GenAI4ELab/papercli-papers huggingface dataset:
Temporal Knowledge Graph Completion: A Survey
Authors: Borui Cai; Yong Xiang; Longxiang Gao; He Zhang; Yunfeng Li; Jianxin Li
Venue: IJCAI 2023 (main)

Abstract: Knowledge graph completion (KGC) predicts missing links and is crucial for real-life knowledge graphs, which widely suffer from incompleteness. KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time. Emerging methods have recently shown improved prediction results by further incorporating the temporal validity of facts; namely, temporal knowledge graph completion (TKGC). With this temporal information, TKGC methods explicitly learn the dynamic evolution of the knowledge graph that KGC methods fail to capture. In this paper, for the first time, we comprehensively summarize the recent advances in TKGC research. First, we detail the background of TKGC, including the preliminary knowledge, benchmark datasets, and evaluation metrics. Then, we summarize existing TKGC methods based on how the temporal validity of facts is used to capture the temporal dynamics. Finally, we conclude the paper and present future research directions of TKGC.
Assessing and Enforcing Fairness in the AI Lifecycle
Authors: Roberta Calegari; Gabriel G. Castañé; Michela Milano; Barry O'Sullivan
Venue: IJCAI 2023 (main)

Abstract: A significant challenge in detecting and mitigating bias is creating a mindset amongst AI developers to address unfairness. The current literature on fairness is broad, and the learning curve to distinguish where to use existing metrics and techniques for bias detection or mitigation is difficult. This survey systematises the state-of-the-art about distinct notions of fairness and relative techniques for bias mitigation according to the AI lifecycle. Gaps and challenges identified during the development of this work are also discussed.
Anti-unification and Generalization: A Survey
Authors: David M. Cerna; Temur Kutsia
Venue: IJCAI 2023 (main)

Abstract: Anti-unification (AU) is a fundamental operation for generalization computation used for inductive inference. It is the dual operation to unification, an operation at the foundation of automated theorem proving. Interest in AU from the AI and related communities is growing, but without a systematic study of the concept nor surveys of existing work, investigations often resort to developing application-specific methods that existing approaches may cover. We provide the first survey of AU research and its applications and a general framework for categorizing existing and future developments.
Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs
Authors: Mingyang Chen; Wen Zhang; Yuxia Geng; Zezhong Xu; Jeff Z. Pan; Huajun Chen
Venue: IJCAI 2023 (main)

Abstract: Knowledge graphs (KGs) have become valuable knowledge resources in various applications, and knowledge graph embedding (KGE) methods have garnered increasing attention in recent years. However, conventional KGE methods still face challenges when it comes to handling unseen entities or relations during model testing. To address this issue, much effort has been devoted to various fields of KGs. In this paper, we use a set of general terminologies to unify these methods and refer to them collectively as Knowledge Extrapolation. We comprehensively summarize these methods, classified by our proposed taxonomy, and describe their interrelationships. Additionally, we introduce benchmarks and provide comparisons of these methods based on aspects that are not captured by the taxonomy. Finally, we suggest potential directions for future research.
A Survey on Proactive Dialogue Systems: Problems, Methods, and Prospects
Authors: Yang Deng; Wenqiang Lei; Wai Lam; Tat-Seng Chua
Venue: IJCAI 2023 (main)

Abstract: Proactive dialogue systems, related to a wide range of real-world conversational applications, equip the conversational agent with the capability of leading the conversation direction towards achieving pre-defined targets or fulfilling certain goals from the system side. It is empowered by advanced techniques to progress to more complicated tasks that require strategical and motivational interactions. In this survey, we provide a comprehensive overview of the prominent problems and advanced designs for conversational agent's proactivity in different types of dialogues. Furthermore, we discuss challenges that meet the real-world application needs but require a greater research focus in the future. We hope that this first survey of proactive dialogue systems can provide the community with a quick access and an overall picture to this practical problem, and stimulate more progresses on conversational AI to the next level.
Machine Learning for Cutting Planes in Integer Programming: A Survey
Authors: Arnaud Deza; Elias B. Khalil
Venue: IJCAI 2023 (main)

Abstract: We survey recent work on machine learning (ML) techniques for selecting cutting planes (or cuts) in mixed-integer linear programming (MILP). Despite the availability of various classes of cuts, the task of choosing a set of cuts to add to the linear programming (LP) relaxation at a given node of the branch-and-bound (B&B) tree has defied both formal and heuristic solutions to date. ML offers a promising approach for improving the cut selection process by using data to identify promising cuts that accelerate the solution of MILP instances. This paper presents an overview of the topic, highlighting recent advances in the literature, common approaches to data collection, evaluation, and ML model architectures. We analyze the empirical results in the literature in an attempt to quantify the progress that has been made and conclude by suggesting avenues for future research.
Game-theoretic Mechanisms for Eliciting Accurate Information
Authors: Boi Faltings
Venue: IJCAI 2023 (main)

Abstract: Artificial Intelligence often relies on information obtained from others through crowdsourcing, federated learning, or data markets. It is crucial to ensure that this data is accurate. Over the past 20 years, a variety of incentive mechanisms have been developed that use game theory to reward the accuracy of contributed data. These techniques are applicable to many settings where AI uses contributed data. This survey categorizes the different techniques and their properties, and shows their limits and tradeoffs. It identifies open issues and points to possible directions to address these.
A Survey on Dataset Distillation: Approaches, Applications and Future Directions
Authors: Jiahui Geng; Zongxiong Chen; Yuandou Wang; Herbert Woisetschlaeger; Sonja Schimmler; Ruben Mayer; Zhiming Zhao; Chunming Rong
Venue: IJCAI 2023 (main)

Abstract: Dataset distillation is attracting more attention in machine learning as training sets continue to grow and the cost of training state-of-the-art models becomes increasingly high. By synthesizing datasets with high information density, dataset distillation offers a range of potential applications, including support for continual learning, neural architecture search, and privacy protection. Despite recent advances, we lack a holistic understanding of the approaches and applications. Our survey aims to bridge this gap by first proposing a taxonomy of dataset distillation, characterizing existing approaches, and then systematically reviewing the data modalities, and related applications. In addition, we summarize the challenges and discuss future directions for this field of research.
A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challenges
Authors: Usman Gohar; Lu Cheng
Venue: IJCAI 2023 (main)

Abstract: The widespread adoption of Machine Learning systems, especially in more decision-critical applications such as criminal sentencing and bank loans, has led to increased concerns about fairness implications. Algorithms and metrics have been developed to mitigate and measure these discriminations. More recently, works have identified a more challenging form of bias called intersectional bias, which encompasses multiple sensitive attributes, such as race and gender, together. In this survey, we review the state-of-the-art in intersectional fairness. We present a taxonomy for intersectional notions of fairness and mitigation. Finally, we identify the key challenges and provide researchers with guidelines for future directions.
Survey on Online Streaming Continual Learning
Authors: Nuwan Gunasekara; Bernhard Pfahringer; Heitor Murilo Gomes; Albert Bifet
Venue: IJCAI 2023 (main)

Abstract: Stream Learning (SL) attempts to learn from a data stream efficiently. A data stream learning algorithm should adapt to input data distribution shifts without sacrificing accuracy. These distribution shifts are known as ”concept drifts” in the literature. SL provides many supervised, semi-supervised, and unsupervised methods for detecting and adjusting to concept drift. On the other hand, Continual Learning (CL) attempts to preserve previous knowledge while performing well on the current concept when confronted with concept drift. In Online Continual Learning (OCL), this learning happens online. This survey explores the intersection of those two online learning paradigms to find synergies. We identify this intersection as Online Streaming Continual Learning (OSCL). The study starts with a gentle introduction to SL and then explores CL. Next, it explores OSCL from SL and OCL perspectives to point out new research trends and give directions for future research.
Graph-based Molecular Representation Learning
Authors: Zhichun Guo; Kehan Guo; Bozhao Nan; Yijun Tian; Roshni G. Iyer; Yihong Ma; Olaf Wiest; Xiangliang Zhang; Wei Wang; Chuxu Zhang; Nitesh V. Chawla
Venue: IJCAI 2023 (main)

Abstract: Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vectors preserving the molecular structures and features, on top of which the downstream tasks (e.g., property prediction) can be performed. Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning. In this survey, we systematically review these graph-based molecular representation techniques, especially the methods incorporating chemical domain knowledge. Specifically, we first introduce the features of 2D and 3D molecular graphs. Then we summarize and categorize MRL methods into three groups based on their input. Furthermore, we discuss some typical chemical applications supported by MRL. To facilitate studies in this fast-developing area, we also list the benchmarks and commonly used datasets in the paper. Finally, we share our thoughts on future research directions.
Towards Utilitarian Online Learning -- A Review of  Online Algorithms in Open Feature Space
Authors: Yi He; Christian Schreckenberger; Heiner Stuckenschmidt; Xindong Wu
Venue: IJCAI 2023 (main)

Abstract: Human intelligence comes from the capability to describe and make sense of the world surrounding us, often in a lifelong manner. Online Learning (OL) allows a model to simulate this capability, which involves processing data in sequence, making predictions, and learning from predictive errors. However, traditional OL assumes a fixed set of features to describe data, which can be restrictive. In reality, new features may emerge and old features may vanish or become obsolete, leading to an open feature space. This dynamism can be caused by more advanced or outdated technology for sensing the world, or it can be a natural process of evolution. This paper reviews recent breakthroughs that strived to enable OL in open feature spaces, referred to as Utilitarian Online Learning (UOL). We taxonomize existing UOL models into three categories, analyze their pros and cons, and discuss their application scenarios. We also benchmark the performance of representative UOL models, highlighting open problems, challenges, and potential future directions of this emerging topic.
A Survey on User Behavior Modeling in Recommender Systems
Authors: Zhicheng He; Weiwen Liu; Wei Guo; Jiarui Qin; Yingxue Zhang; Yaochen Hu; Ruiming Tang
Venue: IJCAI 2023 (main)

Abstract: User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been extensively used in recommender systems. Crucial interactive patterns between users and items have been exploited, which brings compelling improvements in many recommendation tasks. In this paper, we attempt to provide a thorough survey of this research topic. We start by reviewing the research background of UBM. Then, we provide a systematic taxonomy of existing UBM research works, which can be categorized into four different directions including Conventional UBM, Long-Sequence UBM, Multi-Type UBM, and UBM with Side Information. Within each direction, representative models and their strengths and weaknesses are comprehensively discussed. Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions. Finally, we summarize the survey and discuss the future prospects of this field.
Benchmarking eXplainable AI - A Survey on Available Toolkits and Open Challenges
Authors: Phuong Quynh Le; Meike Nauta; Van Bach Nguyen; Shreyasi Pathak; Jörg Schlötterer; Christin Seifert
Venue: IJCAI 2023 (main)

Abstract: The goal of Explainable AI (XAI) is to make the reasoning of a machine learning model accessible to humans, such that users of an AI system can evaluate and judge the underlying model. Due to the blackbox nature of XAI methods it is, however, hard to disentangle the contribution of a model and the explanation method to the final output. It might be unclear on whether an unexpected output is caused by the model or the explanation method. Explanation models, therefore, need to be evaluated in technical (e.g. fidelity to the model) and user-facing (correspondence to domain knowledge) terms. A recent survey has identified 29 different automated approaches to quantitatively evaluate explanations. In this work, we take an additional perspective and analyse which toolkits and data sets are available. We investigate which evaluation metrics are implemented in the toolkits and whether they produce the same results. We find that only a few aspects of explanation quality are currently covered, data sets are rare and evaluation results are not comparable across different toolkits. Our survey can serve as a guide for the XAI community for identifying future directions of research, and most notably, standardisation of evaluation.
Curriculum Graph Machine Learning: A Survey
Authors: Haoyang Li; Xin Wang; Wenwu Zhu
Venue: IJCAI 2023 (main)

Abstract: Graph machine learning has been extensively studied in both academia and industry. However, in the literature, most existing graph machine learning models are designed to conduct training with data samples in a random order, which may suffer from suboptimal performance due to ignoring the importance of different graph data samples and their training orders for the model optimization status. To tackle this critical problem, curriculum graph machine learning (Graph CL), which integrates the strength of graph machine learning and curriculum learning, arises and attracts an increasing amount of attention from the research community. Therefore, in this paper, we comprehensively overview approaches on Graph CL and present a detailed survey of recent advances in this direction. Specifically, we first discuss the key challenges of Graph CL and provide its formal problem definition. Then, we categorize and summarize existing methods into three classes based on three kinds of graph machine learning tasks, i.e., node-level, link-level, and graph-level tasks. Finally, we share our thoughts on future research directions. To the best of our knowledge, this paper is the first survey for curriculum graph machine learning.
A Survey on Out-of-Distribution Evaluation of Neural NLP Models
Authors: Xinzhe Li; Ming Liu; Shang Gao; Wray Buntine
Venue: IJCAI 2023 (main)

Abstract: Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models. However, a comprehensive, integrated discussion of the three research lines is still lacking in the literature. This survey will 1) compare the three lines of research under a unifying definition; 2) summarize their data-generating processes and evaluation protocols for each line of research; and 3) emphasize the challenges and opportunities for future work.
Diffusion Models for Non-autoregressive Text Generation: A Survey
Authors: Yifan Li; Kun Zhou; Wayne Xin Zhao; Ji-Rong Wen
Venue: IJCAI 2023 (main)

Abstract: Non-autoregressive (NAR) text generation has attracted much attention in the field of natural language processing, which greatly reduces the inference latency but has to sacrifice the generation accuracy. Recently, diffusion models, a class of latent variable generative models, have been introduced into NAR text generation, showing an improved text generation quality. In this survey, we review the recent progress in diffusion models for NAR text generation. As the background, we first present the general definition of diffusion models and the text diffusion models, and then discuss their merits for NAR generation. As the core content, we further introduce two mainstream diffusion models in existing work of text diffusion, and review the key designs of the diffusion process. Moreover, we discuss the utilization of pre-trained language models (PLMs) for text diffusion models and introduce optimization techniques for text data. Finally, we discuss several promising directions and conclude this paper. Our survey aims to provide researchers with a systematic reference of related research on text diffusion models for NAR generation. We also demonstrate our collection of text diffusion models at https://github.com/RUCAIBox/Awesome-Text-Diffusion-Models.
Generative Diffusion Models on Graphs: Methods and Applications
Authors: Chengyi Liu; Wenqi Fan; Yunqing Liu; Jiatong Li; Hang Li; Hui Liu; Jiliang Tang; Qing Li
Venue: IJCAI 2023 (main)

Abstract: Diffusion models, as a novel generative paradigm, have achieved remarkable success in various image generation tasks such as image inpainting, image-to-text translation, and video generation. Graph generation is a crucial computational task on graphs with numerous real-world applications. It aims to learn the distribution of given graphs and then generate new graphs. Given the great success of diffusion models in image generation, increasing efforts have been made to leverage these techniques to advance graph generation in recent years. In this paper, we first provide a comprehensive overview of generative diffusion models on graphs, In particular, we review representative algorithms for three variants of graph diffusion models, i.e., Score Matching with Langevin Dynamics (SMLD), Denoising Diffusion Probabilistic Model (DDPM), and Score-based Generative Model (SGM). Then, we summarize the major applications of generative diffusion models on graphs with a specific focus on molecule and protein modeling. Finally, we discuss promising directions in generative diffusion models on graph-structured data.
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities
Authors: Chuang Liu; Yibing Zhan; Jia Wu; Chang Li; Bo Du; Wenbin Hu; Tongliang Liu; Dacheng Tao
Venue: IJCAI 2023 (main)

Abstract: Graph neural networks have emerged as a leading architecture for many graph-level tasks, such as graph classification and graph generation. As an essential component of the architecture, graph pooling is indispensable for obtaining a holistic graph-level representation of the whole graph. Although a great variety of methods have been proposed in this promising and fast-developing research field, to the best of our knowledge, little effort has been made to systematically summarize these works. To set the stage for the development of future works, in this paper, we attempt to fill this gap by providing a broad review of recent methods for graph pooling. Specifically, 1) we first propose a taxonomy of existing graph pooling methods with a mathematical summary for each category; 2) then, we provide an overview of the libraries related to graph pooling, including the commonly used datasets, model architectures for downstream tasks, and open-source implementations; 3) next, we further outline the applications that incorporate the idea of graph pooling in a variety of domains; 4) finally, we discuss certain critical challenges facing current studies and share our insights on future potential directions for research on the improvement of graph pooling.
A Unified View of Deep Learning for Reaction and Retrosynthesis Prediction: Current Status and Future Challenges
Authors: Ziqiao Meng; Peilin Zhao; Yang Yu; Irwin King
Venue: IJCAI 2023 (main)

Abstract: Reaction and retrosynthesis prediction are two fundamental tasks in computational chemistry. In recent years, these two tasks have attracted great attentions from both machine learning and drug discovery communities. Various deep learning approaches have been proposed to tackle these two problems and achieved initial success. In this survey, we conduct a comprehensive investigation on advanced deep learning-based reaction and retrosynthesis prediction models. We first summarize the design mechanism, strengths and weaknesses of the state-of-the-art approaches. Then we further discuss limitations of current solutions and open challenges in the problem itself. Last but not the least, we present some promising directions to facilitate future research. To our best knowledge, this paper is the first comprehensive and systematic survey on unified understanding of reaction and retrosynthesis prediction.
Complexity Results and Exact Algorithms for Fair Division of Indivisible Items: A Survey
Authors: Trung Thanh Nguyen; Jörg Rothe
Venue: IJCAI 2023 (main)

Abstract: Fair allocation of indivisible goods is a central topic in many AI applications. Unfortunately, the corresponding problems are known to be NP-hard for many fairness concepts, so unless P = NP, exact polynomial-time algorithms cannot exist for them. In practical applications, however, it would be highly desirable to find exact solutions as quickly as possible. This motivates the study of algorithms that—even though they only run in exponential time—are as fast as possible and exactly solve such problems. We present known complexity results for them and give a survey of important techniques for designing such algorithms, mainly focusing on four common fairness notions: max-min fairness, maximin share, maximizing Nash social welfare, and envy-freeness. We also highlight the most challenging open problems for future work.
What Lies beyond the Pareto Front? A Survey on Decision-Support Methods for Multi-Objective Optimization
Authors: Zuzanna Osika; Jazmin Zatarain Salazar; Diederik M. Roijers; Frans A. Oliehoek; Pradeep K. Murukannaiah
Venue: IJCAI 2023 (main)

Abstract: We present a review that unifies decision-support methods for exploring the solutions produced by multi-objective optimization (MOO) algorithms. As MOO is applied to solve diverse problems, approaches for analyzing the trade-offs offered by these algorithms are scattered across fields. We provide an overview of the current advances on this topic, including methods for visualization, mining the solution set, and uncertainty exploration as well as emerging research directions, including interactivity, explainability, and support on ethical aspects. We synthesize these methods drawing from different fields of research to enable building a unified approach, independent of the application. Our goals are to reduce the entry barrier for researchers and practitioners on using MOO algorithms and to provide novel research directions.