OpenRaiser/Intern-Atlas huggingface dataset:
State: A robust ate estimator of heavy-tailed metrics for variance reduction in online controlled experiments
Authors: Hao Zhou 0016, Kun Sun, Shaoming Li, Yangfeng Fan, Guibin Jiang, Jiaqi Zheng 0001, Tao Li
Venue: Conference KDD 2024
Year: 2024

Online controlled experiments play a crucial role in enabling data-driven decisions across a wide range of companies. Variance reduction is an effective technique to improve the sensitivity of experiments, achieving higher statistical power while using fewer samples and shorter experimental periods. However, typical variance reduction methods (e.g., regression-adjusted estimators) are built upon the intuitional assumption of Gaussian distributions and cannot properly characterize the real business metrics with heavy-tailed distributions. Furthermore, outliers diminish the correlation between pre-experiment covariates and outcome metrics, greatly limiting the effectiveness of variance reduction. In this paper, we develop a novel framework that integrates the Student's t-distribution with machine learning tools to fit heavy-tailed metrics and construct a robust average treatment effect estimator in online controlled experiments, which we call STATE. By adopting a variational EM method to optimize the loglikehood function, we can infer a robust solution that greatly eliminates the negative impact of outliers and achieves significant variance reduction. Moreover, we extend the STATE method from count metrics to ratio metrics by utilizing linear transformation that preserves unbiased estimation, whose variance reduction is more complex but less investigated in existing works. Finally, both simulations on synthetic data and long-term empirical results on Meituan experiment platform demonstrate the effectiveness of our method. Compared with the state-of-the-art estimators (CUPAC/MLRATE), STATE achieves over 50% variance reduction, indicating it can reach the same statistical power with only half of the observations, or half the experimental duration.
CAP: Causal air quality index prediction under interference with unmeasured confounding
Authors: Huayi Yang, Chunyuan Zheng, Guorui Liao, Shanshan Huang, Jun Liao, Zhili Gong, Haoxuan Li, Li Liu
Venue: Conference WWW 2025
Year: 2025

A significant challenge in air quality index (AQI) prediction is to accurately evaluate the potential outcomes after conducting interventions in pollutant factors such as industrial emissions for each enterprise. Existed methods often suffer from spurious correlations caused by unmeasured confounders and are lack of interpretability of the model, leading to sub-optimal prediction performance. This motivates us to propose a causal AQI prediction framework (CAP) that employs a structural causal model (SCM) to characterize the causal structural variability of various AQI factors for robust AQI prediction. Specifically, we employ the front-door adjustment to explicitly eliminate unmeasured confounders by intervening in industrial emissions from the target enterprise. Meanwhile, we take industrial emissions of neighboring enterprises into account when intervening in the target enterprise and simulate the dispersion of industrial emissions through a Gaussian plume model based on meteorological factors. Experiments on two real-world datasets validate the superior performance of our model on AQI prediction compared to the state-of-the-art baselines.
Htfllib: A comprehensive heterogeneous federated learning library and benchmark
Authors: Jianqing Zhang, Xinghao Wu, Yanbing Zhou, Xiaoting Sun, Qiqi Cai, Yang Liu, Yang Hua, Zhenzhe Zheng, Jian Cao, Qiang Yang
Venue: Conference KDD 2025
Year: 2025

As AI evolves, collaboration among heterogeneous models helps overcome data scarcity by enabling knowledge transfer across institutions and devices. Traditional Federated Learning (FL) only supports homogeneous models, limiting collaboration among clients with heterogeneous model architectures. To address this, Heterogeneous Federated Learning (HtFL) methods are developed to enable collaboration across diverse heterogeneous models while tackling the data heterogeneity issue at the same time. However, a comprehensive benchmark for standardized evaluation and analysis of the rapidly growing HtFL methods is lacking. Firstly, the highly varied datasets, model heterogeneity scenarios, and different method implementations become hurdles to making easy and fair comparisons among HtFL methods. Secondly, the effectiveness and robustness of HtFL methods are under-explored in various scenarios, such as the medical domain and sensor signal modality. To fill this gap, we introduce the first Heterogeneous Federated Learning Library (HtFLlib), an easy-to-use and extensible framework that integrates multiple datasets and model heterogeneity scenarios, offering a robust benchmark for research and practical applications. Specifically, HtFLlib integrates (1) 12 datasets spanning various domains, modalities, and data heterogeneity scenarios; (2) 40 model architectures, ranging from small to large, across three modalities; (3) a modularized and easy-to-extend HtFL codebase with implementations of 10 representative HtFL methods; and (4) systematic evaluations in terms of accuracy, convergence, computation costs, and communication costs. We emphasize the advantages and potential of state-of-the-art HtFL methods and hope that HtFLlib will catalyze advancing HtFL research and enable its broader applications. The code is released at https://github.com/TsingZ0/HtFLlib.
Frequant: A reinforcement-learning based adaptive portfolio optimization with multi-frequency decomposition
Authors: Jihyeong Jeon, Jiwon Park, Chanhee Park, U Kang
Venue: Conference KDD 2024
Year: 2024

How can we leverage inherent frequency features of stock signals for effective portfolio optimization? Portfolio optimization in the domain of finance revolves around strategically allocating assets to maximize returns. Recent advancements highlight the efficacy of deep learning and reinforcement learning (RL) in capturing temporal asset patterns for portfolio optimization. However, previous methodologies focusing on time-domain often fail to detect sudden market shifts and abrupt events because their models are overly tailored to prevalent patterns, resulting in significant losses.
Cognify: Supercharging gen-ai workflows with hierarchical autotuning
Authors: Zijian He, Reyna Abhyankar, Vikranth Srivatsa, Yiying Zhang
Venue: Conference KDD 2025
Year: 2025

Today's gen-AI workflows that involve multiple ML model calls, tool/API calls, data retrieval, or generic code execution are often tuned manually in an ad-hoc way that is both time-consuming and error-prone. In this paper, we propose a systematic approach for automatically tuning gen-AI workflows. Our key insight is that gen-AI workflows can benefit from structure, operator, and prompt changes, but unique properties of gen-AI workflows require new optimization techniques. We propose AdaSeek, an adaptive hierarchical search algorithm for autotuning gen-AI workflows. AdaSeek organizes workflow tuning methods into different layers based on the user-specified total search budget and distributes the budget across different layers based on the complexity of each layer. During its hierarchical search, AdaSeek redistributes the search budget from less useful to more promising tuning configurations based on workflow-level evaluation results. We implement AdaSeek in a workflow autotuning framework called Cognify and evaluate Cognify using six types of workflows such as RAG-based QA and text-to-SQL transformation. Overall, Cognify improves these workflows' generation quality by up to 2.8×, reduces execution monetary cost by up to 10×, and reduces end-to-end latency by 2.7×.
Oneke: A dockerized schema-guided llm agent-based knowledge extraction system
Authors: Yujie Luo, Xiangyuan Ru, Kangwei Liu, Lin Yuan, Mengshu Sun, Ningyu Zhang, Lei Liang, Zhiqiang Zhang, Jun Zhou, Lanning Wei, Da Zheng, Haofen Wang, Huajun Chen
Venue: Conference WWW 2025
Year: 2025

We introduce OneKE, a dockerized schema-guided knowledge extraction system, which can extract knowledge from the Web and raw PDF Books, and support various domains (science, news, etc.). Specifically, we design OneKE with multiple agents and a configure knowledge base. Different agents perform their respective roles, enabling support for various extraction scenarios. The configure knowledge base facilitates schema configuration, error case debugging and correction, further improving the performance. Empirical evaluations on benchmark datasets demonstrate OneKE's efficacy, while case studies further elucidate its adaptability to diverse tasks across multiple domains, highlighting its potential for broad applications. We have open-sourced the Code at https://github.com/zjunlp/OneKE and released a Video at http://oneke.openkg.cn/demo.mp4.
Kg4ex: An explainable knowledge graph-based approach for exercise recommendation //
Authors: Quanlong Guan, Fang Xiao, Xinghe Cheng, Liangda Fang, Ziliang Chen, Guanliang Chen, Weiqi Luo
Venue: Conference CIKM 2023
Year: 2023

Effective exercise recommendation is crucial for guiding students' learning trajectories and fostering their interest in the subject matter. However, the vast exercise resource and the varying learning abilities of individual students pose a significant challenge in selecting appropriate exercise questions. Collaborative filtering-based methods often struggle with recommending suitable exercises, while deep learning-based methods lack explanation, limiting their practical adoption. To address these limitations, this paper proposes KG4Ex, a knowledge graph-based exercise recommendation method. KG4Ex facilitates the matching of diverse students with suitable exercises while providing recommendation reasons. Specifically, we introduce a feature extraction module to represent students' learning states and construct a knowledge graph for exercise recommendation. This knowledge graph comprises three key entities (knowledge concepts, students, and exercises) and their interrelationships, and can be used to recommend suitable exercises. Extensive experiments on three real-world datasets and expert interviews demonstrate the superiority of KG4Ex over existing baseline methods and highlight its strong explainability.
Reverse testing: An efficient framework to select amongst classifiers under sample selection bias
Authors: Wei Fan, Ian Davidson
Venue: Conference KDD 2006
Year: 2006

One of the most important assumptions made by many classification algorithms is that the training and test sets are drawn from the same distribution, i.e., the so-called "stationary distribution assumption" that the future and the past data sets are identical from a probabilistic standpoint. In many domains of real-world applications, such as marketing solicitation, fraud detection, drug testing, loan approval, sub-population surveys, school enrollment among others, this is rarely the case. This is because the only labeled sample available for training is biased in different ways due to a variety of practical reasons and limitations. In these circumstances, traditional methods to evaluate the expected generalization error of classification algorithms, such as structural risk minimization, ten-fold cross-validation, and leave-one-out validation, usually return poor estimates of which classification algorithm, when trained on biased dataset, will be the most accurate for future unbiased dataset, among a number of competing candidates. Sometimes, the estimated order of the learning algorithms' accuracy could be so poor that it is not even better than random guessing. Therefore,a method to determine the most accurate learner is needed for data mining under sample selection bias for many real-world applications. We present such an approach that can determine which learner will perform the best on an unbiased test set, given a possibly biased training set, in a fraction of the computational cost to use cross-validation based approaches.
Learning flexible time-windowed granger causality integrating heterogeneous interventional time series data
Authors: Ziyi Zhang, Shaogang Ren, Xiaoning Qian, Nick Duffield
Venue: Conference KDD 2024
Year: 2024

Granger causality, commonly used for inferring causal structures from time series data, has been adopted in widespread applications across various fields due to its intuitive explainability and high compatibility with emerging deep neural network prediction models. To alleviate challenges in better deciphering causal structures unambiguously from time series, the use of interventional data has become a practical approach. However, existing methods have yet to be explored in the context of imperfect interventions with unknown targets, which are more common and often more beneficial in a wide range of real-world applications. Additionally, the identifiability issues of Granger causality with unknown interventional targets in complex network models remain unsolved. Our work presents a theoretically-grounded method that infers Granger causal structure and identifies unknown targets by leveraging heterogeneous interventional time series data. We further illustrate that learning Granger causal structure and recovering interventional targets can mutually promote each other. Comparative experiments demonstrate that our method outperforms several robust baseline methods in learning Granger causal structure from interventional time series data.
Coded exposure deblurring: Optimized codes for psf estimation and invertibility
Authors: Amit K. Agrawal, Yi Xu
Venue: Conference CVPR 2009
Year: 2009

We consider the problem of single image object motion deblurring from a static camera. It is well-known that deblurring of moving objects using a traditional camera is ill-posed, due to the loss of high spatial frequencies in the captured blurred image. A coded exposure camera modulates the integration pattern of light by opening and closing the shutter within the exposure time using a binary code. The code is chosen to make the resulting point spread function (PSF) invertible, for best deconvolution performance. However, for a successful deconvolution algorithm, PSF estimation is as important as PSF invertibility. We show that PSF estimation is easier if the resulting motion blur is smooth and the optimal code for PSF invertibility could worsen PSF estimation, since it leads to non-smooth blur. We show that both criterions of PSF invertibility and PSF estimation can be simultaneously met, albeit with a slight increase in the deconvolution noise. We propose design rules for a code to have good PSF estimation capability and outline two search criteria for finding the optimal code for a given length. We present theoretical analysis comparing the performance of the proposed code with the code optimized solely for PSF invertibility. We also show how to easily implement coded exposure on a consumer grade machine vision camera with no additional hardware. Real experimental results demonstrate the effectiveness of the proposed codes for motion deblurring.
Eulerformer: Sequential user behavior modeling with complex vector attention
Authors: Zhen Tian, Wayne Xin Zhao, Changwang Zhang, Xin Zhao, Zhongrui Ma, Ji-Rong Wen
Venue: Conference SIGIR 2024
Year: 2024

To capture user preference, transformer models have been widely applied to model sequential user behavior data. The core of transformer architecture lies in the self-attention mechanism, which computes the pairwise attention scores in a sequence. Due to the permutation-equivariant nature, positional encoding is used to enhance the attention between token representations. In this setting, the pairwise attention scores can be derived by both semantic difference and positional difference. However, prior studies often model the two kinds of difference measurements in different ways, which potentially limits the expressive capacity of sequence modeling. To address this issue, this paper proposes a novel transformer variant with complex vector attention, named EulerFormer, which provides a unified theoretical framework to formulate both semantic difference and positional difference. The EulerFormer involves two key technical improvements. First, it employs a new transformation function for efficiently transforming the sequence tokens into polar-form complex vectors using Euler's formula, enabling the unified modeling of both semantic and positional information in a complex rotation form. Secondly, it develops a differential rotation mechanism, where the semantic rotation angles can be controlled by an adaptation function, enabling the adaptive integration of the semantic and positional information according to the semantic contexts. Furthermore, a phase contrastive learning task is proposed to improve the isotropy of contextual representations in EulerFormer. Our theoretical framework possesses a high degree of completeness and generality (e.g., RoPE can be instantiated as a special case). It is more robust to semantic variations and possesses more superior theoretical properties (e.g., long-term decay) in principle. Extensive experiments conducted on four public datasets demonstrate the effectiveness and efficiency of our approach. Our code is available at https://github.com/RUCAIBox/EulerFormer.
Rotative factorization machines
Authors: Zhen Tian 0001, Yuhong Shi, Xiangkun Wu, Wayne Xin Zhao, Ji-Rong Wen
Venue: Conference KDD 2024
Year: 2024

Feature interaction learning (FIL) focuses on capturing the complex relationships among multiple features for building predictive models, which is widely used in real-world tasks. Despite the research progress, existing FIL methods suffer from two major limitations. Firstly, they mainly model the feature interactions within a bounded order (e.g., small integer order) due to the exponential growth of the interaction terms. Secondly, the interaction order of each feature is often independently learned, which lacks the flexibility to capture the feature dependencies in varying contexts.
Navigating the Deployment Dilemma and Innovation Paradox: Open-Source versus Closed-Source Models
Authors: Yanxuan Wu, Haihan Duan, Xitong Li, Xiping Hu
Venue: Conference WWW 2025
Year: 2025

Recent advances in Artificial Intelligence (AI) have introduced a popular paradigm in Machine Learning (ML) model development: pre-training and domain adaptation. As both closed-source developers and open-source community lead in pre-training foundation models, domain deployers face the dilemma about whether to use closed-source models via API access or to host open-source models on proprietary hardware. Using closed-source models incurs recurring costs, while hosting open-source models requires substantial hardware investments and may lead to potentially lagging advancements. This paper presents a game-theoretical model to examine the economic incentives behind the deployment choice and the impact of open-source engagement strategies on technological innovation. We find that deployers consistently opt for closed-source APIs when the open-source community engages reactively by maintaining a fixed performance ratio relative to closed-source advancements. However, open-source models can become preferable when a proactive open-source community produces high-performance models independently. Furthermore, we identify conditions under which the engagement and competitiveness of the open-source community can either foster or inhibit technological progress. These insights offer valuable implications for market regulation and the future of technology innovation.
Information diffusion prediction via cascade-retrieved in-context learning
Authors: Ting Zhong, Jienan Zhang, Zhangtao Cheng, Fan Zhou, Xueqin Chen
Venue: Conference SIGIR 2024
Year: 2024

Information diffusion prediction, which aims to infer the infected behavior of individual users during information spread, is critical for understanding the dynamics of information propagation and users' influence on online social media. To date, existing methods either focus on capturing limited contextual information from a single cascade, overlooking the potentially complex dependencies across different cascades, or they are committed to improving model performance by using intricate technologies to extract additional features as supplements to user representations, neglecting the drift of model performance across different platforms. To address these limitations, we propose a novel framework called CARE (CAscade-REtrieved In-Context Learning) inspired by the concept of in-context learning in LLMs. Specifically, CARE first constructs a prompts pool derived from historical cascades, then utilizes ranking-based search engine techniques to retrieve prompts with similar patterns based on the query. Moreover, CARE also introduces two augmentation strategies alongside social relationship enhancement to enrich the input context. Finally, the transformed query-cascade representation from a GPT-type architecture is projected to obtain the prediction. Experiments on real-world datasets from various platforms show that CARE outperforms state-of-the-art baselines in terms of effectiveness and robustness in information diffusion prediction.
Doctor specific tag recommendation for online medical record management
Authors: Yejing Wang, Shen Ge, Xiangyu Zhao 0001, Xian Wu 0001, Tong Xu 0001, Chen Ma 0001, Zhi Zheng 0008
Venue: Conference KDD 2023
Year: 2023

With the rapid growth of online medical platforms, more and more doctors are willing to manage and communicate with patients via online services. Considering the large volume and various patient conditions, identifying and classifying patients' medical records has become a crucial problem. To efficiently index these records, a common practice is to annotate them with semantically meaningful tags. However, manual labeling tags by doctors is impractical due to the possibility of thousands of tag candidates, which necessitates a tag recommender system. Due to the long tail distribution of tags and the dominance of low-activity doctors, as well as the unique uploaded medical records, this task is rather challenging. This paper proposes an efficient doctor specific tag recommendation framework for improved medical record management without side information. Specifically, we first utilize effective language models to learn the text representation. Then, we construct a doctor embedding learning module to enhance the recommendation quality by integrating implicit information within text representations and considering latent tag correlations to make more accurate predictions. Extensive experiment results demonstrate the effectiveness of our framework from the viewpoints of all doctors (20% improvement) or low-activity doctors (10% improvement).
Friend Recommendations with Self-Rescaling Graph Neural Networks
Authors: Xiran Song, Jianxun Lian, Hong Huang 0001, Mingqi Wu, Hai Jin 0001, Xing Xie 0001
Venue: Conference KDD 2022
Year: 2022

Friend recommendation service plays an important role in shaping and facilitating the growth of online social networks. Graph embedding models, which can learn low-dimensional embeddings for nodes in the social graph to effectively represent the proximity between nodes, have been widely adopted for friend recommendations. Recently, Graph Neural Networks (GNNs) have demonstrated superiority over shallow graph embedding methods, thanks to their ability to explicitly encode neighborhood context. This is also verified in our Xbox friend recommendation scenario, where some simplified GNNs, such as LightGCN and PPRGo, achieve the best performance. However, we observe that many GNN variants, including LightGCN and PPRGo, use a static and pre-defined normalizer in neighborhood aggregation, which is decoupled with the representation learning process and can cause the scale distortion issue. As a consequence, the true power of GNNs has not yet been fully demonstrated in friend recommendations.