OpenRaiser/Intern-Atlas huggingface dataset:
Automated Machine Learning & Tuning with FLAML
Authors: Chi Wang 0001, Qingyun Wu, Xueqing Liu 0001, Luis Quintanilla
Venue: KDD
Year: 2022

In this tutorial, we will provide an in-depth and hands-on tutorial on Automated Machine Learning & Tuning with a fast python library FLAML. We will start with an overview of the AutoML problem and the FLAML library. In the first half of the tutorial, we will then give a hands-on tutorial on how to use FLAML to automate typical machine learning tasks in an end-to-end manner with different customization options and how to perform general tuning tasks on user-defined functions. In the second half of the tutorial, we will introduce several advanced functionalities of the library. For example, zero-shot AutoML, fair AutoML, and online AutoML. We will close the tutorial with several open problems, and challenges learned from AutoML practice.
New Frontiers of Scientific Text Mining: Tasks, Data, and Tools
Authors: Xuan Wang 0008, Hongwei Wang, Heng Ji 0001, Jiawei Han 0001
Venue: KDD
Year: 2022

Exploring the vast amount of rapidly growing scientific text data is highly beneficial for real-world scientific discovery. However, scientific text mining is particularly challenging due to the lack of specialized domain knowledge in natural language context, complex sentence structures in scientific writing, and multi-modal representations of scientific knowledge. This tutorial presents a comprehensive overview of recent research and development on scientific text mining, focusing on the biomedical and chemistry domains. First, we introduce the motivation and unique challenges of scientific text mining. Then we discuss a set of methods that perform effective scientific information extraction, such as named entity recognition, relation extraction, and event extraction. We also introduce real-world applications such as textual evidence retrieval, scientific topic contrasting for drug discovery, and molecule representation learning for reaction prediction. Finally, we conclude our tutorial by demonstrating, on real-world datasets (COVID-19 and organic chemistry literature), how the information can be extracted and retrieved, and how they can assist further scientific discovery. We also discuss the emerging research problems and future directions for scientific text mining.
Unsupervised Learning Style Classification for Learning Path Generation in Online Education Platforms
Authors: Zhicheng He 0001, Wei Xia 0001, Kai Dong, Huifeng Guo, Ruiming Tang, Dingyin Xia, Rui Zhang 0003
Venue: KDD
Year: 2022

Online education, which educates students that cannot be present at school, has become an important supplement to traditional education. Without the direct supervision and instruction of teachers, online education is always concerned with potential distractions and misunderstandings. Learning Style Classification (LSC) is proposed to analyze the learning behavior patterns of online learning users, based on which personalized learning paths are generated to help them learn and maintain their interests.
DeepSpatial'22: The 3rd International Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems
Authors: Zhe Jiang 0001, Liang Zhao 0002, Xun Zhou 0001, Robert N. Stewart, Junbo Zhang 0004, Shashi Shekhar 0001, Jieping Ye
Venue: KDD
Year: 2022

With the advancement of GPS and remote sensing technologies and the pervasiveness of smartphones and IoT devices, an enormous amount of spatiotemporal data are being collected from various domains. Knowledge discovery from spatiotemporal data is crucial in addressing many grand societal challenges, ranging from flood disaster management to monitoring coastal hazards, and from autonomous driving to disease forecasting. The recent success in deep learning technologies in computer vision and natural language processing provides new opportunities for spatiotemporal data mining, but existing deep learning techniques also face unique spatiotemporal challenges (e.g., autocorrelation, non-stationarity, physics awareness). This workshop provides a premium platform for researchers from both academia and industry to exchange ideas on the opportunities, challenges, and cutting-edge techniques related to deep learning for spatiotemporal data.
Automatic Controllable Product Copywriting for E-Commerce
Authors: Xiaojie Guo 0002, Qingkai Zeng 0001, Meng Jiang 0001, Yun Xiao, Bo Long, Lingfei Wu 0001
Venue: KDD
Year: 2022

Automatic product description generation for e-commerce has witnessed significant advancement in the past decade. Product copy- writing aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. As the services provided by e-commerce platforms become diverse, it is necessary to adapt the patterns of automatically-generated descriptions dynamically. In this paper, we report our experience in deploying an E-commerce Prefix-based Controllable Copywriting Generation (EPCCG) system into the JD.com e-commerce product recommendation platform. The development of the system contains two main components: 1) copywriting aspect extraction; 2) weakly supervised aspect labelling; 3) text generation with a prefix-based language model; and 4) copywriting quality control. We conduct experiments to validate the effectiveness of the proposed EPCCG. In addition, we introduce the deployed architecture which cooperates the EPCCG into the real-time JD.com e-commerce recommendation platform and the significant payoff since deployment. The codes for implementation are provided at https://github.com/xguo7/Automatic-Controllable-Product-Copywriting-for-E-Commerce.git.
Multi-Agent Graph Convolutional Reinforcement Learning for Dynamic Electric Vehicle Charging Pricing
Authors: Weijia Zhang 0003, Hao Liu 0026, Jindong Han, Yong Ge, Hui Xiong 0001
Venue: KDD
Year: 2022

Electric Vehicles (EVs) have been emerging as a promising low-carbon transport target. While a large number of public charging stations are available, the use of these stations is often imbalanced, causing many problems to Charging Station Operators (CSOs). To this end, in this paper, we propose a Multi-Agent Graph Convolutional Reinforcement Learning (MAGC) framework to enable CSOs to achieve more effective use of these stations by providing dynamic pricing for each of the continuously arising charging requests with optimizing multiple long-term commercial goals. Specifically, we first formulate this charging station request-specific dynamic pricing problem as a mixed competitive-cooperative multi-agent reinforcement learning task, where each charging station is regarded as an agent. Moreover, by modeling the whole charging market as a dynamic heterogeneous graph, we devise a multi-view heterogeneous graph attention networks to integrate complex interplay between agents induced by their diversified relationships. Then, we propose a shared meta generator to generate individual customized dynamic pricing policies for large-scale yet diverse agents based on the extracted meta characteristics. Finally, we design a contrastive heterogeneous graph pooling representation module to learn a condensed yet effective state action representation to facilitate policy learning of large-scale agents. Extensive experiments on two real-world datasets demonstrate the effectiveness of MAGC and empirically show that the overall use of stations can be improved if all the charging stations in a charging market embrace our dynamic pricing policy.
Domain Adaptation in Physical Systems via Graph Kernel
Authors: Haoran Li 0005, Hanghang Tong, Yang Weng
Venue: KDD
Year: 2022

Physical systems are extending their monitoring capacities to edge areas with low-cost, low-power sensors and advanced data mining and machine learning techniques. However, new systems often have limited data for training the model, calling for effective knowledge transfer from other relevant grids. Specifically, Domain Adaptation (DA) seeks domain-invariant features to boost the model performance in the target domain. Nonetheless, existing DA techniques face significant challenges due to the unique characteristics of physical datasets: (1) complex spatial-temporal correlations, (2) diverse data sources including node/edge measurements and labels, and (3) large-scale data sizes. In this paper, we propose a novel cross-graph DA based on two core designs of graph kernels and graph coarsening. The former design handles spatial-temporal correlations and can incorporate networked measurements and labels conveniently. The spatial structures, temporal trends, measurement similarity, and label information together determine the similarity of two graphs, guiding the DA to find domain-invariant features. Mathematically, we construct a Graph kerNel-based distribution Adaptation (GNA) with a specifically-designed graph kernel. Then, we prove the proposed kernel is positive definite and universal, which strictly guarantees the feasibility of the used DA measure. However, the computation cost of the kernel is prohibitive for large systems. In response, we propose a novel coarsening process to obtain much smaller graphs for GNA. Finally, we report the superiority of GNA in diversified systems, including power systems, mass-damper systems, and human-activity sensing systems.
G2NET: A General Geography-Aware Representation Network for Hotel Search Ranking
Authors: Jia Xu 0005, Fei Xiong, Zulong Chen, Mingyuan Tao, Liangyue Li, Quan Lu
Venue: KDD
Year: 2022

Hotel search ranking is the core function of Online Travel Platforms (OTPs), while geography information of location entities involved in it plays a critically important role in guaranteeing its ranking quality. The closest line of works to the hotel search ranking problem is thus the next POI (or location) recommendation problem, which has extensive works but fails to cope with two new challenges, i.e., consideration of two more location entities and effective utilization of geographical information, in a hotel search ranking scenario. To this end, we propose a General Geography-aware representation NETwork (G2NET for short) to better represent geography information of location entities so as to optimize the hotel search ranking. In G2NET, to address the first challenge, we first propose the concept of Geography Interaction Schema (GIS) which is a meta template for representing the arbitrary number of location entity types and their interactions. Then, a novel geography interaction encoder is devised providing general representation ability for an instance of GIS, followed by an attentive operation that aggregates representations of instances corresponding to all historically interacted hotels of a user in a weighted manner. The second challenge is handled by the combined application of three proposed geography embedding modules in G2NET, each of which focuses on computing embeddings of location entities based on a certain aspect of geographical information of location entities. Moreover, a self-attention layer is deployed in G2NET, to capture correlations among historically interacted hotels of a user which provides non-trivial functionality of understanding the user's behaviors. Both offline and online experiments show that G2NET outperforms the state-of-the-art methods. G2NET has now been successfully deployed to provide the high-quality hotel search ranking service at Fliggy, one of the most popular OTPs in China, serving tens of millions of users.
Towards Adversarial Learning: From Evasion Attacks to Poisoning Attacks
Authors: Wentao Wang 0006, Han Xu 0002, Yuxuan Wan, Jie Ren 0019, Jiliang Tang
Venue: KDD
Year: 2022

Although deep neural networks (DNNs) have been successfully deployed in various real-world application scenarios, recent studies demonstrated that DNNs are extremely vulnerable to adversarial attacks. By introducing visually imperceptible perturbations into benign inputs, the attacker can manipulate a DNN model into providing wrong predictions. For practitioners who are applying DNNs into real-world problems, understanding the characteristics of different kinds of attacks will not only help them improve the robustness of their models, but also can help them have deeper insights into the working mechanism of DNNs. In this tutorial, we provide a comprehensive overview of the recent advances of adversarial learning, including both attack methods and defense methods. Specifically, we first give a detailed introduction of various types of evasion attacks, followed by a series of representative defense methods against evasion attacks. We then discuss different poisoning attack methods, followed by several defense methods against poisoning attacks. In addition, besides introducing attack methods working in the digital setting, we also introduce attack methods designed for threatening physical world systems. Finally, we present DeepRobust, a PyTorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field. Via our tutorial, audience can grasp the main ideas of adversarial attacks and defenses and obtain a deep insight of the robustness of DNNs. The tutorial official website is available at https://sites.google.com/view/kdd22-tutorial-adv-learn/.
RT-VeD: Real-Time VoI Detection on Edge Nodes with an Adaptive Model Selection Framework
Authors: Shuai Wang 0008, Junke Lu, Baoshen Guo, Zheng Dong 0002
Venue: KDD
Year: 2022

Real-time Vehicle-of-Interest (VoI) detection is becoming a core application to smart cities, especially in areas with high accident rates. With the increasing number of surveillance cameras and the advanced developments in edge computing, video tasks prefer to run on edge devices close to cameras due to the constraints of bandwidth, latency, and privacy concerns. However, resource-constrained edge devices are not competent for dynamic traffic loads with resource-intensive video analysis models. To address this challenge, we propose RT-VeD, a real-time VoI detection system based on the limited resources of edge nodes. RT-VeD utilizes multi-granularity computer vision models with different resource-accuracy trade-offs. It schedules vehicle tasks based on a traffic-aware actor-critic framework to maximize the accuracy of VoI detection while ensuring an inference time-bound. To evaluate the proposed RT-VeD, we conduct extensive experiments based on a real-world vehicle dataset. The experiment results demonstrate that our model outperforms other competitive methods.
Causal Discovery on Non-Euclidean Data
Authors: Jing Yang 0008, Kai Xie, Ning An 0001
Venue: KDD
Year: 2022

Researchers recently started developing deep learning models capable of handling non-Euclidean data. However, because of existing framework limitations on model representations and learning algorithms, few have explored causal discovery on non-Euclidean data. This paper is the first attempt to do so. We start by proposing the Non-Euclidean Causal Model (NECM) which describes the causal generative relationship of non-Euclidean data and creates a new tensor data type along with a mapping process for the non-Euclidean causal mechanism. Second, within the NECM, we propose the non-Euclidean Hybrid Learning (NEHL) method, a causal discovery algorithm relying on the concept of the ball covariance recently introduced in the statistics field. Third, we generate two types of non-Euclidean datasets: Functional Data and Symmetric Positive Definite manifold data in conformity with the NECM. Finally, experimental results on the generated data and real-world data demonstrate the effectiveness of the proposed NEHL method.
Efficient Join Order Selection Learning with Graph-based Representation
Authors: Jin Chen 0008, Guanyu Ye, Yan Zhao 0008, Shuncheng Liu 0001, Liwei Deng 0001, Xu Chen 0023, Rui Zhou 0015, Kai Zheng 0001
Venue: KDD
Year: 2022

Join order selection plays an important role in DBMS query optimizers. The problem aims to find the optimal join order with the minimum cost, and usually becomes an NP-hard problem due to the exponentially increasing search space. Recent advanced studies attempt to use deep reinforcement learning (DRL) to generate better join plans than the ones provided by conventional query optimizers. However, DRL-based methods require time-consuming training, which is not suitable for online applications that need frequent periodic re-training. In this paper, we propose a novel framework, namely efficient Join Order selection learninG with Graph-basEd Representation (JOGGER). We firstly construct a schema graph based on the primary-foreign key relationships, from which table representations are well learned to capture the correlations between tables. The second component is the state representation, where a graph convolutional network is utilized to encode the query graph and a tailored-tree-based attention module is designed to encode the join plan. To speed up the convergence of DRL training process, we exploit the idea of curriculum learning, in which queries are incrementally added into the training set according to the level of difficulties. We conduct extensive experiments on JOB and TPC-H datasets, which demonstrate the effectiveness and efficiency of the proposed solutions.
KRATOS: Context-Aware Cell Type Classification and Interpretation using Joint Dimensionality Reduction and Clustering
Authors: Zihan Zhou 0013, Zijia Du, Somali Chaterji
Venue: KDD
Year: 2022

A common workflow for single-cell RNA-sequencing (sc-RNA-seq) data analysis is to orchestrate a three-step pipeline. First, conduct a dimension reduction of the input cell profile matrix; second, cluster the cells in the latent space; and third, extract the "gene panels" that distinguish a certain cluster from others. This workflow has the primary drawback that the three steps are performed independently, neglecting the dependencies among the steps and among the marker genes or gene panels. In our system, KRATOS, we alter the three-step workflow to a two-step one, where we jointly optimize the first two steps and add the third (interpretability) step to form an integrated sc-RNA-seq analysis pipeline. We show that the more compact workflow of KRATOS extracts marker genes that can better discriminate the target cluster, distilling underlying mechanisms guiding cluster membership. In doing so, KRATOS is significantly better than the two SOTA baselines we compare against, specifically 5.62% superior to Global Counterfactual Explanation (GCE) [ICML-20], and 3.31% better than Adversarial Clustering Explanation (ACE) [ICML-21], measured by the AUROC of a kernel-SVM classifier. We opensource our code and datasets here: https://github.com/icanforce/single-cell-genomics-kratos.
Domain Adaptation with Dynamic Open-Set Targets
Authors: Jun Wu 0019, Jingrui He
Venue: KDD
Year: 2022

Open-set domain adaptation aims to improve the generalization performance of a learning algorithm on a target task of interest by leveraging the label information from a relevant source task with only a subset of classes. However, most existing works are designed for the static setting, and can be hardly extended to the dynamic setting commonly seen in many real-world applications. In this paper, we focus on the more realistic open-set domain adaptation setting with a static source task and a time evolving target task where novel unknown target classes appear over time. Specifically, we show that the classification error of the new target task can be tightly bounded in terms of positive-unlabeled classification errors for historical tasks and open-set domain discrepancy across tasks. By empirically minimizing the upper bound of the target error, we propose a novel positive-unlabeled learning based algorithm named OuterAdapter for dynamic open-set domain adaptation with time evolving unknown classes. Extensive experiments on various data sets demonstrate the effectiveness and efficiency of our proposed OuterAdapter algorithm over state-of-the-art domain adaptation baselines.
Lion: A GPU-Accelerated Online Serving System for Web-Scale Recommendation at Baidu
Authors: Hao Liu 0026, Qian Gao, Xiaochao Liao, Guangxing Chen, Hao Xiong, Silin Ren, Guobao Yang, Zhiwei Zha
Venue: KDD
Year: 2022

Deep Neural Network (DNN) based recommendation systems are widely used in the modern internet industry for a variety of services. However, the rapid expansion of application scenarios and the explosive global internet traffic growth have caused the industry to face increasing challenges to serve the complicated recommendation workflow regarding online recommendation efficiency and compute resource overhead. In this paper, we present a GPU-accelerated online serving system, namely Lion, which consists of the staged event-driven heterogeneous pipeline, unified memory manager, and automatic execution optimizer to handle web-scale traffic in a real-time and cost-effective way. Moreover, Lion provides a heterogeneous template library to enable fast development and migration for diverse in-house web-scale recommendation systems without requiring knowledge of heterogeneous programming. The system is currently deployed at Baidu, supporting over twenty recommendation services, including news feed, short video clips, and the search engine. Extensive experimental studies on five real-world deployed online recommendation services demonstrate the superiority of the proposed GPU-accelerated online serving system. Since launched in early 2020, Lion has answered billions of recommendation requests per day, and has helped Baidu successfully save millions of U.S. dollars in hardware and utility costs per year.
EdgeWatch: Collaborative Investigation of Data Integrity at the Edge based on Blockchain
Authors: Bo Li 0103, Qiang He 0001, Liang Yuan, Feifei Chen 0001, Lingjuan Lyu, Yun Yang 0001
Venue: KDD
Year: 2022

Mobile edge computing (MEC) offers the infrastructure for improving data caching performance structurally by deploying edge servers at the network edge within users' close geographic proximity. Popular data like viral videos can be cached on edge servers to serve users with low latency. Investigating the integrity of these edge data is critical and challenging as edge servers often suffer from unreliability and constrained resources. Meanwhile, EDI (edge data integrity) investigation must be performed by edge servers collaboratively at the edge to avoid excessive backhaul network traffic. There are two main challenges in practice: 1) there is a lack of Byzantine-tolerant collaborative investigation method; and 2) edge servers may be reluctant to collaborate without proper incentives. To tackle these challenges systematically, this paper proposes a novel scheme named EdgeWatch to enable robust and collaborative EDI investigation in a decentralized manner based on blockchain. Under EdgeWatch, edge servers collaborate on EDI investigation following a novel integrity consensus. A blockchain system comprises of three main components is built as the infrastructure to facilitate integrity consensus: 1) an incentive mechanism that motivates edge servers to participate in EDI investigation; 2) a reputation system that elects reliable leaders for block consensus; and 3) a leader randomization technique that protects leaders from targeted attacks. We evaluate it against three representative schemes experimentally. The results demonstrate the high precision, efficiency, and robustness of EdgeWatch.