OpenRaiser/Intern-Atlas huggingface dataset:
epiDAMIK 5.0: The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery
Authors: Bijaya Adhikari, Amulya Yadav, Sen Pei, Ajitesh Srivastava, Sarah Kefayati, Alexander Rodríguez, Marie-Laure Charpignon, Anil Vullikanti, B. Aditya Prakash
Venue: KDD
Year: 2022

Similar to previous iterations, the epiDAMIK @ KDD workshop is a forum to promote data driven approaches in epidemiology and public health research. Even after the devastating impact of COVID-19 pandemic, data driven approaches are not as widely studied in epidemiology, as they are in other spaces. We aim to promote and raise the profile of the emerging research area of data-driven and computational epidemiology, and create a venue for presenting state-of-the-art and in-progress results-in particular, results that would otherwise be difficult to present at a major data mining conference, including lessons learnt in the 'trenches'. The current COVID-19 pandemic has only showcased the urgency and importance of this area. Our target audience consists of data mining and machine learning researchers from both academia and industry who are interested in epidemiological and public-health applications of their work, and practitioners from the areas of mathematical epidemiology and public health. Homepage: https://epidamik.github.io/.
A Process-Aware Decision Support System for Business Processes
Authors: Prerna Agarwal, Buyu Gao, Siyu Huo, Prabhat Reddy, Sampath Dechu, Yazan Obeidi, Vinod Muthusamy, Vatche Isahagian, Sebastian Carbajales
Venue: KDD
Year: 2022

Business processes in workflows comprise of an ordered sequence of tasks and decisions to accomplish certain business goals. Each decision point requires the input of a decision-maker to distill complex case information and make an optimal decision given their experience, organizational policy, and external contexts. Overlooking some of the essential factors or lack of knowledge can impact the throughput and business outcomes. Therefore, we propose an end-to-end automated decision support system with explanation for business processes. The system uses the proposed process-aware feature engineering methodology that extracts features from process and business data attributes. The system helps a decision-maker to make quick and quality decisions by predicting the decision and providing an explanation of the factors which led to the prediction. We provide offline and online training methods robust to data drift that can also incorporate user feedback. The system also support predictions with live instance data i.e., allow decision-makers to conduct trials on current data instance by modifying its business data attribute values. We evaluate our system on real-world and synthetic datasets and benchmark the performance, achieving an average of 15% improvement over baselines.
RCAD: Real-time Collaborative Anomaly Detection System for Mobile Broadband Networks
Authors: Azza H. Ahmed, Michael A. Riegler, Steven Alexander Hicks, Ahmed Elmokashfi
Venue: KDD
Year: 2022

The rapid increase in mobile data traffic and the number of connected devices and applications in networks is putting a significant pressure on the current network management approaches that heavily rely on human operators. Consequently, an automated network management system that can efficiently predict and detect anomalies is needed. In this paper, we propose, RCAD, a novel distributed architecture for detecting anomalies in network data forwarding latency in an unsupervised fashion. RCAD employs the hierarchical temporal memory (HTM) algorithm for the online detection of anomalies. It also involves a collaborative distributed learning module that facilitates knowledge sharing across the system. We implement and evaluate RCAD on real world measurements from a commercial mobile network. RCAD achieves over 0.7 F-1 score significantly outperforming current state-of-the-art methods.
Reward Optimizing Recommendation using Deep Learning and Fast Maximum Inner Product Search
Authors: Imad Aouali, Amine Benhalloum, Martin Bompaire, Achraf Ait Sidi Hammou, Sergey Ivanov, Benjamin Heymann, David Rohde, Otmane Sakhi, Flavian Vasile, Maxime Vono
Venue: KDD
Year: 2022

How can we build and optimize a recommender system that must rapidly fill slates (i.e. banners) of personalized recommendations? The combination of deep learning stacks with fast maximum inner product search (MIPS) algorithms have shown it is possible to deploy flexible models in production that can rapidly deliver personalized recommendations to users. Albeit promising, this methodology is unfortunately not sufficient to build a recommender system which maximizes the reward, e.g. the probability of click. Usually instead a proxy loss is optimized and A/B testing is used to test if the system actually improved performance. This tutorial takes participants through the necessary steps to model the reward and directly optimize the reward of recommendation engines built upon fast search algorithms to produce high-performance reward-optimizing recommender systems.
AdKDD 2022
Authors: Abraham Bagherjeiran, Nemanja Djuric, Mihajlo Grbovic, Kuang-Chih Lee, Kun Liu, Wei Liu 0007, Linsey Pang, Vladan Radosavljevic, Suju Rajan, Kexin Xie
Venue: KDD
Year: 2022

An average consumer spends 8+ hours a day across all devices interacting with online content almost entirely sponsored by advertisements. At over $450B global market size in 2022 and expected to pass $1T by 2027, online advertising has already surpassed traditional ads in global spend. Moreover, computational advertising in particular is perhaps the most visible and ubiquitous application of machine learning and one that interacts directly with consumers. When done right, ads help us enrich our lives and creep us out when done badly. Looking at the published literature over the last few years, many researchers might consider computational advertising as a mature field. Yet, the opposite is true. The field is evolving, however, from ads controlled by monolithic publishers and randomly rotating banner ads to highly personalized content experiences in news feeds on mobile devices and even on TV-all utilizing data amassed from petabytes of stored user data. Ads are far from done.
Submodular Feature Selection for Partial Label Learning
Authors: Wei-Xuan Bao, Jun-Yi Hang, Min-Ling Zhang
Venue: KDD
Year: 2022

Partial label learning induces a multi-class classifier from training examples each associated with a candidate label set where the ground-truth label is concealed. Feature selection improves the generalization ability of learning system via selecting essential features for classification from the original feature set, while the task of partial label feature selection is challenging due to ambiguous labeling information. In this paper, the first attempt towards partial label feature selection is investigated via mutual-information-based dependency maximization. Specifically, the proposed approach SAUTE iteratively maximizes the dependency between selected features and labeling information, where the value of mutual information is estimated from confidence-based latent variable inference. In each iteration, the near-optimal features are selected greedily according to properties of submodular mutual information function, while the density of latent label variable is inferred with the help of updated labeling confidences over candidate labels by resorting to kNN aggregation in the induced lower-dimensional feature space. Extensive experiments over synthetic as well as real-world partial label data sets show that the generalization ability of well-established partial label learning algorithms can be significantly improved after coupling with the proposed feature selection approach.
Joint International Workshop on Misinformation and Misbehavior Mining on the Web & Making a Credible Web for Tomorrow (MIS2-TrueFact)
Authors: Pamela Bhattacharya, Jing Gao 0004, Meng Jiang 0001, Mehran Kafai, Srijan Kumar, Qi Li 0012, Neil Shah, Sihong Xie, Philip S. Yu, Ming Zeng 0001
Venue: KDD
Year: 2022

The MIS2-TrueFact is geared towards bringing academic, industry, and government researchers and practitioners together to tackle the challenges in misinformation, misbehavior, and data quality issues on the web with heterogeneous and multi-modal sources of information including texts, images, videos, relational data, social networks, and knowledge graphs.
Shallow and Deep Non-IID Learning on Complex Data
Authors: Longbing Cao, Philip S. Yu, Zhilin Zhao 0001
Venue: KDD
Year: 2022

Non-IID (i.i.d.) data holds complex non-IIDness, e.g., couplings and interactions (non-independent) and heterogeneities (not IID drawn from a given distribution). Non-IID learning emerges as a major challenge to shallow and deep learning, including classic statistical learning, mathematical modeling, shallow machine learning, and deep neural learning. Here, we outline the problem, research map, main challenges and topics of shallow and deep non-IID learning.
The Fourth Workshop on Adversarial Learning Methods for Machine Learning and Data Mining (AdvML 2022)
Authors: Pin-Yu Chen, Cho-Jui Hsieh, Bo Li 0026, Sijia Liu 0001
Venue: KDD
Year: 2022

Adversarial learning methods and their applications such as generative adversarial network, adversarial robustness, and security and privacy, have prevailed and revolutionized the research in machine learning and data mining. Their importance has not only been emphasized by the research community but also been widely recognized by the industry and the general public. Continuing the synergies in previous years, this third annual workshop aims to advance this research field. The AdvML'22 workshop consists of four tracks: (i) open-call paper submissions; (ii) invited speakers; (iii) rising star awards and presentations; and (iv) panel discussion on AdvML. The full details about the workshop can be found at https://sites.google.com/view/advml.
Effective Social Network-Based Allocation of COVID-19 Vaccines
Authors: Jiangzhuo Chen, Stefan Hoops, Achla Marathe, Henning S. Mortveit, Bryan L. Lewis, Srinivasan Venkatramanan, Arash Haddadan, Parantapa Bhattacharya, Abhijin Adiga, Anil Vullikanti, Aravind Srinivasan, Mandy L. Wilson, Gal Ehrlich, Maier Fenster, Stephen G. Eubank, Christopher L. Barrett, Madhav V. Marathe
Venue: KDD
Year: 2022

We study allocation of COVID-19 vaccines to individuals based on the structural properties of their underlying social contact network. Using a realistic representation of a social contact network for the Commonwealth of Virginia, we study how a limited number of vaccine doses can be strategically distributed to individuals to reduce the overall burden of the pandemic. We show that allocation of vaccines based on individuals' degree (number of social contacts) and total social proximity time is significantly more effective than the usually used age-based allocation strategy in reducing the number of infections, hospitalizations and deaths. The overall strategy is robust even: (i) if the social contacts are not estimated correctly; (ii) if the vaccine efficacy is lower than expected or only a single dose is given; (iii) if there is a delay in vaccine production and deployment; and (iv) whether or not non-pharmaceutical interventions continue as vaccines are deployed. For reasons of implementability, we have used degree, which is a simple structural measure and can be easily estimated using several methods, including the digital technology available today. These results are significant, especially for resource-poor countries, where vaccines are less available, have lower efficacy, and are more slowly distributed.
Multi-modal Siamese Network for Entity Alignment
Authors: Liyi Chen 0001, Zhi Li 0057, Tong Xu 0001, Han Wu 0002, Zhefeng Wang 0001, Nicholas Jing Yuan, Enhong Chen
Venue: KDD
Year: 2022

The booming of multi-modal knowledge graphs (MMKGs) has raised the imperative demand for multi-modal entity alignment techniques, which facilitate the integration of multiple MMKGs from separate data sources. Unfortunately, prior arts harness multi-modal knowledge only via the heuristic merging of uni-modal feature embeddings. Therefore, inter-modal cues concealed in multi-modal knowledge could be largely ignored. To deal with that problem, in this paper, we propose a novel Multi-modal Siamese Network for Entity Alignment (MSNEA) to align entities in different MMKGs, in which multi-modal knowledge could be comprehensively leveraged by the exploitation of inter-modal effect. Specifically, we first devise a multi-modal knowledge embedding module to extract visual, relational, and attribute features of entities to generate holistic entity representations for distinct MMKGs. During this procedure, we employ inter-modal enhancement mechanisms to integrate visual features to guide relational feature learning and adaptively assign attention weights to capture valuable attributes for alignment. Afterwards, we design a multi-modal contrastive learning module to achieve inter-modal enhancement fusion with avoiding the overwhelming impact of weak modalities. Experimental results on two public datasets demonstrate that our proposed MSNEA provides state-of-the-art performance with a large margin compared with competitive baselines.
Efficient Orthogonal Multi-view Subspace Clustering
Authors: Man-Sheng Chen, Chang-Dong Wang 0001, Dong Huang 0001, Jian-Huang Lai, Philip S. Yu
Venue: KDD
Year: 2022

Multi-view subspace clustering targets at clustering data lying in a union of low-dimensional subspaces. Generally, an n X n affinity graph is constructed, on which spectral clustering is then performed to achieve the final clustering. Both graph construction and graph partitioning of spectral clustering suffer from quadratic or even cubic time and space complexity, leading to difficulty in clustering large-scale datasets. Some efforts have recently been made to capture data distribution in multiple views by selecting key anchor bases beforehand with k-means or uniform sampling strategy. Nevertheless, few of them pay attention to the algebraic property of the anchors. How to learn a set of high-quality orthogonal bases in a unified framework, while maintaining its scalability for very large datasets, remains a big challenge. In view of this, we propose an Efficient Orthogonal Multi-view Subspace Clustering (OMSC) model with almost linear complexity. Specifically, the anchor learning, graph construction and partition are jointly modeled in a unified framework. With the mutual enhancement of each other, a more discriminative and flexible anchor representation and cluster indicator can be jointly obtained. An alternate minimizing strategy is developed to deal with the optimization problem, which is proved to have linear time complexity w.r.t. the sample number. Extensive experiments have been conducted to confirm the superiority of the proposed OMSC method. The source codes and data are available at https://github.com/ManshengChen/Code-for-OMSC-master.
Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting
Authors: Weiqi Chen, Wenwei Wang, Bingqing Peng, Qingsong Wen, Tian Zhou 0004, Liang Sun 0001
Venue: KDD
Year: 2022

Time series forecasting is a critical and challenging problem in many real applications. Recently, Transformer-based models prevail in time series forecasting due to their advancement in long-range dependencies learning. Besides, some models introduce series decomposition to further unveil reliable yet plain temporal dependencies. Unfortunately, few models could handle complicated periodical patterns, such as multiple periods, variable periods, and phase shifts in real-world datasets. Meanwhile, the notorious quadratic complexity of dot-product attentions hampers long sequence modeling. To address these challenges, we design an innovative framework Quaternion Transformer (Quatformer), along with three major components: 1). learning-to-rotate attention (LRA) based on quaternions which introduces learnable period and phase information to depict intricate periodical patterns. 2). trend normalization to normalize the series representations in hidden layers of the model considering the slowly varying characteristic of trend. 3). decoupling LRA using global memory to achieve linear complexity without losing prediction accuracy. We evaluate our framework on multiple real-world time series datasets and observe an average 8.1% and up to 18.5% MSE improvement over the best state-of-the-art baseline.
Interpreting Trajectories from Multiple Views: A Hierarchical Self-Attention Network for Estimating the Time of Arrival
Authors: Zebin Chen, Xiaolin Xiao, Yue-Jiao Gong, Jun Fang, Nan Ma, Hua Chai, Zhiguang Cao
Venue: KDD
Year: 2022

Estimating the time of arrival is a crucial task in intelligent transportation systems. Although considerable efforts have been made to solve this problem, most of them decompose a trajectory into several segments and then compute the travel time by integrating the attributes from all segments. The segment view, though being able to depict the local traffic conditions straightforwardly, is insufficient to embody the intrinsic structure of trajectories on the road network. To overcome the limitation, this study proposes multi-view trajectory representation that comprehensively interprets a trajectory from the segment-, link-, and intersection-views. To fulfill the purpose, we design a hierarchical self-attention network (HierETA) that accurately models the local traffic conditions and the underlying trajectory structure. Specifically, a segment encoder is developed to capture the spatio-temporal dependencies at a fine granularity, within which an adaptive self-attention module is designed to boost performance. Further, a joint link-intersection encoder is developed to characterize the natural trajectory structure consisting of alternatively arranged links and intersections. Afterward, a hierarchy-aware attention decoder is designed to realize a tradeoff between the multi-view spatio-temporal features. The hierarchical encoders and the attentive decoder are simultaneously learned to achieve an overall optimality. Experiments on two large-scale practical datasets show the superiority of HierETA over the state-of-the-arts.
Efficient Approximate Algorithms for Empirical Variance with Hashed Block Sampling
Authors: Xingguang Chen, Fangyuan Zhang 0001, Sibo Wang 0001
Venue: KDD
Year: 2022

Empirical variance is a fundamental concept widely used in data management and data analytics, e.g., query optimization, approximate query processing, and feature selection. A direct solution to derive the empirical variance is scanning the whole data table, which is expensive when the data size is huge. Hence, most current works focus on approximate answers by sampling. For results with approximation guarantees, the samples usually need to be uniformly independent random, incurring high cache miss rates especially in compact columnar style layouts. An alternative uses block sampling to avoid this issue, which directly samples a block of consecutive records fitting page sizes instead of sampling one record each time. However, this provides no theoretical guarantee. Existing studies show that the practical estimations can be inaccurate as the records within a block can be correlated.
FedMSplit: Correlation-Adaptive Federated Multi-Task Learning across Multimodal Split Networks
Authors: Jiayi Chen, Aidong Zhang 0001
Venue: KDD
Year: 2022

With the advancement of data collection techniques, end users are interested in how different types of data can collaborate to improve our life experiences. Multimodal Federated Learning (MFL) is an emerging area allowing many distributed clients, each of which can collect data from multiple types of sensors, to participate in the training of some multimodal data-related models without sharing their data. In this paper, we address a novel challenging issue in MFL, the modality incongruity, where clients may have heterogeneous setups of sensors and their local data consists of different combinations of modalities. With the modality incongruity, clients may solve different tasks on different parameter spaces, which escalates the difficulties in dealing with the statistical heterogeneity problem of federated learning; also, it would be hard to perform accurate model aggregation across different types of clients. To tackle these challenges, in this work, we propose the FedMSplit framework, which allows federated training over multimodal distributed data without assuming similar active sensors in all clients. The key idea is to employ a dynamic and multi-view graph structure to adaptively capture the correlations amongst multimodal client models. More specifically, we split client models into smaller shareable blocks and allow each type of blocks to provide a specific view on client relationships. With the graph representation, the underlying correlations between clients can be captured as the edge features in the multi-view graph, and then be utilized to promote local model relations through the neighborhood message passing in the graph. Our experimental results demonstrate the effectiveness of our method under different sensor setups with statistical heterogeneity.
Physics-Guided Graph Meta Learning for Predicting Water Temperature and Streamflow in Stream Networks
Authors: Shengyu Chen, Jacob A. Zwart, Xiaowei Jia
Venue: KDD
Year: 2022

This paper proposes a graph-based meta learning approach to separately predict water quantity and quality variables for river segments in stream networks. Given the heterogeneous water dynamic patterns in large-scale basins, we introduce an additional meta-learning condition based on physical characteristics of stream segments, which allows learning different sets of initial parameters for different stream segments. Specifically, we develop a representation learning method that leverages physical simulations to embed the physical characteristics of each segment. The obtained embeddings are then used to cluster river segments and add the condition for the meta-learning process. We have tested the performance of the proposed method for predicting daily water temperature and streamflow for the Delaware River Basin (DRB) over a 14 year period. The results confirm the effectiveness of our method in predicting target variables even using sparse training samples. We also show that our method can achieve robust performance with different numbers of clusterings.
Sufficient Vision Transformer
Authors: Zhi Cheng, Xiu Su, Xueyu Wang, Shan You, Chang Xu 0002
Venue: KDD
Year: 2022

Currently, Vision Transformer (ViT) and its variants have demonstrated promising performance on various computer vision tasks. Nevertheless, task-irrelevant information such as background nuisance and noise in patch tokens would damage the performance of ViT-based models. In this paper, we develop Sufficient Vision Transformer (Suf-ViT) as a new solution to address this issue. In our research, we propose the Sufficiency-Blocks (S-Blocks) to be applied across the depth of Suf-ViT to disentangle and discard task-irrelevant information accurately. Besides, to boost the training of Suf-ViT, we formulate a Sufficient-Reduction Loss (SRLoss) leveraging the concept of Mutual Information (MI) that enables Suf-ViT to extract more reliable sufficient representations by removing task-irrelevant information. Extensive experiments on benchmark datasets such as ImageNet, ImageNet-C, and CIFAR-10 indicate that our method can achieve state-of-the-art or competing performance over other baseline methods. Codes are available at: https://github.com/zhicheng2T0/Sufficient-Vision-Transformer.git