OpenRaiser/Intern-Atlas huggingface dataset:
Sharing is Caring: A Practical Guide to FAIR(ER) Open Data Release
Authors: Amelia Henriksen, Miranda Mundt
Venue: KDD
Year: 2024

Findable. Accessible. Interoperable. Reusable. Since their introduction in 2016, the FAIR data principles have defined the standards by which scientific researchers share data. However, modern research in data editing and management consistently shows that while the FAIR data principles are widely accepted in theory, they can be much more difficult to understand and implement in practice. In this tutorial, we explore some of the simple, realistic steps scientists can take to FAIRly release open data. We also explore areas where the current FAIR guidelines fall short and offer practical suggestions for making open data FAIR(ER): more Equitable and Realistic. This first involves ways to make datasets themselves more equitably accessible for researchers with disabilities. While equitably accessible data design has some research overlap with paper, presentation, and website design, we suggest several unique distinctions specific to datasets. The "Realistic'' aspect of FAIR(ER) data facilitates a path to translate open data (and research on that data) back to true applications. Driven by national security applications pipelines, we call out important considerations for balancing data editing against data realism.
Budgeted Multi-Armed Bandits with Asymmetric Confidence Intervals
Authors: Marco Heyden, Vadim Arzamasov, Edouard Fouché, Klemens Böhm
Venue: KDD
Year: 2024

We study the stochastic Budgeted Multi-Armed Bandit (MAB) problem, where a player chooses from K arms with unknown expected rewards and costs. The goal is to maximize the total reward under a budget constraint. A player thus seeks to choose the arm with the highest reward-cost ratio as often as possible. Current approaches for this problem have several issues, which we illustrate. To overcome them, we propose a new upper confidence bound (UCB) sampling policy, ømega-UCB, that uses asymmetric confidence intervals. These intervals scale with the distance between the sample mean and the bounds of a random variable, yielding a more accurate and tight estimation of the reward-cost ratio compared to our competitors. We show that our approach has sublinear instance-dependent regret in general and logarithmic regret for parameter ρ ≥ 1, and that it outperforms existing policies consistently in synthetic and real settings.
Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization
Authors: Bao Hoang, Yijiang Pang, Siqi Liang 0001, Liang Zhan, Paul M. Thompson, Jiayu Zhou
Venue: KDD
Year: 2024

Independent and identically distributed (<i>i.i.d.</i>) data is essential to many data analysis and modeling techniques. In the medical domain, collecting data from multiple sites or institutions is a common strategy that guarantees sufficient clinical diversity, determined by the decentralized nature of medical data. However, data from various sites are easily biased by the local environment or facilities, thereby violating the <i>i.i.d.</i> rule. A common strategy is to harmonize the site bias while retaining important biological information. The COMBAT is among the most popular harmonization approaches and has recently been extended to handle distributed sites. However, when faced with situations involving newly joined sites in training or evaluating data from unknown/unseen sites, COMBAT lacks compatibility and requires retraining with data from all the sites. The retraining leads to significant computational and logistic overhead that is usually prohibitive. In this work, we develop a novel <i>Cluster ComBat</i> harmonization algorithm, which leverages cluster patterns of the data in different sites and greatly advances the usability of COMBAT harmonization. We use extensive simulation and real medical imaging data from ADNI to demonstrate the superiority of the proposed approach. Our codes are provided in https://github.com/illidanlab/distributed-cluster-harmonization.
FedKDD: International Joint Workshop on Federated Learning for Data Mining and Graph Analytics
Authors: Junyuan Hong, Carl Yang 0001, Zhuangdi Zhu, Zheng Xu 0002, Nathalie Baracaldo, Neil Shah, Salman Avestimehr, Jiayu Zhou
Venue: KDD
Year: 2024

Deep Learning has facilitated various high-stakes applications such as crime detection, urban planning, drug discovery, and healthcare. Its continuous success hinges on learning from massive data in miscellaneous sources, ranging from data with independent distributions to graph-structured data capturing intricate inter-sample relationships. Scaling up the data access requires global collaboration from distributed data owners. Yet, centralizing all data sources to an untrustworthy centralized server will put users' data at risk of privacy leakage or regulation violation. Federated Learning (FL) is a de facto decentralized learning framework that enables knowledge aggregation from distributed users without exposing private data. Though promising advances are witnessed for FL, new challenges are emerging when integrating FL with the rising needs and opportunities in data mining, graph analytics, foundation models, generative AI, and new interdisciplinary applications in science. By hosting this workshop, we aim to attract a broad range of audiences, including researchers and practitioners from academia and industry interested in the emergent challenges in FL. As an effort to advance the fundamental development of FL, this workshop will encourage ideas exchange on the trustworthiness, scalability, and robustness of distributed data mining and graph analytics and their emergent challenges.
Artificial Intelligence and Data Science for Healthcare: Bridging Data-Centric AI and People-Centric Healthcare
Authors: Shenda Hong, Daoxin Yin, Gongzheng Tang, Tianfan Fu, Liantao Ma, Junyi Gao, Mengling Feng, Mai Wang, Yu Yang, Fei Wang 0001, Hongfang Liu, Luxia Zhang
Venue: KDD
Year: 2024

KDD AIDSH 2024 aims to foster discussions and developments that push the boundaries of Artificial Intelligence (AI) and Data Science (DS) in healthcare, enhance diagnostic accuracy and promote human-centric approaches to healthcare, thus stimulating future interdisciplinary collaborations. This year's symposium will focus on expanding the application of AI/DS in healthcare/medicine and bridging existing gaps. The workshop invites submissions of full papers as well as work-in-progress on the application of AI/DS in healthcare. The workshop will feature three invited talks from eminent speakers, spanning academia, industry, and clinical researchers. In addition, selected papers will be invited to publish in Health Data Science, a Science Partner Journal. This summary provides a brief description of the half-day workshop to be held on August 26th, 2024. The webpage for the workshop can be found at https://aimel.ai/kdd2024aidsh.
RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks
Authors: Seonghyeon Hwang, Minsu Kim 0006, Steven Euijong Whang
Venue: KDD
Year: 2024

We study the problem of robust data augmentation for regression tasks in the presence of noisy data. Data augmentation is essential for generalizing deep learning models, but most of the techniques like the popular Mixup are primarily designed for classification tasks on image data. Recently, there are also Mixup techniques that are specialized to regression tasks like C-Mixup. In comparison to Mixup, which takes linear interpolations of pairs of samples, C-Mixup is more selective in which samples to mix based on their label distances for better regression performance. However, C-Mixup does not distinguish noisy versus clean samples, which can be problematic when mixing and lead to suboptimal model performance. At the same time, robust training has been heavily studied where the goal is to train accurate models against noisy data through multiple rounds of model training. We thus propose our data augmentation strategy RC-Mixup, which tightly integrates C-Mixup with multi-round robust training methods for a synergistic effect. In particular, C-Mixup improves robust training in identifying clean data, while robust training provides cleaner data to C-Mixup for it to perform better. A key advantage of RC-Mixup is that it is data-centric where the robust model training algorithm itself does not need to be modified, but can simply benefit from data mixing. We show in our experiments that RC-Mixup significantly outperforms C-Mixup and robust training baselines on noisy data benchmarks and can be integrated with various robust training methods.
Learn Together Stop Apart: An Inclusive Approach to Ensemble Pruning
Authors: Bulat Ibragimov, Gleb Gusev
Venue: KDD
Year: 2024

Gradient Boosting is a leading learning method that builds ensembles and adapts their sizes to particular tasks, consistently delivering top-tier results across various applications. However, determining the optimal number of models in the ensemble remains a critical yet underexplored aspect. Traditional approaches assume a universal ensemble size effective for all data points, which may not always hold true due to data heterogeneity.
Uplift Modelling via Gradient Boosting
Authors: Bulat Ibragimov, Anton Vakhrushev
Venue: KDD
Year: 2024

The Gradient Boosting machine learning ensemble algorithm, well-known for its proficiency and superior performance in intricate machine learning tasks, has encountered limited success in the realm of uplift modeling. Uplift modeling is a challenging task that necessitates a known target for the precise computation of the training gradient. The prevailing two-model strategies, which separately model treatment and control outcomes, are encumbered with limitations as they fail to directly tackle the uplift problem.
Efficient Discovery of Time Series Motifs under both Length Differences and Warping
Authors: Makoto Imamura, Takaaki Nakamura
Venue: KDD
Year: 2024

Over the past two decades, time series motif discovery has become a crucial subroutine for many time series data mining tasks; concurrently, it has been established that Dynamic Time Warping (DTW) outperforms other similarity measures like Euclidean Distance in most scenarios. Against this backdrop, a DTW motif discovery algorithm was recently developed; however, it is confined to working with fixed-length subsequences. In this work, we propose a novel approach that allows us to find motifs under both length differences and warping. Our algorithm exploits a promising time series representation called Spikelets and introduces the first lower bound for DTW in the Spikelet space. Extensive empirical studies demonstrate that our method scales effectively across various real-world datasets and efficiently identifies DTW motif pairs of different lengths.
Promoting Fairness and Priority in Selecting k-Winners Using IRV
Authors: Md Mouinul Islam, Soroush Vahidi, Baruch Schieber, Senjuti Basu Roy
Venue: KDD
Year: 2024

We investigate the problem of finding winner(s) given a large number of users' (voters') preferences casted as ballots, one from each of the m users, where each ballot is a ranked order of preference of up to ℓ out of n items (candidates). Given a group protected attribute with k different values and a priority that imposes a selection order among these groups, the goal is to satisfy the priority order and select a winner per group that is most representative. It is imperative that at times the original users' preferences may require further manipulation to meet these fairness and priority requirement. We consider manipulation by modifications and formalize the margin finding problem under modification problem. We study the suitability of Instant Run-off Voting (IRV) as a preference aggregation method and demonstrate its advantages over positional methods. We present a suite of technical results on the hardness of the problem, design algorithms with theoretical guarantees and further investigate efficiency opportunities. We present exhaustive experimental evaluations using multiple applications and large-scale datasets to demonstrate the effectiveness of IRV, and efficacy of our designed solutions qualitatively and scalability-wise.
Explainable and Interpretable Forecasts on Non-Smooth Multivariate Time Series for Responsible Gameplay
Authors: Hussain Jagirdar, Rukma Talwadker, Aditya Pareek, Pulkit Agrawal 0004, Tridib Mukherjee
Venue: KDD
Year: 2024

Multi-variate Time Series (MTS) forecasting has made large strides (with very negligible errors) through recent advancements in neural networks, e.g., Transformers. However, in critical situations like predicting gaming overindulgence that affects one's mental well-being; an accurate forecast without a contributing evidence (explanation) is irrelevant. Hence, it becomes important that the forecasts are Interpretable - intermediate representation of the forecasted trajectory is comprehensible; as well as Explainable - attentive input features and events are accessible for a personalized and timely intervention of players at risk. While the contributing state of the art research on interpretability primarily focuses on temporally-smooth single-process driven time series data, our online multi-player gameplay data demonstrates intractable temporal randomness due to intrinsic orthogonality between player's game outcome and their intent to engage further. We introduce a novel deep Actionable Forecasting Network (AFN), which addresses the inter-dependent challenges associated with three exclusive objectives - 1) forecasting accuracy; 2) smooth comprehensible trajectory and 3) explanations via multi-dimensional input features while tackling the challenges introduced by our non-smooth temporal data, together in one single solution. AFN establishes a new benchmark via: (i) achieving 25% improvement on the MSE of the forecasts on player data in comparison to the SOM-VAE based SOTA networks; (ii) attributing unfavourable progression of a player's time series to a specific future time step(s), with the premise of eliminating near-future overindulgent player volume by over 18% with player specific actionable inputs feature(s) and (iii) proactively detecting over 23% (100% jump from SOTA) of the to-be overindulgent, players on an average, 4 weeks in advance.
Learning to Bid the Interest Rate in Online Unsecured Personal Loans
Authors: Dong Jun Jee, Seung Jung Jin, Ji-Hoon Yoo, Byunggyu Ahn
Venue: KDD
Year: 2024

The unsecured personal loan (UPL) market is a multi-billion dollar market where numerous financial institutions compete. Due to the development of online banking, loan applicants start to compare numerous loan products. They aim for high loan limits and low interest rates. Since loan applicants have a desired loan amount, institutions instead focus on adjusting interest rates. Despite the importance of determining optimal interest strategies, institutions have traditionally relied on heuristic methods by human experts to set interest rates. This is done by adding a target return on assets (ROA) to the applicant's expected default probability predicted by a credit scoring system (CSS) such as the FICO score.
Decomposed Attention Segment Recurrent Neural Network for Orbit Prediction
Authors: Seungwon Jeong, Soyeon Woo, Daewon Chung, Simon S. Woo, Youjin Shin
Venue: KDD
Year: 2024

As the focus of space exploration shifts from national agencies to private companies, the interest in space industry has been steadily increasing. With the increasing number of satellites, the risk of collisions between satellites and space debris has escalated, potentially leading to significant property and human losses. Therefore, accurately modeling the orbit is critical for satellite operations. In this work, we propose the Decomposed Attention Segment Recurrent Neural Network (DASR) model, adding two key components, Multi-Head Attention and Tensor Train Decomposition, to SegRNN for orbit prediction. The DASR model applies Multi-Head Attention before segmenting at input data and before the input of the GRU layers. In addition, Tensor Train (TT) Decomposition is applied to the weight matrices of the Multi-Head Attention in both the encoder and decoder. For evaluation, we use three real-world satellite datasets from the Korea Aerospace Research Institute (KARI), which are currently operating: KOMPSAT-3, KOMPSAT-3A, and KOMPSAT-5 satellites. Our proposed model demonstrates superior performance compared to other SOTA baseline models. We demonstrate that our approach has 94.13% higher predictive performance than the second-best model in the KOMPSAT-3 dataset, 89.79% higher in the KOMPSAT-3A dataset, and 76.71% higher in the KOMPSAT-5 dataset.
On (Normalised) Discounted Cumulative Gain as an Off-Policy Evaluation Metric for Top-n Recommendation
Authors: Olivier Jeunen, Ivan Potapov, Aleksei Ustimenko
Venue: KDD
Year: 2024

Approaches to recommendation are typically evaluated in one of two ways: (1) via a (simulated) online experiment, often seen as the gold standard, or (2) via some offline evaluation procedure, where the goal is to approximate the outcome of an online experiment. Several offline evaluation metrics have been adopted in the literature, inspired by ranking metrics prevalent in the field of Information Retrieval. (Normalised) Discounted Cumulative Gain (nDCG) is one such metric that has seen widespread adoption in empirical studies, and higher (n)DCG values have been used to present new methods as the state-of-the-art in top-n recommendation for many years.
Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization
Authors: Sheo Yon Jhin, Seojin Kim, Noseong Park
Venue: KDD
Year: 2024

Time series forecasting has been an essential field in many different application areas, including economic analysis, meteorology, and so forth. The majority of time series forecasting models are trained using the mean squared error (MSE). However, this training based on MSE causes a limitation known as prediction delay. The prediction delay, which implies the ground-truth precedes the prediction, can cause serious problems in a variety of fields, e.g., finance and weather forecasting --- as a matter of fact, predictions succeeding ground-truth observations are not practically meaningful although their MSEs can be low. This paper proposes a new perspective on traditional time series forecasting tasks and introduces a new solution to mitigate the prediction delay. We introduce a continuous-time gated recurrent unit (GRU) based on the neural ordinary differential equation (NODE) which can supervise explicit time-derivatives. We generalize the GRU architecture in a continuous-time manner and minimize the prediction delay through our time-derivative regularization. Our method outperforms in metrics such as MSE, Dynamic Time Warping (DTW) and Time Distortion Index (TDI). In addition, we demonstrate the low prediction delay of our method in a variety of datasets.
Tensorized Unaligned Multi-view Clustering with Multi-scale Representation Learning
Authors: Jintian Ji, Songhe Feng, Yidong Li
Venue: KDD
Year: 2024

The Unaligned Multi-view Clustering (UMC) problem is currently receiving widespread attention, focusing on clustering unaligned multi-view data generated in real-world applications. Although some algorithms have emerged to address this issue, there still exist the following drawbacks: 1) The fully unknown correspondence of samples across views can significantly limit the exploration of consistent clustering structure. 2) The fixed representation space makes it difficult to mine the comprehensive information in the original data. 3) Unbiased tensor rank approximation is desired to capture the high-order correlation among different views. To address these issues, we proposed a novel UMC framework termed Tensorized Unaligned Multi-view Clustering with Multi-scale Representation Learning (TUMCR). Specifically, TUMCR designs a multi-scale representation learning and alignment framework, which constructs multi-scale representation spaces to comprehensively explore the unknown correspondence across views. Then, a tensorial multi-scale fusion module is proposed to fuse multi-scale representations and explore the high-order correlation hidden in different views, which utilizes the Enhanced Tensor Rank (ETR) to learn the low-rank structure. Furthermore, TUMCR is solved by an efficient algorithm with good convergence. Extensive experiments on different types of datasets demonstrate the effectiveness and superiority of our TUMCR compared with state-of-the-art methods. Our code is publicly available at: https://github.com/jijintian/TUMCR.
Mutual Distillation Extracting Spatial-temporal Knowledge for Lightweight Multi-channel Sleep Stage Classification
Authors: Ziyu Jia, Haichao Wang, Yucheng Liu, Tianzi Jiang
Venue: KDD
Year: 2024

Sleep stage classification has important clinical significance for the diagnosis of sleep-related diseases. To pursue more accurate sleep stage classification, multi-channel sleep signals are widely used due to the rich spatial-temporal information contained. However, it leads to a great increment in the size and computational costs, which constrain the application of multi-channel sleep models on hardware devices. Knowledge distillation is an effective way to compress models, yet existing knowledge distillation methods cannot fully extract and transfer the spatial-temporal knowledge in the multi-channel sleep signals. To solve the problem, we propose a general knowledge distillation framework for multi-channel sleep stage classification called spatial-temporal mutual distillation. Based on the spatial relationship of human body and the temporal transition rules of sleep signals, the spatial and temporal modules are designed to extract the spatial-temporal knowledge, thus help the lightweight student model learn the rich spatial-temporal knowledge from large-scale teacher model. The mutual distillation framework transfers the spatial-temporal knowledge mutually. Teacher model and student model can learn from each other, further improving the student model. The results on the ISRUC-III and MASS-SS3 datasets show that our proposed framework compresses the sleep models effectively with minimal performance loss and achieves the state-of-the-art performance compared to the baseline methods.
FairMatch: Promoting Partial Label Learning by Unlabeled Samples
Authors: Jiahao Jiang, Yuheng Jia, Hui Liu 0032, Junhui Hou
Venue: KDD
Year: 2024

This paper studies the semi-supervised partial label learning (SSPLL) problem, which aims to improve the partial label learning (PLL) by leveraging unlabeled samples. Both the existing SSPLL methods and the semi-supervised learning methods exploit the information in unlabeled samples by selecting high-confidence unlabeled samples as the pseudo labels based on the maximum value of the model output. However, the scarcity of labeled samples and the ambiguity from partial labels skew this strategy towards an unfair selection of high-confidence samples on each class, most notably during the initial phases of training, resulting in slower training and performance degradation. In this paper, we propose a novel method FairMatch, which adopts a learning state aware self-adaptive threshold for selecting the same number of high-confidence samples on each class, and uses augmentation consistency to incorporate the unlabeled samples to promote PLL. In addition, we adopt the candidate label disambiguation to utilize the partial labeled samples and mix up the partial labeled samples and the selected high-confidence unlabeled samples to prevent the model from overfitting on partial label samples. FairMatch can achieve maximum accuracy improvements of 9.53%, 4.9%, and 16.45% on CIFAR-10, CIFAR-100, and CIFAR-100H, respectively. The codes can be found at https://github.com/jhjiangSEU/FairMatch.