks, including causal discovery, causal effect estimation, and causal inference. CausalBench is a comprehensive benchmarking tool for causal machine learning that facilitates accurate and reproducible benchmarking of causal models across metrics and deployment contexts and helps users to select the most appropriate set up (such as hyper-parameter configuration) for the specific problem setting. This tutorial is intended to familiarize attendees from diverse backgrounds, who are interested in causal learning models and with the capabilities of CausalBench. The tutorial begins with an introduction to ''causality'' and causal machine learning, and then provides hands-on experience with CausalBench to equip attendees with the knowledge necessary to utilize CausalBench for their causal learning problems.
One-shot Multi-view Visual Conversational Recommendation
Authors: Noriaki Kawamae
Venue: KDD
Year: 2025

We propose One-Shot Multi-view Visual Conversational Recommendation (OSMVR), a novel framework to tackle key challenges in personalized recommendation systems from minimal user input. OSMVR addresses three critical issues: the Cold Start problem, the lack of transparent justifications, and the difficulty of accurately estimating nuanced user preferences. Unlike multi-turn systems, OSMVR reduces user burden by structuring the recommendation process into two phases-Preference Estimation and Recommendation Generation-within a single conversational round. It leverages a multi-view approach that enables one-shot preference inference and multi-perspective recommendation generation. To support this, we introduce View Effective Fine-Tuning (VEFT), a novel method that enhances the reasoning capabilities of Large Language Models (LLMs) and Vision-Language Models (VLMs) for one-shot, multi-view preference inference and recommendation. VEFT dynamically reconfigures subnetworks and soft prompts to simulate expert roles and enable perspective-aware reasoning, capturing diverse aspects of user intent. This lightweight, one shot process can be executed on client devices, ensuring privacy preserving personalization. In the Preference Estimation phase, users provide minimal input, such as text or images of preferred items. OSMVR, using VEFT, infers user preferences without historical data and presents these preferences from multiple viewpoints, inviting user feedback. In the subsequent Recommendation Generation phase, OSMVR generates multi-view recommendations with expert-informed, transparent justifications derived from the inferred preferences. Experimental results show that VEFT significantly improves one shot preference estimation and justification quality, even with limited data. OSMVR proves to be a robust and promising framework for personalized, multimodal recommendation systems, offering a solution that reduces user effort while enhancing satisfaction.
Knowledge-Aligned Domain Shift Tuning for Efficient Adaptation in Large Language Models
Authors: Noriaki Kawamae
Venue: KDD
Year: 2025

This paper introduces the Lottery Hedge Fund Hypothesis (LHFH) and proposes Knowledge-Aligned Domain Adaptation (KADA), a novel framework for addressing domain discrepancies between source and target domains. While LLMs demonstrate strong generalization across tasks, their performance often degrades on domain-specific tasks requiring specialized knowledge due to a mismatch in domain-specific knowledge distributions. KADA is motivated by LHFH, which posits that models can be viewed as a mixture of knowledge-specialized subnetworks, where domain differences correspond to the weights of these subnetworks. In contrast to conventional domain adaptation methods such as adapters or PEFT, which statically adjust model parameters, this perspective leads to a dynamic adaptation approach: selectively reweighting and activating relevant subnetworks aligned with the target domain. To realize this, KADA introduces two key components: the Knowledge Steering Layer (KSL) - a lightweight, dynamic module that routes input representations through expert subnetworks - and Knowledge Distribution Modeling (KDM) - a training objective that aligns source and target knowledge distributions by regulating routing entropy and balance. These components enable KADA to fine-tune only the subnetworks and routing parameters, avoiding full model retraining and significantly reducing computational overhead. Empirical results demonstrate that KADA effectively bridges domain gaps, achieving competitive or superior performance compared to baseline PEFT methods, even in low-resource settings. KADA offers a scalable and theoretically grounded solution for efficient domain adaptation in LLMs.
Finding Repeated Structures in Time Series: Algorithms and Applications: A Unifying View of Time Series Motifs/Shapelets/Chains and Similar Primitives
Authors: Eamonn J. Keogh
Venue: KDD
Year: 2025

Repeated structures in time series (time series motifs) are interesting in their own right and are often used in downstream algorithms for tasks as diverse as classification, clustering, rule-discovery, segmentation, summarization, compression and anomaly detection. If a structure is repeated, that hints at some mechanism of conservation, and the discovery of conserved structure is one of the most basic tools/goals of science. In this survey (which is a companion to a tutorial) I show the two key ideas needed to do successful time series motif discovery. First, making a computationally hard problem tractable; by the use of anytime algorithms, contract algorithms and specialist hardware. I will further review work on how to obtain more meaningful results by considering additional constraints on the returned patterns. For example, class conditional motifs (i.e. shapelets), motifs with a drift (i.e. time series chains), motifs that exist in two or more time series (motif-joins, consensus motifs), range motifs, KNN motifs etc. The companion tutorial is illustrated with novel interesting examples from science, industry, entertainment and medicine. Moreover, the tutorial slides will contain code snippets and a data archive that will allow the community to reproduce all the results and then generalize them to their own domain of interest.
STARLINE: Contrastive Learning with Modality-Aware Graph Refinement for Effective Multimedia Recommendation
Authors: Taeri Kim, Sohee Ban, Hyunjoon Kim, Sang-Wook Kim
Venue: KDD
Year: 2025

Beyond using multimodal features of items in addition to user-item interactions, researchers have additionally utilized Contrastive Learning (CL) in recent multimedia recommender systems to highly alleviate the data sparsity problem. CL-based methods generate at least two embeddings (i.e., views) for each instance and enrich the information of each instance from various perspectives via the views, thereby alleviating the data sparsity problem. Therefore, CL-based methods have focused on generating views that effectively represent the characteristics of each instance for their downstream tasks. Similarly, CL-based multimedia recommender systems have made efforts to effectively generate their user/item views by leveraging items' multimodal features. However, we point out the following two limitations that they have overlooked in generating their views: (1) they either have not attempted to identify the influence of each modality feature of an item on user-item interactions, or have identified it by randomly masking or dropping user-item interactions, and (2) they have not attempted to identify non-interactions likely to result in interactions in the future. To overcome these limitations, we propose a novel multimedia recommendation framework, named STARLINE, utilizing contraSTive leARning with modaLIty-aware graph refiNEment. Extensive experiments on five real-world datasets validate the effectiveness and validity of STARLINE, especially showing consistently higher accuracy by up to 13.24% compared to the best competitor.
Delving into Instance-Dependent Label Noise in Graph Data: A Comprehensive Study and Benchmark
Authors: Suyeon Kim, SeongKu Kang, Dongwoo Kim, Jungseul Ok, Hwanjo Yu
Venue: KDD
Year: 2025

Graph Neural Networks (GNNs) have achieved state-of-the-art performance in node classification tasks but struggle with label noise in real-world data. Existing studies on graph learning with label noise commonly rely on class-dependent label noise, overlooking the complexities of instance-dependent noise and falling short of capturing real-world corruption patterns. We introduce BeGIN (Benchmarking for Graphs with Instance-dependent Noise), a new benchmark that provides realistic graph datasets with various noise types and comprehensively evaluates noise-handling strategies across GNN architectures, noisy label detection, and noise-robust learning. To simulate instance-dependent corruptions, BeGIN introduces algorithmic methods and LLM-based simulations. Our experiments reveal the challenges of instance-dependent noise, particularly LLM-based corruption, and underscore the importance of node-specific parameterization to enhance GNN robustness. By comprehensively evaluating noise-handling strategies, BeGIN provides insights into their effectiveness, efficiency, and key performance factors. We expect that BeGIN will serve as a valuable resource for advancing research on label noise in graphs and fostering the development of robust GNN training methods. The code is available at https://github.com/kimsu55/BeGIN.
Lost in Sequence: Do Large Language Models Understand Sequential Recommendation?
Authors: Sein Kim, Hongseok Kang, Kibum Kim, Jiwan Kim, Donghyun Kim, Minchul Yang, Kwangjin Oh, Julian J. McAuley, Chanyoung Park
Venue: KDD
Year: 2025

Large Language Models (LLMs) have recently emerged as promising tools for recommendation thanks to their advanced textual understanding ability and context-awareness. Despite the current practice of training and evaluating LLM-based recommendation (LLM4Rec) models under a sequential recommendation scenario, we found that whether these models understand the sequential information inherent in users' item interaction sequences has been largely overlooked. In this paper, we first demonstrate through a series of experiments that existing LLM4Rec models do not fully capture sequential information both during training and inference. Then, we propose a simple yet effective LLM-based sequential recommender, called LLM-SRec, a method that enhances the integration of sequential information into LLMs by distilling the user representations extracted from a pre-trained CF-SRec model into LLMs. Our extensive experiments show that LLM-SRec enhances LLMs' ability to understand users' item interaction sequences, ultimately leading to improved recommendation performance. Furthermore, unlike existing LLM4Rec models that require fine-tuning of LLMs, LLM-SRec achieves state-of-the-art performance by training only a few lightweight MLPs, highlighting its practicality in real-world applications. Our code is available at https://github.com/Sein-Kim/LLM-SRec.
Accurate Graph-based Multi-Positive Unlabeled Learning via Disentangled Multi-view Feature Propagation
Authors: Junghun Kim, Hoyoung Yoon, Ka Hyun Park, U Kang
Venue: KDD
Year: 2025

How can we classify graph-structured data with labels of only positive classes? Graph-based MPU learning is to train a classifier where only a few nodes of positive classes are labeled and the others remain unlabeled; i.e., negative labels are completely absent. This scenario is deeply connected to real-world problems such as classifying multiple types of cyberattackers (e.g., DDoS, phishing) in network graphs where explicit labels for normal users are unavailable since the undetected cyberattackers disguise themselves as normal users. The main challenge lies in the connections between nodes of different classes, which cause the learned features of positive and unlabeled nodes to become indistinguishable. This issue is particularly severe for negative nodes as there are no observed labels to guide them in MPU settings. In this paper, we propose D-MVP (Disentangled Multi-View Propagation), an accurate method for graph-based MPU learning. D-MVP disentangles feature propagation into multiple views by assigning distinct weights to each view and aggregating information differently based on these weights. This makes the node classifier learn information that distinguishes between multiple positive classes while simultaneously capturing shared features among them, thereby effectively identifying negative classes that lack these shared characteristics. Extensive experiments on real-world datasets show that D-MVP achieves the best performance.
'Oh LLM, I'm Asking Thee, Please Give Me a Decision Tree': Zero-Shot Decision Tree Induction and Embedding with Large Language Models
Authors: Ricardo Knauer, Mario Koddenbrock, Raphael Wallsberger, Nicholas M. Brisson, Georg N. Duda, Deborah Falla, David W. Evans, Erik Rodner
Venue: KDD
Year: 2025

Large language models (LLMs) provide powerful means to leverage prior knowledge for predictive modeling when data is limited. In this work, we demonstrate how LLMs can use their compressed world knowledge to generate intrinsically interpretable machine learning models, i.e., decision trees, without any training data. We find that these zero-shot decision trees can even surpass data-driven trees on some small-sized tabular datasets and that embeddings derived from these trees perform better than data-driven tree-based embeddings on average. Our decision tree induction and embedding approaches can therefore serve as new knowledge-driven baselines for data-driven machine learning methods in the low-data regime. Furthermore, they offer ways to harness the rich world knowledge within LLMs for tabular machine learning tasks. Our code and results are available at https://github.com/ml-lab-htw/llm-trees.
Simulated Infectious Diseases Datasets with Controlled Data Bias
Authors: Ruochen Kong, Taylor Anderson, Matthew Scotch, David J. Heslop, Yonchanok Khaokaew, Hao Xue, Li Xiong, Chandini Raina MacIntyre, Flora D. Salim, Andreas Züfle
Venue: KDD
Year: 2025

Massive datasets related to infectious diseases became available after the COVID-19 pandemic, supporting data-driven approaches in modeling and forecasting infectious diseases. However, these approaches are known to exacerbate data biases present in the training data such as having certain demographic groups being over or underrepresented in the data. Such data collection biases may propagate through the modeling and prediction pipelines to decision-making, and the consequences are relatively unknown. Therefore, efforts are needed to understand how data collection bias affects data-driven infectious disease models. This datasets and benchmarks paper provides a suite of datasets, each corresponding to a simulated disease spread among a population of 5000 simulated agents over 90 days in Atlanta and San Francisco. For each dataset, we provide not only the full (simulated ground truth) of the disease spread in terms of when, where, and by whom the disease spreads, but also information on which cases are observed when different types and degrees of data collection bias are applied. The agents' characteristics, check-ins, and social network data are also available to support downstream tasks. Additionally, we also describe how to use the simulation to re-generate the data and to generate new datasets in different regions and with different parameters. With the provided datasets and the simulation tools, researchers studying the spread of infectious diseases may better understand, account for, and correct the systematic bias caused by the inherent real-world data bias, and hence improve the prediction of infectious diseases.
LH-Mix: Local Hierarchy Correlation Guided Mixup over Hierarchical Prompt Tuning
Authors: Fanshuang Kong, Richong Zhang, Ziqiao Wang
Venue: KDD
Year: 2025

Hierarchical text classification (HTC) aims to assign one or more labels in the hierarchy for each text. Many methods represent this structure as a global hierarchy, leading to redundant graph structures. To address this, incorporating a text-specific local hierarchy is essential. However, existing approaches often model this local hierarchy as a sequence, focusing on explicit parent-child relationships while ignoring implicit correlations among sibling/peer relationships. In this paper, we first integrate local hierarchies into a manual depth-level prompt to capture parent-child relationships. We then apply Mixup to this hierarchical prompt tuning scheme to improve the latent correlation within sibling/peer relationships. Notably, we propose a novel Mixup ratio guided by local hierarchy correlation to effectively capture intrinsic correlations. This Local Hierarchy Mixup (LH-Mix) model demonstrates remarkable performance across three widely-used datasets.
Cross-Validation for Longitudinal Datasets with Unstable Correlations
Authors: Meera Krishnamoorthy, Michael W. Sjoding, Jenna Wiens
Venue: KDD
Year: 2025

Cross-validation (CV) approaches are widely used in machine learning model development. They select hyperparameters associated with models with high average performance across multiple folds sampled from training data. Popular CV approaches do this sampling either randomly (random CV) or temporally (block CV). Averaging performance across multiple folds is intended to result in robust models; however, it can mask a model's reliance on unstable correlations - transient relationships between inputs and outputs that do not hold over time - which can make a model fail over time. In this paper, we show, both theoretically and empirically, that random and block CV often select hyperparameters corresponding to models that rely on unstable correlations over stable correlations. In light of this shortcoming, we propose a novel CV approach that is more likely to result in models that rely on stable correlations over unstable correlations, compared to random and block CV. Our proposed approach leverages the fact that when a model relies on features whose relationship with the outcome is stable, random and block CV result in the same estimated model performance; it does not matter how you split the training data into CV folds. However, when a model relies on features whose relationship with the outcome is unstable, random and block CV result in different estimates of model performance. We use this difference as a signal that a model relies on an unstable correlation and propose a model selection technique that selects hyperparameters that minimize this difference. When combined with standard model performance criteria, our approach results in models with higher average and more stable performance over time compared to random and block CV. Applied to the task of predicting 5-year survival among individuals with lung cancer, our approach leads to models that perform better in the long term compared to the next best approach (0.850, IQR: [0.824, 0.853] vs. 0.804, IQR: [0.753, 0.850]). In high-stakes dynamic domains like healthcare, where good performance over time is crucial, our approach can help result in models that perform consistently well despite unstable correlations in the training set. Code to implement our approach and reproduce all experiments in the paper is available at https://github.com/mld3/cv_unstable_correlations.