GenAI4ELab/papercli-papers huggingface dataset:
AI for Equitable, Data-Driven Decisions in Public Health
Authors: Bryan Wilder
Venue: AAAI 2023 (main)

Abstract: As exemplified by the COVID-19 pandemic, our health and wellbeing depend on a difficult-to-measure web of societal factors and individual behaviors. This effort requires new algorithmic and data-driven paradigms which span the full process of gathering costly data, learning models to understand and predict such interactions, and optimizing the use of limited resources in interventions. In response to these needs, I present methodological developments at the intersection of machine learning, optimization, and social networks which are motivated by on-the-ground collaborations on HIV prevention, tuberculosis treatment, and the COVID-19 response. Here, I give an overview of two lines of work.
Learning to See the Physical World
Authors: Jiajun Wu
Venue: AAAI 2023 (main)

Abstract: This paper is part of the AAAI-23 New Faculty Highlights. In my presentation, I will introduce my research goal, which is to build machines that see, interact with, and reason about the physical world just like humans. This problem, which we call physical scene understanding, involves three key topics that bridge research in computer science, AI, robotics, cognitive science, and neuroscience: Perception, Physical Interaction, and Reasoning.
Enhance Robustness of Machine Learning with Improved Efficiency
Authors: Yan Yan
Venue: AAAI 2023 (main)

Abstract: Robustness of machine learning, often referring to securing performance on different data, is always an active field due to the ubiquitous variety and diversity of data in practice. Many studies have been investigated to enhance the learning process robust in recent years. To this end, there is usually a trade-off that results in somewhat extra cost, e.g., more data samples, more complicated objective functions, more iterations to converge in optimization, etc. Then this problem boils down to finding a better trade-off under some conditions. My recent research focuses on robust machine learning with improved efficiency. Particularly, the efficiency here represents learning speed to find a model, and the number of data required to secure the robustness. In the talk, I will survey three pieces of my recent research by elaborating the algorithmic idea and theoretical analysis as technical contributions --- (i) epoch stochastic gradient descent ascent for min-max problems, (ii) stochastic optimization algorithm for non-convex inf-projection problems, and (iii) neighborhood conformal prediction. In the first two pieces of work, the proposed optimization algorithms are general and cover objective functions for robust machine learning. In the third one, I will elaborate an efficient conformal prediction algorithm that guarantee the robustness of prediction after model is trained. Particularly, the efficiency of conformal prediction is measured by its bandwidth.
The Analysis of Deep Neural Networks by Information Theory: From Explainability to Generalization
Authors: Shujian Yu
Venue: AAAI 2023 (main)

Abstract: Despite their great success in many artificial intelligence tasks, deep neural networks (DNNs) still suffer from a few limitations, such as poor generalization behavior for out-of-distribution (OOD) data and the "black-box" nature. Information theory offers fresh insights to solve these challenges. In this short paper, we briefly review the recent developments in this area, and highlight our contributions.
Towards Societal Impact of AI
Authors: Chuxu Zhang
Venue: AAAI 2023 (main)

Abstract: Artificial intelligence (AI) and Machine Learning (ML) have shown great success in many areas such as computer vision, natural language processing, and knowledge discovery. However, AI research to deliver social benefits and impacts is less explored while imminent needed. Guided by the United Nations’ Sustainable Development Goals, my research involves the development of advanced AI techniques, in particular Deep Graph Learning (DGL), to address the grand societal challenges and further apply them to various social good applications for improving our society and people’s daily life, namely DGL for Social Good (DGL4SG). Achieving the goal is not easy since challenges come from the increasing complexity of many factors including problems, data, and techniques, which require long-term and concentrated effort. DGL presents a good opportunity to build better solutions and tools due to its strong capability in learning and inferring graph data which is ideal for modeling many real-world social good systems. Fortunately, I have been working on DGL with continued contributions and impacts since my graduate study. The special research experience lifts me up to a unique position for conducting research that intersects AI, DGL, and social good, and pushing the field of DGL4SG forward.
Information Transfer in Multitask Learning, Data Augmentation, and Beyond
Authors: Hongyang R. Zhang
Venue: AAAI 2023 (main)

Abstract: A hallmark of human intelligence is that we continue to learn new information and then extrapolate the learned information onto new tasks and domains (see, e.g., Thrun and Pratt (1998)). While this is a fairly intuitive observation, formulating such ideas has proved to be a challenging research problem and continues to inspire new studies. Recently, there has been increasing interest in AI/ML about building models that generalize across tasks, even when they have some form of distribution shifts. How can we ground this research in a solid framework to develop principled methods for better practice? This talk will present my recent works addressing this research question. My talk will involve three parts: revisiting multitask learning from the lens of deep learning theory, designing principled methods for robust transfer, and algorithmic implications for data augmentation.
A New Challenge in Policy Evaluation
Authors: Shangtong Zhang
Venue: AAAI 2023 (main)

Abstract: This paper proposes a new challenge in policy evaluation: to improve the online data efficiency of Monte Carlo methods via information extracted from offline data while maintaining the unbiasedness of Monte Carlo methods.
Building Compositional Robot Autonomy with Modularity and Abstraction
Authors: Yuke Zhu
Venue: AAAI 2023 (main)

Abstract: This paper summarizes my research roadmap for building compositional robot autonomy with the principles of modularity and abstraction.
Accurate Detection of Weld Seams for Laser Welding in Real-World Manufacturing
Authors: Rabia Ali; Muhammad Sarmad; Jawad Tayyub; Alexander Vogel
Venue: AAAI 2023 (main)

Abstract: Welding is a fabrication process used to join or fuse two mechanical parts. Modern welding machines have automated lasers that follow a pre-defined weld seam path between the two parts to create a bond. Previous efforts have used simple computer vision edge detectors to automatically detect the weld seam edge on an image at the junction of two metals to be welded. However, these systems lack reliability and accuracy resulting in manual human verification of the detected edges. This paper presents a neural network architecture that automatically detects the weld seam edge between two metals with high accuracy. We augment this system with a pre-classifier that filters out anomalous workpieces (e.g., incorrect placement). Finally, we justify our design choices by evaluating against several existing deep network pipelines as well as proof through real-world use. We also describe in detail the process of deploying this system in a real-world shop floor including evaluation and monitoring. We make public a large, well-labeled laser seam dataset to perform deep learning-based edge detection in industrial settings.
Blending Advertising with Organic Content in E-commerce via Virtual Bids
Authors: Carlos Carrion; Zenan Wang; Harikesh Nair; Xianghong Luo; Yulin Lei; Peiqin Gu; Xiliang Lin; Wenlong Chen; Junsheng Jin; Fanan Zhu; Changping Peng; Yongjun Bao; Zhangang Lin; Weipeng Yan; Jingping Shao
Venue: AAAI 2023 (main)

Abstract: It has become increasingly common that sponsored content (i.e., paid ads) and non-sponsored content are jointly displayed to users, especially on e-commerce platforms. Thus, both of these contents may interact together to influence their engagement behaviors. In general, sponsored content helps brands achieve their marketing goals and provides ad revenue to the platforms. In contrast, non-sponsored content contributes to the long-term health of the platform through increasing users' engagement. A key conundrum to platforms is learning how to blend both of these contents allowing their interactions to be considered and balancing these business objectives. This paper proposes a system built for this purpose and applied to product detail pages of JD.COM, an e-commerce company. This system achieves three objectives: (a) Optimization of competing business objectives via Virtual Bids allowing the expressiveness of the valuation of the platform for these objectives. (b) Modeling the users' click behaviors considering explicitly the influence exerted by the sponsored and non-sponsored content displayed alongside through a deep learning approach. (c) Consideration of a Vickrey-Clarke-Groves (VCG) Auction design compatible with the allocation of ads and its induced externalities. Experiments are presented demonstrating the performance of the proposed system. Moreover, our approach is fully deployed and serves all traffic through JD.COM's mobile application.
Efficient Training of Large-Scale Industrial Fault Diagnostic Models through Federated Opportunistic Block Dropout
Authors: Yuanyuan Chen; Zichen Chen; Sheng Guo; Yansong Zhao; Zelei Liu; Pengcheng Wu; Chengyi Yang; Zengxiang Li; Han Yu
Venue: AAAI 2023 (main)

Abstract: Artificial intelligence (AI)-empowered industrial fault diagnostics is important in ensuring the safe operation of industrial applications. Since complex industrial systems often involve multiple industrial plants (possibly belonging to different companies or subsidiaries) with sensitive data collected and stored in a distributed manner, collaborative fault diagnostic model training often needs to leverage federated learning (FL). As the scale of the industrial fault diagnostic models are often large and communication channels in such systems are often not exclusively used for FL model training, existing deployed FL model training frameworks cannot train such models efficiently across multiple institutions. In this paper, we report our experience developing and deploying the Federated Opportunistic Block Dropout (FedOBD) approach for industrial fault diagnostic model training. By decomposing large-scale models into semantic blocks and enabling FL participants to opportunistically upload selected important blocks in a quantized manner, it significantly reduces the communication overhead while maintaining model performance. Since its deployment in ENN Group in February 2022, FedOBD has served two coal chemical plants across two cities in China to build industrial fault prediction models. It helped the company reduce the training communication overhead by over 70% compared to its previous AI Engine, while maintaining model performance at over 85% test F1 score. To our knowledge, it is the first successfully deployed dropout-based FL approach.
AmnioML: Amniotic Fluid Segmentation and Volume Prediction with Uncertainty Quantification
Authors: Daniel Csillag; Lucas Monteiro Paes; Thiago Ramos; João Vitor Romano; Rodrigo Schuller; Roberto B. Seixas; Roberto I. Oliveira; Paulo Orenstein
Venue: AAAI 2023 (main)

Abstract: Accurately predicting the volume of amniotic fluid is fundamental to assessing pregnancy risks, though the task usually requires many hours of laborious work by medical experts. In this paper, we present  AmnioML, a machine learning solution that leverages deep learning and conformal prediction to output fast and accurate volume estimates and segmentation masks from fetal MRIs with Dice coefficient over 0.9. Also, we make available a novel, curated dataset for fetal MRIs with 853 exams and benchmark the performance of many recent deep learning architectures. In addition, we introduce a conformal prediction tool that yields narrow predictive intervals with theoretically guaranteed coverage, thus aiding doctors in detecting pregnancy risks and saving lives. A successful case study of AmnioML deployed in a medical setting is also reported. Real-world clinical benefits include up to 20x segmentation time reduction, with most segmentations deemed by doctors as not needing any further manual refinement. Furthermore, AmnioML's volume predictions were found to be highly accurate in practice, with mean absolute error below 56mL and tight predictive intervals, showcasing its impact in reducing pregnancy complications.
A Robust and Scalable Stacked Ensemble for Day-Ahead Forecasting of Distribution Network Losses
Authors: Gunnar Grotmol; Eivind Hovdegård Furdal; Nisha Dalal; Are Løkken Ottesen; Ella-Lovise Hammervold Rørvik; Martin Mølnå; Gleb Sizov; Odd Erik Gundersen
Venue: AAAI 2023 (main)

Abstract: Accurate day-ahead nominations of grid losses in electrical distribution networks are important to reduce the societal cost of these losses. We present a modification of the CatBoost ensemble-based system for day-ahead grid loss prediction detailed in Dalal et al. (2020), making four main changes. Base models predict on the log-space of the target, to ensure non-negative predictions. The model ensemble is changed to include different model types, for increased ensemble variance. Feature engineering is applied to consumption and weather forecasts, to improve base model performance. Finally, a non-negative least squares-based stacking method that uses as many available models as possible for each prediction is introduced, to achieve an improved model selection that is robust to missing data.
When deployed for over three months in 2022, the resulting system reduced mean absolute error by 10.7% compared to the system from Dalal et al. (2020), a reduction from 5.05 to 4.51 MW. With no tuning of machine learning parameters, the system was also extended to three new grids, where it achieved similar relative error as on the old grids. Our system is robust and easily scalable, and our proposed stacking method could provide improved performance in applications outside grid loss.
Developing the Wheel Image Similarity Application with Deep Metric Learning: Hyundai Motor Company Case
Authors: Kyung Pyo Kang; Ga Hyeon Jeong; Jeong Hoon Eom; Soon Beom Kwon; Jae Hong Park
Venue: AAAI 2023 (main)

Abstract: The global automobile market experiences quick changes in design preferences. In response to the demand shifts, manufacturers now try to apply new technologies to bring a novel design to market faster. In this paper, we introduce a novel application that performs a similarity verification task of wheel designs using an AI model and cloud computing technology. At Jan 2022, we successfully implemented the application to the wheel design process of Hyundai Motor Company’s design team and shortened the similarity verification time by 90% to a maximum of 10 minutes. We believe that this study is the first to build a wheel image database and empirically prove that the cross-entropy loss does similar tasks as the pairwise losses do in the embedding space. As a result, we successfully automated Hyundai Motor’s verification task of wheel design similarity. With a few clicks, the end-users in Hyundai Motor could take advantage of our application.
NewsPanda: Media Monitoring for Timely Conservation Action
Authors: Sedrick Scott Keh; Zheyuan Ryan Shi; David J. Patterson; Nirmal Bhagabati; Karun Dewan; Areendran Gopala; Pablo Izquierdo; Debojyoti Mallick; Ambika Sharma; Pooja Shrestha; Fei Fang
Venue: AAAI 2023 (main)

Abstract: Non-governmental organizations for environmental conservation have a significant interest in monitoring conservation-related media and getting timely updates about infrastructure construction projects as they may cause massive impact to key conservation areas. Such monitoring, however, is difficult and time-consuming. We introduce NewsPanda, a toolkit which automatically detects and analyzes online articles related to environmental conservation and infrastructure construction. We fine-tune a BERT-based model using active learning methods and noise correction algorithms to identify articles that are relevant to conservation and infrastructure construction. For the identified articles, we perform further analysis, extracting keywords and finding potentially related sources. NewsPanda has been successfully deployed by the World Wide Fund for Nature teams in the UK, India, and Nepal since February 2022. It currently monitors over 80,000 websites and 1,074 conservation sites across India and Nepal, saving more than 30 hours of human efforts weekly. We have now scaled it up to cover 60,000 conservation sites globally.
Trustworthy Residual Vehicle Value Prediction for Auto Finance
Authors: Mihye Kim; Jimyung Choi; Jaehyun Kim; Wooyoung Kim; Yeonung Baek; Gisuk Bang; Kwangwoon Son; Yeonman Ryou; Kee-Eung Kim
Venue: AAAI 2023 (main)

Abstract: The residual value (RV) of a vehicle refers to its estimated worth at some point in the future. It is a core component in every auto financial product, used to determine the credit lines and the leasing rates. As such, an accurate prediction of RV is critical for the auto finance industry, since it can pose a risk of revenue loss by over-prediction or make the financial product incompetent by under-prediction. Although there are a number of prior studies on training machine learning models on a large amount of used car sales data, we had to cope with real-world operational requirements such as compliance with regulations (i.e. monotonicity of output with respect to a subset of features) and generalization to unseen input (i.e. new and rare car models). In this paper, we describe how we coped with these practical challenges and created value for our business at Hyundai Capital Services, the top auto financial service provider in Korea.
A Dataset and Baseline Approach for Identifying Usage States from Non-intrusive Power Sensing with MiDAS IoT-Based Sensors
Authors: Bharath Muppasani; Cheyyur Jaya Anand; Chinmayi Appajigowda; Biplav Srivastava; Lokesh Johri
Venue: AAAI 2023 (main)

Abstract: The state identification problem seeks to identify power usage patterns of any system, like buildings or factories, of interest. In this challenge paper, we make power usage dataset available from 8 institutions in manufacturing, education and medical institutions from the US and India, and an initial unsupervised machine learning based solution as a baseline for the community to accelerate research in this area.
Real-Time Detection of Robotic Traffic in Online Advertising
Authors: Anand Muralidhar; Sharad Chitlangia; Rajat Agarwal; Muneeb Ahmed
Venue: AAAI 2023 (main)

Abstract: Detecting robotic traffic at scale on online ads needs an approach that is scalable, comprehensive, precise, and can rapidly respond to changing traffic patterns. In this paper we describe SLIDR or SLIce-Level Detection of Robots, a real-time deep neural network model trained with weak supervision to identify invalid clicks on online ads. We ensure fairness across different traffic slices by formulating a convex optimization problem that allows SLIDR to achieve optimal performance on individual traffic slices with a budget on overall false positives. SLIDR has been deployed since 2021 and safeguards advertiser campaigns on Amazon against robots clicking on ads on the e-commerce site. We describe some of the important lessons learned by deploying SLIDR that include guardrails that prevent updates of anomalous models and disaster recovery mechanisms to mitigate or correct decisions made by a faulty model.
Dynamic Pricing with Volume Discounts in Online Settings
Authors: Marco Mussi; Gianmarco Genalti; Alessandro Nuara; Francesco Trovó; Marcello Restelli; Nicola Gatti
Venue: AAAI 2023 (main)

Abstract: According to the main international reports, more pervasive industrial and business-process automation, thanks to machine learning and advanced analytic tools, will unlock more than 14 trillion USD worldwide annually by 2030. In the specific case of pricing problems, which constitute the class of problems we investigate in this paper, the estimated unlocked value will be about 0.5 trillion USD per year. In particular, this paper focuses on pricing in e-commerce when the objective function is profit maximization and only transaction data are available. This setting is one of the most common in real-world applications. Our work aims to find a pricing strategy that allows defining optimal prices at different volume thresholds to serve different classes of users. Furthermore, we face the major challenge, common in real-world settings, of dealing with limited data available. We design a two-phase online learning algorithm, namely PVD-B, capable of exploiting the data incrementally in an online fashion. The algorithm first estimates the demand curve and retrieves the optimal average price, and subsequently it offers discounts to differentiate the prices for each volume threshold. We ran a real-world 4-month-long A/B testing experiment in collaboration with an Italian e-commerce company, in which our algorithm PVD-B - corresponding to A configuration - has been compared with human pricing specialists - corresponding to B configuration. At the end of the experiment, our algorithm produced a total turnover of about 300 KEuros, outperforming the B configuration performance by about 55%. The Italian company we collaborated with decided to adopt our algorithm for more than 1,200 products since January 2022.
An Explainable Forecasting System for Humanitarian Needs Assessment
Authors: Rahul Nair; Bo Madsen; Alexander Kjærum
Venue: AAAI 2023 (main)

Abstract: We present a machine learning system for forecasting forced displacement populations deployed at the Danish Refugee Council (DRC). The system, named Foresight, supports long term forecasts aimed at humanitarian response planning. It is explainable, providing evidence and context supporting the forecast. Additionally, it supports scenarios, whereby analysts are able to generate forecasts under alternative conditions. The system has been in deployment since early 2020 and powers several downstream business functions within DRC. It is central to our annual Global Displacement Report which informs our response planning. We describe the system, key outcomes, lessons learnt, along with technical limitations and challenges in deploying machine learning systems in the humanitarian sector.