OpenRaiser/Intern-Atlas huggingface dataset:
The 11th Mining and Learning from Time Series (MILETS): From Classical Methods to LLMs
Authors: Sanjay Purushotham, Dongjin Song, Qingsong Wen, Jun Huan, Yuxuan Liang, Cong Shen, Stefan Zohren, Yuriy Nevmyvaka
Venue: KDD
Year: 2025

Time series data is now pervasive across domains such as healthcare, finance, entertainment, and transportation, driven by advances in sensing technologies that enable continuous data collection. The resulting increase in data volume and complexity poses significant challenges to traditional analysis methods, calling for the development of advanced, interdisciplinary approaches to temporal data mining. This workshop aims to: (1) identify key challenges in learning from time series data, including irregular sampling, spatiotemporal dependencies, and uncertainty quantification; (2) explore recent advances in algorithmic, statistical, theoretical, and systems-based solutions-ranging from classical methods to emerging techniques involving large language models (LLMs); and (3) foster collaboration by highlighting open problems and novel research directions in time series analysis. Bridging theory and practice, the workshop provides a platform for researchers and practitioners from academia, industry, and government to exchange ideas, discuss technical challenges, and showcase practical applications. Contributions from related areas such as AI, machine learning, data science, and statistics are strongly encouraged.
Input Snapshots Fusion for Scalable Discrete-Time Dynamic Graph Neural Networks
Authors: QingGuo Qi, Hongyang Chen, Minhao Cheng, Han Liu
Venue: KDD
Year: 2025

In recent years, there has been a surge in research on dynamic graph representation learning, primarily focusing on modeling the evolution of temporal-spatial patterns in real-world applications. However, within the domain of discrete-time dynamic graphs, the exploration of temporal edges remains underexplored. Existing approaches often rely on additional sequential models to capture dynamics, leading to high computational and memory costs, particularly for large-scale graphs. To address this limitation, we propose the Input Snapshots Fusion based Dynamic Graph Neural Network (SFDyG), which combines Hawkes processes with graph neural networks to capture temporal and structural patterns in dynamic graphs effectively. By fusing multiple snapshots into a single temporal graph, SFDyG decouples computational complexity from the number of snapshots, enabling efficient full-batch and mini-batch training. Experimental evaluations on eight diverse dynamic graph datasets for future link prediction tasks demonstrate that SFDyG consistently outperforms existing methods.
Tackling the Length Barrier: Dynamic Context Browsing for Knowledge-Intensive Task
Authors: Hongjin Qian, Zheng Liu, Peitian Zhang, Kelong Mao, Yujia Zhou, Xu Chen, Zhicheng Dou
Venue: KDD
Year: 2025

Knowledge-intensive tasks often require complex reasoning and contextual understanding over long contexts. However, the learning and deployment of long-LLMs remains a challenging problem despite recent progresses. In this work, we propose that the short LLMs have great potentiality for solving knowledge-intensive tasks that have long context, i.e. they can be solved by purely working with oracle short-contexts within the input long-context. On top of this argument, we propose a framework called DCISO DynamiC knowledge-Intensive task S>Olver), which enables a short-LLM to address the knowledge-intensive tasks with long context via dynamic context browsing. In our framework, the short-LLM prompts itself to reason for two critical decisions: 1) how to access to the appropriate part of context within the input, 2) how to make effective use of the accessed context. By adaptively accessing and utilizing the context based on the presented tasks, DCISO can serve as a general framework to handle diversified knowledge-intensive long-context problems. We comprehensively evaluate different types of tasks from popular long-context benchmarks, where DCISO is able to achieve a substantially improved performance. Our codes will be released at this repository.
Adapting to Generalized Online Label Shift by Invariant Representation Learning
Authors: Yu-Yang Qian, Yi-Han Wang, Zhen-Yu Zhang, Yuan Jiang, Zhi-Hua Zhou
Venue: KDD
Year: 2025

The problem of online label shift, where label distribution evolves over time while the label-conditional density remains unchanged, has attracted increasing attentions. Although existing approaches have achieved sound theoretical guarantees and encouraging performance, the assumption of an unchanged conditional distribution may limit its application in broader tasks. In this paper, we investigate an extended variant named generalized online label shift (GOLS) problem, in which we relax the label shift assumption on the raw feature space and instead assume the existence of an unknown invariant representation such that conditional distribution of this representation given the label remains constant. To handle GOLS, our main idea involves capturing the inherently stable information from non-stationary streams, in the form of learning an invariant representation. Specifically, we design a novel objective to learn the invariant representation, which exploits the unique structure in GOLS. To optimize this objective, we propose an algorithm employing online ensemble paradigm to perform multi-resolution updates using various historical data windows, thereby enhancing the stability of the representation. This approach is theoretically guaranteed to achieve an optimal convergence rate. To improve the efficiency of the ensemble framework, we further propose a mask-based implementation for ensembling with DNNs. Experiments on benchmarks and real-world tasks validate the effectiveness of our approach.
Quantum Time-index Models with Reservoir for Time Series Forecasting
Authors: Wenbo Qiao, Jiaming Zhao, Peng Zhang
Venue: KDD
Year: 2025

The time-index models are a class of time series forecasting models that map time-index features to forecasts in continuous space. Compared to the historical-value models, the time-index models can avoid the effect of data sampling frequency and are usually more expressive. However, the vanilla deep time-index model is weak in modeling the high-frequency components of time series and often requires the introduction of many parameters to enhance the modeling capability. Moreover, the time-index model learns only a mapping relationship and ignores the sequence relationship between temporal features, leading to a weak extrapolation capability in the forecast horizon. In this paper, inspired by the ability of quantum implicit neural representations to model the high-frequency components of signals with fewer parameters, we propose Quantum Time-Index Models with Reservoir (QuantumTime). Specifically, we introduce variational quantum circuits to address the challenge of representing high-frequency components in time series. Then, we introduce a reservoir that empowers QuantumTime with powerful extrapolation capabilities by exploiting the rich dynamical properties of reservoir computing. Ultimately, experiments conducted on chaotic datasets and various real-world datasets demonstrate that QuantumTime achieves highly competitive results compared to the state-of-the-art deep time-index model while reducing training parameters by at least 95%. Our approach provides a paradigm for utilizing potential quantum advantage in practical tasks.
Finding Counterfactual Evidences for Node Classification
Authors: Dazhuo Qiu, Jinwen Chen, Arijit Khan, Yan Zhao, Francesco Bonchi
Venue: KDD
Year: 2025

Counterfactual learning is emerging as an important paradigm, rooted in causality, which promises to alleviate common issues of graph neural networks (GNNs), such as fairness and interpretability. However, as in many real-world application domains where conducting randomized controlled trials is impractical, one has to rely on available observational (factual) data to detect counterfactuals. In this paper, we introduce and tackle the problem of searching for counterfactual evidences for the GNN-based node classification task. A counterfactual evidence is a pair of nodes such that, regardless they exhibit great similarity both in the features and in their neighborhood subgraph structures, they are classified differently by the GNN. We develop effective and efficient search algorithms and a novel indexing solution that leverages both node features and structural information to identify counterfactual evidences, and generalizes beyond any specific GNN. Through various downstream applications, we demonstrate the potential of counterfactual evidences to enhance fairness and accuracy of GNNs.
DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting
Authors: Xiangfei Qiu, Xingjian Wu, Yan Lin, Chenjuan Guo, Jilin Hu, Bin Yang
Venue: KDD
Year: 2025

Multivariate time series forecasting is crucial for various applications, such as financial investment, energy management, weather forecasting, and traffic optimization. However, accurate forecasting is challenging due to two main factors. First, real-world time series often show heterogeneous temporal patterns caused by distribution shifts over time. Second, correlations among channels are complex and intertwined, making it hard to model the interactions among channels precisely and flexibly. In this study, we address these challenges by proposing a general framework called DUET, which introduces DU al clustering on the temporal and channel dimensions to Enhance multivariate Time series forecasting. First, we design a Temporal Clustering Module (TCM) that clusters time series into fine-grained distributions to handle heterogeneous temporal patterns. For different distribution clusters, we design various pattern extractors to capture their intrinsic temporal patterns, thus modeling the heterogeneity. Second, we introduce a novel Channel-Soft-Clustering strategy and design a Channel Clustering Module (CCM), which captures the relationships among channels in the frequency domain through metric learning and applies sparsification to mitigate the adverse effects of noisy channels. Finally, DUET combines TCM and CCM to incorporate both the temporal and channel dimensions. Extensive experiments on 25 real-world datasets from 10 application domains, demonstrate the state-of-the-art performance of DUET.
Practical Guidance and Tutorial on Incentivizing Reasoning in LLMs using Distillation and Reinforcement Learning
Authors: Zhaopeng Qiu, Jingqi Zhang, Shuang Yu, Shuai Zhang, Junjie Lai
Venue: KDD
Year: 2025

With reasoning models like DeepSeek-R1 and OpenAI's o1 demonstrating breakthrough capabilities in complex problem-solving, there is growing interest in the AI community about how to unlock similar capabilities in other large language models (LLMs). This hands-on tutorial dives into practical methods for building reasoning capabilities in LLMs through two primary approaches: knowledge distillation from advanced reasoning models and post-training with reinforcement learning techniques. Participants will learn how to transfer reasoning capabilities from cutting-edge models like DeepSeek-R1 into smaller LLMs such as Qwen and Llama, and then explore how reinforcement learning can take these capabilities even further. Through interactive Jupyter notebook, the participants will exercise through the entire process. By the end of this session, participants will be equipped with practical knowledge in how to incentivize reasoning capabilities into LLMs, understand how to use various frameworks for this task, and leave with hands-on experience that can be applied to their own projects. The related materials are available at https://zpqiu.github.io/reasoning-model-tutorial-kdd2025.
DarkDistill: Difficulty-Aligned Federated Early-Exit Network Training on Heterogeneous Devices
Authors: Lehao Qu, Shuyuan Li, Zimu Zhou, Boyi Liu, Yi Xu, Yongxin Tong
Venue: KDD
Year: 2025

Early-exit networks (EENs), which adapt their computational depths based on input samples, are widely adopted to accelerate inference in edge computing applications. The effectiveness of EENs relies on difficulty-aware training, which tailors shallow exits for simple samples and deep exits for complex ones. However, existing difficulty-aware training schemes assume centralized environments with sufficient data, which become invalid with real-world edge devices. In this paper, we explore difficulty-aware training in a federated manner, where EENs are collaboratively trained on heterogeneous devices. We observe the cross-model exit unalignment phenomenon, a unique problem when aggregating local EENs into a cohesive global model. To address this problem, we design a novel Difficulty-Aligned Reverse Knowledge Distillation scheme named DarkDistill that preserves the difficulty-specific specialization for aggregating heterogeneous local models. Instead of direct parameter averaging, it trains difficulty-conditional data generators, and selectively transfers generated knowledge of specific difficulty among matched exits of heterogeneous EENs. Evaluations show that DarkDistill outperforms the state-of-the-arts in both full-parameter and parameter-efficient fine-tuning of EENs.
Explainable LiDAR 3D Point Cloud Segmentation and Clustering for Detecting Airplane-Generated Wind Turbulence
Authors: Zhan Qu, Shuzhou Yuan, Michael Färber, Marius Brennfleck, Niklas Wartha, Anton Stephan
Venue: KDD
Year: 2025

Wake vortices-strong, coherent air turbulences created by aircrafts-pose a significant risk to aviation safety and therefore require accurate and reliable detection methods. In this paper, we present an advanced, explainable machine learning method that utilizes Light Detection and Ranging (LiDAR) data for effective wake vortex detection. Our method leverages a dynamic graph CNN (DGCNN) with semantic segmentation to partition a 3D LiDAR point cloud into meaningful segments. Further refinement is achieved through clustering techniques. A novel feature of our research is the use of a perturbation-based explanation technique, which clarifies the model's decision-making processes for air traffic regulators and controllers, increasing transparency and building trust. Our experimental results, based on measured and simulated LiDAR scans compared against four baseline methods, underscore the effectiveness and reliability of our approach. This combination of semantic segmentation and clustering for real-time wake vortex tracking significantly advances aviation safety measures, ensuring that these are both effective and comprehensible.
Anytime Algorithms for Approximate Functional Dependencies
Authors: Sanjivni Rana, Junya Ogawa, Suraj Shetiya, Senjuti Basu Roy, Gautam Das
Venue: KDD
Year: 2025

We propose a computational framework for identifying approximate functional dependencies (AFDs) in a relation, leveraging the frequency distribution information of individual attributes. This framework operates without requiring access to the full database, processing records one at a time as necessary. Our approach generalizes existing measures for quantifying errors in perfect dependencies and formalizes two primary problems: finding top-k AFDs and identifying all AFDs within a specified error threshold, ε. Our pro posed framework provides anytime solutions, meaning it returns results after processing each record. A key innovation of our work lies in effectively estimating error bounds of the candidate AFDs, which allows to produce anytime solutions. We present an exact algorithm that delivers precise solutions when possible. We also develop an algorithm that always returns a solution albeit with some imprecision in the output. We demonstrate the applicability of these algorithms under various data organization strategies, such as indexing by key or key-like attributes. Our experimental results, based on both real-world and synthetic datasets, validate the effectiveness of our approach and show that it outperforms state-of-the-art solutions.
Incorporating Retrieval-based Causal Learning with Information Bottlenecks for Interpretable Molecular Graph Learning
Authors: Jiahua Rao, Hanjing Lin, Jiancong Xie, Zhen Wang, Shuangjia Zheng, Yuedong Yang
Venue: KDD
Year: 2025

Graph Neural Networks (GNNs) have gained considerable traction for modeling molecular structures and predicting properties, but their interpretability remains a significant challenge in understanding chemical behaviors. Current interpretation methods often rely on post-hoc explanations, which aim to provide transparency in GNN decisions. However, these approaches struggle with interpreting complex subgraphs and fail to leverage explanations to enhance predictive capabilities. While transparent methods can enhance GNN predictions, they typically compromise on explanation precision. This limitation underscores the need for a new strategy that effectively integrates GNN explanations and predictions. In this study, we have developed a novel interpretable causal GNN framework that combines retrieval-based causal learning with Graph Information Bottleneck (GIB) theory. Our framework semi-parametrically identifies crucial subgraphs through GIB and compresses explanatory subgraphs using a causal module. The framework consistently outperformed state-of-the-art methods, achieving a 32.72% increase in precision for scientific explanation tasks involving diverse substructures. More importantly, the learned explanations were also shown to be able to improve GNN prediction performance. This advancement is particularly vital for molecular graph learning, as it addresses the critical need to interpret how molecular structures influence predicted properties, thereby aiding drug discovery and materials science by providing insights into chemical mechanisms.
Score-based Generative Modeling for Conditional Independence Testing
Authors: Yixin Ren, Chenghou Jin, Yewei Xia, Li Ke, Longtao Huang, Hui Xue, Hao Zhang, Jihong Guan, Shuigeng Zhou
Venue: KDD
Year: 2025

Determining conditional independence (CI) relationships between random variables is a fundamental yet challenging task in machine learning and statistics, especially in high-dimensional settings. Existing generative model-based CI testing methods, such as those utilizing generative adversarial networks (GANs), often struggle with undesirable modeling of conditional distributions and training instability, resulting in subpar performance. To address these issues, we propose a novel CI testing method via score-based generative modeling, which achieves precise Type I error control and strong testing power. Concretely, we first employ a sliced conditional score matching scheme to accurately estimate conditional score and use Langevin dynamics conditional sampling to generate null hypothesis samples, ensuring precise Type I error control. Then, we incorporate a goodness-of-fit stage into the method to verify generated samples and enhance interpretability in practice. We theoretically establish the error bound of conditional distributions modeled by score-based generative models and prove the validity of our CI tests. Extensive experiments on both synthetic and real-world datasets show that our method significantly outperforms existing state-of-the-art methods, providing a promising way to revitalize generative model-based CI testing.
Towards Understanding Link Predictor Generalizability Under Distribution Shifts
Authors: Jay Revolinsky, Harry Shomer, Jiliang Tang
Venue: KDD
Year: 2025

State-of-the-art link prediction (LP) models demonstrate impressive benchmark results. However, popular benchmark datasets often assume that training, validation, and testing samples are represen- tative of the overall dataset distribution. In real-world situations, this assumption is often incorrect; uncontrolled factors lead new dataset samples to come from a different distribution than train- ing samples. Additionally, the majority of recent work with graph dataset shift focuses on node- and graph-level tasks, largely ignor- ing link-level tasks. To bridge this gap, we introduce a novel splitting strategy, known as LPShift, which utilizes structural properties to induce a controlled distribution shift. We verify LPShift's effect through empirical evaluation of SOTA LP models on 16 LPShift variants of original dataset splits, with results indicating drastic changes to model performance. Additional experiments demon- strate graph structure has a strong influence on the success of current generalization methods. The source code is available here https://github.com/revolins/LPShift.
The Adaptive Use of Count-Min Sketch: What is Safe and What is Not?
Authors: Dragos-Florian Ristache, Krzysztof Onak
Venue: KDD
Year: 2025

Small-space frequency estimators play a crucial role in a multitude of settings related to both data science and machine learning in the context of big data processing. Many frequency estimators use internal randomness to compress the information about the frequencies of items to a small sketch that can be used to provide estimates. Historically, these types of estimators were designed without considering the danger of invalid estimates caused by their adaptive use. However, this kind of scenario naturally occurs when they are used as a subroutine in a more complicated algorithm or when an adversary maliciously attempts to corrupt estimates. The reason why the classic way of analyzing these types of algorithms does not provide satisfying guarantees is that it often assumes that the queries and updates are independent of the previously generated estimates. In the adaptive setting, each provided estimate has the potential of leaking information about the estimator's internal randomness, which can, in turn, be used to craft queries and updates on which the frequency estimator does not perform well. In this work, we consider one of the simplest such estimators, Count-Min Sketch, and demonstrate both positive and negative results. On the one hand, we show that Count-Min Sketch can be made resistant to adaptive attacks in both the random oracle model, which corresponds to cryptographically hard hash functions, and using universal hash functions if the domain size is in polynomial relationship with with the size of hash tables. On the other hand, we develop techniques for more efficient attacks on Count-Min Sketch than those known previously. First, we introduce value-based attack techniques. They leverage information from queries to Count-Min Sketch beyond whether a collision between different elements occurs. By inserting carefully selected values, we can more efficiently recover the elements that hash to the same bucket as the targeted element, for which we want to manipulate estimates. Second, for a very popular family of universal hash functions, we demonstrate that it is possible to generate simultaneous collisions between two elements in two hash tables at the rate which in general it is possible to generate collisions in one hash table. This allows for efficiently producing more skewed estimates than those proposed by previous works. This is achieved by developing methods that allow for narrowing the range of internal parameters of these hash functions.