OpenRaiser/Intern-Atlas huggingface dataset:
On Aligning Tuples for Regression
Authors: Chenguang Fang, Shaoxu Song, Yinan Mei, Ye Yuan, Jianmin Wang 0001
Venue: KDD
Year: 2022

Regression models are learned over multiple variables, e.g., using engine torque and speed to predict its fuel consumption. In practice, the values of these variables are often collected separately, e.g., by different sensors in a vehicle, and need to be aligned first in a tuple before learning. Unfortunately, flowing to various issues like network delays, values generated at the same time could be recorded with different timestamps, making the alignment diffcult. According to our study in a vehicle manufacturer, engine torque, speed and fuel consumption values are mostly not recorded with the same timestamps. Aligning tuples by simply concatenating values of variables with equal timestamps leads to limited data for learning regression model. To deal with timestamp variations, existing time series matching techniques rely on the similarity of values and timestamps, which unfortunately are very likely to be absent among the variables in regression (no similarity between engine torque and speed values). In this sense, we propose to bridge tuple alignment and regression. Rather than similar values and timestamps, we align the values of different variables in a tuple that (i) are recorded in a short period, i.e., time constraint, and more importantly (ii) coincide well with the regression model, known as model constraint. Our theoretical and technical contributions include (1) formulating the problem of tuple alignment with time and model constraints, (2) proving NP-completeness of the problem, (3) devising an approximation algorithm with performance guarantee, and (4) proposing efficient pruning strategies for the algorithm. Experiments over real world datasets, including the aforesaid engine data collected by a vehicle manufacturer, demonstrate that our proposal outperforms the existing methods on alignment accuracy and improves regression precision.
SAMCNet: Towards a Spatially Explainable AI Approach for Classifying MxIF Oncology Data
Authors: Majid Farhadloo, Carl Molnar, Gaoxiang Luo, Yan Li 0049, Shashi Shekhar 0001, Rachel L. Maus, Svetomir N. Markovic, Alexey A. Leontovich, Raymond Moore
Venue: KDD
Year: 2022

The goal of spatially explainable artificial intelligence (AI) classification approach is to build a classifier to distinguish two classes (e.g., responder, non-responder) based on the their spatial arrangements (e.g., spatial interactions between different point categories) given multi-category point data from two classes. This problem is important for generating hypotheses towards discovering new immunotherapies for cancer treatment as well as for other applications in biomedical research and microbial ecology. This problem is challenging due to an exponential number of category subsets which may vary in the strength of their spatial interactions. Most prior efforts on using human selected spatial association measures may not be sufficient for capturing the relevant spatial interactions (e.g., surrounded by) which may be of biological significance. In addition, the related deep neural networks are limited to category pairs and do not explore larger subsets of point categories. To overcome these limitations, we propose a Spatial-interaction Aware Multi-Category deep neural Network (SAMCNet) architecture and contribute novel local reference frame characterization and point pair prioritization layers for spatially explainable classification. Experimental results on multiple cancer datasets (e.g., MxIF) show that the proposed architecture provides higher prediction accuracy over baseline methods. A real-world case study demonstrates that the proposed work discovers patterns that are missed by the existing methods and has the potential to inspire new scientific discoveries.
3rd IADSS Workshop on Data Science Standards - Hiring, Assessing and Upskilling Data Science Talent
Authors: Usama M. Fayyad, Hamit Hamutcu
Venue: KDD
Year: 2022

The 3rd IADSS Workshop on Data Science Standards follows a tradition of two prior KDD workshops and the initial workshop at ICDM-2018. The theme of the 2022 workshop is: Hiring, Assessing and Upskilling Data Science Talent. Organized by the Initiative for Analytics and Data Science (IADSS.org) at KDD, the workshop provides a platform to discuss industry needs and practices around external and internal talent pipeline development in data science. We aim to provide an understanding of the data science job market, and the critical role of collaboration between academic institutions and industry to meet the increasing need for talent. IADSS conducts ongoing research in this domain. In this workshop, we share detailed findings and observations from this research. In addition to contribution from researchers and industry practitioners through an open call for papers, the workshop features several invited presentations and invited speakers. In order to achieve intended aim, interactive panels will discuss topics of interest and feedback from the workshop will be used to produce post-workshop learnings. This workshop is designed as a half-day working meeting with short talks, invited panels and discussion sessions to plan for future steps in the topic. Post conference, learnings from the workshop will be available at the workshop's home page: https://www.iadss.org/kdd2022
Large-Scale Acoustic Automobile Fault Detection: Diagnosing Engines Through Sound
Authors: Dennis Fedorishin, Justas Birgiolas, Deen Dayal Mohan, Livio Forte, Philip Schneider, Srirangaraj Setlur, Venu Govindaraju
Venue: KDD
Year: 2022

In this paper we present AMPNet, an acoustic abnormality detection model deployed at ACV Auctions to automatically identify engine faults of vehicles listed on the ACV Auctions platform. We investigate the problem of engine fault detection and discuss our approach of deep-learning based audio classification on a large-scale automobile dataset collected at ACV Auctions. Specifically, we discuss our data collection pipeline and its challenges, dataset preprocessing and training procedures, and deployment of our trained models into a production setting. We perform empirical evaluations of AMPNet and demonstrate that our framework is able to successfully capture various engine anomalies agnostic of vehicle type. Finally we demonstrate the effectiveness and impact of AMPNet in the real world, specifically showing a 20.85% reduction in vehicle arbitrations on ACV Auctions' live auction platform.
Precise Mobility Intervention for Epidemic Control Using Unobservable Information via Deep Reinforcement Learning
Authors: Tao Feng, Tong Xia, Xiaochen Fan, Huandong Wang, Zefang Zong, Yong Li 0008
Venue: KDD
Year: 2022

To control the outbreak of COVID-19, efficient individual mobility intervention for EPidemic Control (EPC) strategies are of great importance, which cut off the contact among people at epidemic risks and reduce infections by intervening the mobility of individuals. Reinforcement Learning (RL) is powerful for decision making, however, there are two major challenges in developing an RL-based EPC strategy: (1) the unobservable information about asymptomatic infections in the incubation period makes it difficult for RL's decision-making, and (2) the delayed rewards for RL causes the deficiency of RL learning. Since the results of EPC are reflected in both daily infections (including unobservable asymptomatic infections) and long-term cumulative cases of COVID-19, it is quite daunting to design an RL model for precise mobility intervention. In this paper, we propose a Variational hiErarcHICal reinforcement Learning method for Epidemic control via individual-level mobility intervention, namely Vehicle. To tackle the above challenges, Vehicle first exploits an information rebuilding module that consists of a contact-risk bipartite graph neural network and a variational LSTM to restore the unobservable information. The contact-risk bipartite graph neural network estimates the possibility of an individual being an asymptomatic infection and the risk of this individual spreading the epidemic, as the current state of RL. Then, the Variational LSTM further encodes the state sequence to model the latency of epidemic spreading caused by unobservable asymptomatic infections. Finally, a Hierarchical Reinforcement Learning framework is employed to train Vehicle, which contains dual-level agents to solve the delayed reward problem. Extensive experimental results demonstrate that Vehicle can effectively control the spread of the epidemic. Vehicle outperforms the state-of-the-art baseline methods with remarkably high-precision mobility interventions on both symptomatic and asymptomatic infections.
DP-GAT: A Framework for Image-based Disease Progression Prediction
Authors: Alex Foo, Wynne Hsu, Mong-Li Lee, Gavin Siew Wei Tan
Venue: KDD
Year: 2022

Predicting disease progression is key to provide stratified patient care and enable good utilization of healthcare resources. The availability of longitudinal images has enabled image-based disease progression prediction. In this work, we propose a framework called DP-GAT to identify regions containing significant biological structures and model the relationships among these regions as a graph along with their respective contexts. We perform reasoning via Graph Attention Network to generate representations that enable accurate disease progression prediction. We further extend DP-GAT to perform 3D medical volume segmentation. Experiments on real world medical image datasets demonstrate the advantage of our approach over strong baseline methods for both disease progression prediction and 3D segmentation tasks.
SIPF: Sampling Method for Inverse Protein Folding
Authors: Tianfan Fu, Jimeng Sun 0001
Venue: KDD
Year: 2022

Protein engineering has important applications in drug discovery. Among others, inverse protein folding is a fundamental task in protein design, which aims at generating protein's amino acid sequence given a 3D graph structure. However, most existing methods for inverse protein folding are based on sequential generative models and therefore limited in uncertainty quantification and exploration ability to the entire protein space. To address the issues, we propose a sampling method for inverse protein folding (SIPF). Specifically, we formulate inverse protein folding as a sampling problem and design two pretrained neural networks as Markov Chain Monte Carlo (MCMC) proposal distribution. To ensure sampling efficiency, we further design (i) an adaptive sampling scheme to select variables for sampling and (ii) an approximate target distribution as a surrogate of the unavailable target distribution. Empirical studies have been conducted to validate the effectiveness of SIPF, achieving 7.4% relative improvement on recovery rate and 6.4% relative reduction in perplexity compared to the best baseline.
The Battlefront of Combating Misinformation and Coping with Media Bias
Authors: Yi R. Fung 0001, Kung-Hsiang Huang, Preslav Nakov, Heng Ji 0001
Venue: KDD
Year: 2022

Misinformation is a pressing issue in modern society. It arouses a mixture of anger, distrust, confusion, and anxiety that cause damage on our daily life judgments and public policy decisions. While recent studies have explored various fake news detection and media bias detection techniques in attempts to tackle the problem, there remain many ongoing challenges yet to be addressed, as can be witnessed from the plethora of untrue and harmful content present during the COVID-19 pandemic, which gave rise to the first social-media infodemic, and the international crises of late. In this tutorial, we provide researchers and practitioners with a systematic overview of the frontier in fighting misinformation. Specifically, we dive into the important research questions of how to (i) develop a robust fake news detection system that not only fact-checks information pieces provable by background knowledge, but also reason about the consistency and the reliability of subtle details about emerging events; (ii) uncover the bias and the agenda of news sources to better characterize misinformation; as well as (iii) correct false information and mitigate news biases, while allowing diverse opinions to be expressed. Participants will learn about recent trends, representative deep neural network language and multimedia models, ready-to-use resources, remaining challenges, future research directions, and exciting opportunities to help make the world a better place, with safer and more harmonic information sharing.
Optimal Interpretable Clustering Using Oblique Decision Trees
Authors: Magzhan Gabidolla, Miguel Á. Carreira-Perpiñán
Venue: KDD
Year: 2022

Recent years have seen a renewed interest in interpretable machine learning, which seeks insight into how a model achieves a prediction. Here, we focus on the relatively unexplored case of interpretable clustering. In our approach, the cluster assignments of the training instances are constrained to be the output of a decision tree. This has two advantages: 1) it makes it possible to understand globally how an instance is mapped to a cluster, in particular to see which features are used for which cluster; 2) it forces the clusters to respect a hierarchical structure while optimizing the original clustering objective function. Rather than the traditional axisaligned trees, we use sparse oblique trees, which have far more modelling power, particularly with high-dimensional data, while remaining interpretable. Our approach applies to any clustering method which is defined by optimizing a cost function and we demonstrate it with two -means variants.
Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand
Authors: Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues 0001, Francisco C. Pereira, Marco Pavone 0001
Venue: KDD
Year: 2022

Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel needs. By centrally controlling a fleet of self-driving vehicles, these systems provide mobility service to customers and are currently starting to be deployed in a number of cities around the world. Current learning-based approaches for controlling AMoD systems are limited to the single-city scenario, whereby the service operator is allowed to take an unlimited amount of operational decisions within the same transportation system. However, real-world system operators can hardly afford to fully re-train AMoD controllers for every city they operate in, as this could result in a high number of poor-quality decisions during training, making the single-city strategy a potentially impractical solution. To address these limitations, we propose to formalize the multi-city AMoD problem through the lens of meta-reinforcement learning (meta-RL) and devise an actor-critic algorithm based on recurrent graph neural networks. In our approach, AMoD controllers are explicitly trained such that a small amount of experience within a new city will produce good system performance. Empirically, we show how control policies learned through meta-RL are able to achieve near-optimal performance on unseen cities by learning rapidly adaptable policies, thus making them more robust not only to novel environments, but also to distribution shifts common in real-world operations, such as special events, unexpected congestion, and dynamic pricing schemes.
The Power of (Statistical) Relational Thinking
Authors: Lise Getoor
Venue: KDD
Year: 2022

Taking into account relational structure during data mining can lead to better results, both in terms of quality and computational efficiency. This structure may be captured in the schema, in links between entities (e.g., graphs) or in rules describing the domain (e.g., knowledge graphs). Further, for richly structured prediction problems, there is often a need for a mix of both logical reasoning and statistical inference. In this talk, I will give an introduction to the field of Statistical Relational Learning (SRL), and I'll identify useful tips and tricks for exploiting structure in both the input and output space. I'll describe our recent work on highly scalable approaches for statistical relational inference. I'll close by introducing a broader interpretation of relational thinking that reveals new research opportunities (and challenges!).
Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology
Authors: Rahul Ghosh, Arvind Renganathan, Kshitij Tayal, Xiang Li, Ankush Khandelwal, Xiaowei Jia, Christopher J. Duffy, John Nieber, Vipin Kumar 0001
Venue: KDD
Year: 2022

Machine Learning is beginning to provide state-of-the-art performance in a range of environmental applications such as streamflow prediction in a hydrologic basin. However, building accurate broad-scale models for streamflow remains challenging in practice due to the variability in the dominant hydrologic processes, which are best captured by sets of process-related basin characteristics. Existing basin characteristics suffer from noise and uncertainty, among many other things, which adversely impact model performance. To tackle the above challenges, in this paper, we propose a novel Knowledge-guided Self-Supervised Learning (KGSSL) inverse framework to extract system characteristics from driver(input) and response(output) data. This first-of-its-kind framework achieves robust performance even when characteristics are corrupted or missing. We evaluate the KGSSL framework in the context of stream flow modeling using CAMELS (Catchment Attributes and MEteorology for Large-sample Studies) which is a widely used hydrology benchmark dataset. Specifically, KGSSL outperforms baseline by 16% in predicting missing characteristics. Furthermore, in the context of forward modelling, KGSSL inferred characteristics provide a 35% improvement in performance over a standard baseline when the static characteristic are unknown.
Workshop on Applied Machine Learning Management
Authors: Dmitri Goldenberg, Elena Sokolova, Shir Meir Lador, Amit Mandelbaum, Irina Vasilinetc, Ankit Jain
Venue: KDD
Year: 2022

Machine learning applications are rapidly adopted by industry leaders in any field. The growth of investment in AI-driven solutions created new challenges in managing Data Science and ML resources, people and projects as a whole. The discipline of managing applied machine learning teams, requires a healthy mix between agile product development tool-set and a long term research oriented mindset. The abilities of investing in deep research while at the same time connecting the outcomes to significant business results create a large knowledge based on management methods and best practices in the field. The Workshop on Applied Machine Learning Management brings together applied research managers from various fields to share methodologies and case-studies on management of ML teams, products, and projects, achieving business impact with advanced AI-methods.
Large-Scale Information Extraction under Privacy-Aware Constraints
Authors: Rajeev Gupta, Ranganath Kondapally
Venue: KDD
Year: 2022

In this digital age, people spend a significant portion of their lives online and this has led to an explosion of personal data of users due to their activities. Typically, this data is private and nobody else, except the user, is allowed to look at it. To provide better experience and assist users in their activities, it is critical to mine certain information from this data. This poses interesting and complex challenge from scalable information extraction point of view: building information extraction models where there is little data to learn from due to privacy constraints but need highly accurate models to run on a large amount of diverse data across different users. Anonymization of data is typically used to convert private data into publicly accessible data. But this may not always be feasible and may require complex differential privacy guarantees to be safe from any potential negative consequences. Further, the anonymization process needs to ensure that it retains sufficient information for modeling purposes post anonymization. Other techniques involve building extraction models using a small amount of seen (eyes-on) data with no privacy restrictions (hence, can be labeled) and a large amount of unseen (eyes-off) data which only a machine or a program can access. In this tutorial, we use emails as the canonical example of private data to explain in detail the challenges and solutions for scalable information extraction (IE) under privacy-aware constraints.
1st Workshop on End-End Customer Journey Optimization
Authors: Neha Gupta, Zhenyu Zhao, Mert Bay, Anbang Xu, Faisal Farooq
Venue: KDD
Year: 2022

At present, most machine learning research on customer optimization focuses on short term success of the customers by addressing questions such as - which users have a higher propensity to click? Where to place one ad/multiple contents on a web page? What is the most appropriate time to show content? There has been less/little thought put into building a coherent system for the long term/end-end customer optimization from acquisition by understanding a user's propensity to convert to a particular product at a certain time, to user's ability to be successful long term on a platform as measured by CLV (Customer Lifetime Value), to users' ability to buy more products (cross sell) on the same platform, and finally users propensity to churn. Currently, such models and algorithms are built in isolation to serve a single purpose which leads to inefficiencies in modeling and data pipelines. Also, most of the time the customer is not looked at as a single entity - but each product/subgroup within an organization (marketing, sales, product growth, go-to-market, product) considers the customer independently. This workshop aims to connect academic researchers and industrial practitioners who are working on, or interested in building holistic systems and solutions in the field of end to end customer journey optimization.
Real-Time Rideshare Driver Supply Values Using Online Reinforcement Learning
Authors: Benjamin Han, Hyungjun Lee, Sébastien Martin
Venue: KDD
Year: 2022

In this paper, we present Online Supply Values (OSV), a system for estimating the return of available rideshare drivers to match drivers to ride requests at Lyft. Because a future driver state can be accurately predicted from a request destination, it is possible to estimate the expected action value of assigning a ride request to an available driver as a Markov Decision Process using the Bellman Equation. These estimates are updated using temporal difference and are shown to adapt to changing marketplace conditions in real-time. While reinforcement learning has been studied for rideshare dispatch, fully-online approaches without offline priors or other guardrails had never been evaluated in the real world. This work presents the algorithmic changes needed to bridge this gap. OSV is now deployed globally as a core component of Lyft's dispatch matching system. Our A/B user experiments in major US cities measure a +(0.96±0.53)% increase in the request fulfillment rate and a +(0.73±0.22)% increase to profit per passenger session over the previous algorithm.