AAAI-22 Tutorial Forum

Thirty-Sixth Conference on Artificial Intelligence
February 23, 2022
Vancouver, BC, Canada

What Is the Tutorial Forum?

The Tutorial Forum provides an opportunity for researchers and practitioners to explore exciting advances in disciplines outside their normal focus. We believe this type of forum is essential for the cross fertilization, cohesiveness, and vitality of the AI field. We all have a lot to learn from each other; the Tutorial Forum promotes the continuing education of each member of AAAI.

Schedule

The following list of tutorials have been accepted for presentation at AAAI-22. Start and end times will be determined at a later date.

Wednesday, February 23, 2022

MORNING QUARTER-DAY TUTORIALS

TIME: 8:30 AM – 10:15 AM

  • MQ1: Explanations in Interactive Machine Learning
    Stefano Teso, Oznur Alkan, Elizabeth Daly and Wolfgang Stammer
  • MQ2: Formal Verification of Deep Neural Networks: Theory and Practice
    Huan Zhang, Kaidi Xu, Shiqi Wang and Cho-Jui Hsieh

TIME: 10:45 AM – 12:30 PM

  • MQ3: Hate Speech: Detection, Mitigation and Beyond
    Punyajoy Saha, Binny Mathew, Mithun Das and Animesh Mukherjee
  • MQ4: Time Series in Healthcare: Challenges and Solutions
    Mihaela van der Schaar and Fergus Imrie

MORNING HALF-DAY TUTORIALS

TIME: 8:30 AM – 12:30 PM

  • MH1: Automated Negotiation: Challenges, Approaches, & Tools
    Yasser Mohammad and Amy Greenwald
  • MH2: Automated Synthesis: Towards the Holy Grail of AI
    Kuldeep S Meel, Supratik Chakraborty, S Akshay, Priyanka Golia and Subhajit Roy
  • MH3: Bayesian Optimization: From Foundations to Advanced Topics
    Jana Doppa, Aryan Deshwal and Syrine Belakaria
  • MH4: Deep Learning on Graphs for Natural Language Processing
    Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li and Bang Liu
  • MH5: Gromov-Wasserstein Learning for Structured Data Modeling
    Hongteng Xu
  • MH6: Neuro-Symbolic Methods for Language and Vision
    Hamid Palangi, Antoine Bosselut and Pradeep Dasigi
  • MH7: Reasoning on Knowledge Graphs: Symbolic or Neural?
    Meng Qu, Zhaocheng Zhu and Jian Tang
  • MH8: Subset Selection in Machine Learning: Theory, Applications, and Hands On
    Rishabh Iyer, Abir De, Ganesh Ramakrishnan and Jeff Bilmes

AFTERNOON QUARTER-DAY TUTORIALS

TIME: 2:00 PM – 3:45 PM

  • AQ1: Adversarial Machine Learning for Good
    Pin-Yu Chen

TIME: 4:15 PM -6:00 PM

  • AQ2: Recent Advances in Multi-Agent Path Finding
    Jiaoyang Li, Daniel Harabor, Sven Koenig and Ariel Felner

AFTERNOON HALF-DAY TUTORIALS

TIME: 2:00 PM – 6:00 PM

  • AH1: AI Planning: Theory and Practice
    Shirin Sohrabi, Michael Katz and Octavian Udrea
  • AH2: Automated Learning from Graph-Structured Data
    Quanming Yao, Huan Zhao and Yongqi Zhang
  • AH3: Causal Inference from Relational Data
    Elena Zheleva and David Arbour
  • AH4: Ethics in Sociotechnical Systems
    Nirav Ajmeri, Pradeep Kumar Murukannaiah and Munindar Singh
  • AH5: Fairness in Clustering
    Brian Brubach, Deeparnab Chakrabarty, John P Dickerson, Seyed Esmaeili, Matthäus Kleindessner, Marina Knittel, Jamie Morgenstern, Samira Samadi, Aravind Srinivasan and Leonidas Tsepenekas
  • AH6: New Trends in Mechanism Design for Considering Participants’ Interactions
    Dengji Zhao
  • AH7: On Explainable AI: From Theory to Motivation, Industrial Applications, XAI Coding & Engineering Practices
    Freddy Lecue, Fosca Giannotti, Riccardo Guidotti and Pasquale Minervini
  • AH8: Pre-Trained Language Representations for Text Mining
    Yu Meng, Jiaxin Huang, Yu Zhang and Jiawei Han

 

MQ1: Explanations in Interactive Machine Learning
Stefano Teso, Oznur Alkan, Elizabeth Daly and Wolfgang Stammer

This tutorial is intended for Artificial Intelligence researchers and practitioners, as well as domain experts interested in human-in-the-loop machine learning, including interactive recommendation and active learning. The participants will gain an understanding of current developments in interactive machine learning from rich human feedback – with an emphasis on white-box interaction and explanation-guided learning – as well as a conceptual map of the variety of methods available and of the relationships between them. The main goal is to inform the audience about the state-of-the-art approaches for explanations for interactive machine learning, open issues and research directions, and how these developments relate to the broader context of machine learning and AI.

Basic knowledge of machine learning at the level of an introductory course is required. An elementary understanding of active learning and recommender systems will be assumed.

Stefano Teso

Stefano Teso

University of Trento, Italy

Stefano is an assistant professor at the University of Trento, Italy. His research spans interactive machine learning, explainable AI, constraint learning, structured prediction, and preference elicitation.

Öznur Alkan

Öznur Alkan

IBM Research, Ireland

Oznur’s research focuses on exploring different collaboration techniques between machine learning systems and the user, designing solutions that can facilitate this collaboration in the areas of structured predictive models and recommender systems.

Elizabeth Daly

Elizabeth Daly

IBM Research, Ireland

Elizabeth is an STSM at IBM Research. Elizabeth’s work focuses on innovative solutions for interactive AI where systems influence users and users influence systems.

Wolfgang Stammer

Wolfgang Stammer

Technical University of Darmstadt

Wolfgang is a Ph.D. candidate at TU Darmstadt. Wolfgang’s work focuses on explainable and interactive machine learning as well as neuro-symbolic models and discrete neural representations for improved reasoning and human-machine interactions.

MQ2: Formal Verification of Deep Neural Networks: Theory and Practice
Huan Zhang, Kaidi Xu, Shiqi Wang and Cho-Jui Hsieh

Neural networks are largely black-boxes and can sometimes behave unpredictably and produce surprisingly wrong results. For example, we can find adversarial examples in many neural network based applications, and it is hard to guarantee the robustness of deep learning under malicious inputs. In this tutorial, we introduce the neural network verification problem, which aims to formally guarantee properties of neural networks such as adversarial robustness, safety, and correctness. Formal verification is crucial when applying neural networks to mission-critical systems such as autonomous driving and aircraft control, where guaranteed worst-case behavior is necessary. Our tutorial introduces the backgrounds and mathematical formulations of the verification problem, and includes an easy-to-understand introduction to the state-of-the-art bound propagation based neural network verification algorithms. Furthermore, we will give a hands-on coding tutorial for user-friendly neural network verification toolboxes, including the auto_LiRPA library and the state-of-the-art alpha-beta-CROWN verifier, allowing participants to easily apply formal verification techniques to their customized neural networks, and we provide complete coding examples for verifying various neural networks including image classification models and sequence models.

Huan Zhang

Huan Zhang

Carnegie Mellon University

Huan Zhang is a postdoctoral researcher at CMU. He received his Ph.D. degree at UCLA in 2020. Huan’s research focuses on the robustness and trustworthiness of artificial intelligence, especially on developing formal verification methods. Huan Zhang was awarded an IBM Ph.D. fellowship during 2018 – 2020 and he led the VNN-COMP 2021 winning team.

Kaidi Xu

Kaidi Xu

Drexel University

Kaidi Xu is an assistant professor in the Department of Computer Science at Drexel University. Kaidi’s primary research interest is the robustness of machine learning, including adversarial attacks and rigorous robustness verification. Kaidi Xu has published in various top international conferences and is also the winner of the 2nd International Verification of Neural Networks Competition.

Shiqi Wang

Shiqi Wang

Columbia University

Shiqi Wang is a PhD candidate at Columbia University. His research interest focuses on security and verification of ML models. He has published over ten research papers in top-tier ML/Security conferences. Also, he is one of the main contributors to open-source tool alpha-beta-CROWN, the winner of the 2nd VNN-Competition 2021 with the highest score.

Cho-Jui Hsieh

Cho-Jui Hsieh

Columbia University

Cho-Jui Hsieh is an assistant professor in UCLA Computer Science Department. He obtained his Ph.D. from the University of Texas at Austin in 2015. His work mainly focuses on improving the efficiency and robustness of machine learning systems and he has contributed to several widely used machine learning packages. He is the recipient of NSF Career Award, Samsung AI Researcher of the Year, and Google Research Scholar Award. His work has been recognized by several paper awards in ICLR, KDD, ICDM, ICPP, SC.

MQ3: Hate Speech: Detection, Mitigation and Beyond
Punyajoy Saha, Binny Mathew, Mithun Das and Animesh Mukherjee

Social media platforms have given the opportunity to the users to share their ideas and opinions instantly. That being said, there are several ill consequences. Among these consequences, hate speech presents a unique challenge as it is deeply engraved into our society and is often linked with offline violence. Social media platforms rely on moderators to identify hate speech and take necessary action, but with a prolific increase in such content over social media many are turning toward the assistance of automated hate speech detection and mitigation systems. This brings several challenges to the NLP community working in this domain. In this tutorial, we present an exposition of hate speech detection and mitigation in three steps. First, we shall describe the current state of research in the hate speech domain, focusing on different detection and mitigation systems that have developed over time. Next, we shall highlight the challenges that these systems might carry like bias and lack of transparency. The final section will concretize the path ahead, providing clear guidelines for the community working on hate speech and related areas. We expect our audience to have a basic understanding of NLP, social networks and familiarity with python language.

Punyajoy Saha

Punyajoy Saha

Indian Institute of Technology, Kharagpur (India)

Punyajoy Saha is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interests lie in the nexus of social computing and natural language processing. He is currently involved in developing mitigation algorithms for hate speech in social media. More details here.

Binny Mathew

Binny Mathew

Indian Institute of Technology, Kharagpur (India)

Binny Mathew is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interest lies in computational social science and natural language processing. He is currently interested in solving issues surrounding hate speech in online social media and providing solutions to counter them. More details here.

Mithun Das

Mithun Das

Indian Institute of Technology, Kharagpur (India)

Mithun Das is a PhD scholar at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interests lie in computational social science and natural language processing. More details here.

Animesh Mukherjee

Animesh Mukherjee

Indian Institute of Technology, Kharagpur (India)

Animesh Mukherjee is an Associate Professor at the Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur (India). His research interests lie in natural language processing, information retrieval and AI and ethics. More details here.

MQ4: Time Series in Healthcare: Challenges and Solutions
Mihaela van der Schaar and Fergus Imrie

Time series datasets such as electronic health records (EHR) and registries represent valuable (but imperfect) sources of information spanning a patient’s entire lifetime of care. While learning from temporal data is an established field and has been covered in a number of prior tutorials, the healthcare domain raises unique problems and challenges that require new methodologies and ways of thinking.

Perhaps the most common application of time series is forecasting. While we will discuss state-of-the-art approaches for disease forecasting, we will also focus on other important problems in time series, such as time-to-event or survival analysis, personalized monitoring, and treatment effects over time. These topics will be introduced in the context of healthcare, but they have broad applicability to other domains beyond medicine.

In addition, we will explore several characteristics that are necessary to make AI and machine learning models as useful as possible in the clinical setting. We will discuss automated machine learning and we will address the challenges of understanding and explaining machine learning models as well as uncertainty estimation, both of which are critical in high-stakes scenarios such as healthcare.

We will aim for minimal required prerequisite knowledge. However, we will assume basic knowledge of standard machine learning methods (e.g. MLPs, RNNs). While our tutorial will include some detailed explanations of machine learning techniques, significant focus will be placed on the problem areas, their unique challenges, and ways of thinking to overcome these.

Mihaela van der Schaar

Mihaela van der Schaar

University of Cambridge

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, a Fellow at The Alan Turing Institute in London, and a Chancellor’s Professor at UCLA.

Mihaela has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).

Fergus Imrie

Fergus Imrie

University of California, Los Angeles (UCLA)

​Fergus Imrie is a Postdoctoral Scholar at the Department of Electrical and Computer Engineering, University of California, Los Angeles (UCLA). His research focus is machine learning for healthcare and medicine. Previously, Fergus completed his DPhil (PhD) at the University of Oxford in the Department of Statistics.

MH1: Automated Negotiation: Challenges, Approaches, & Tools
Yasser Mohammad and Amy Greenwald

This tutorial will provide an overview of the field of automated negotiation, from the seminal game-theoretic work of Nash on bargaining theory and Rubinstein’s analysis of the alternating offers protocol to more recent results and open challenges. The tutorial will also describe various software platforms that are being used to build automated negotiation agents.

Our main goal is that an interested listener with little or no past experience building negotiation software will be able to get started developing negotiation games and automated negotiation strategies. More experienced listeners will benefit from a comprehensive overview of ongoing research in the area as well as a hands-on experience.

Amy Greenwald

Amy Greenwald

Brown University

​Amy Greenwald is Professor of Computer Science at Brown University in Providence, Rhode Island. Her research focuses on game-theoretic and economic interactions among computational agents, applied to autonomous bidding and negotiation. She is also active in promoting diversity in Computer Science, leading multiple K-12 initiatives in the Providence public schools.

Yasser Mohammad

Yasser Mohammad

NEC

Yasser Mohammad is a Principle Researcher at the Global Innovation Unit of NEC CORPORATION, a visiting researcher at AIST and RIKEN, Japan and an Associate Professor at Assiut University, Egypt. His research is currently focusing on automated negotiation and its industrial applications. He is the maintainer of the NegMAS platform.

MH2: Automated Synthesis: Towards the Holy Grail of AI
Kuldeep S. Meel, Supratik Chakraborty, S. Akshay, Priyanka Golia and Subhajit Roy

The seminal work of Eugene Freuder articulated the holy grail of Computer Science as: “the user states the problem and the computer solves it”. While this general goal remains elusive for Artificial Intelligence, significant advances have been made in several sub-areas in the past few decades. One such sub-area is automated synthesis, wherein a machine automatically synthesizes programs (also represented as circuits) that probably meet the end-user’s functional requirements. In this tutorial, we will present approaches that combine recent advances in automated reasoning, knowledge compilation, and machine learning to solve a variety of practical functional synthesis problems. Given the fundamental importance of synthesis in Computer Science, recent developments in automated synthesis have been reported by researchers in formal methods, programming languages and software engineering, besides in core AI. The goal of this tutorial is to introduce the Artificial Intelligence researcher and practitioner to the theory and tools of this emerging area of importance. The tutorial will be organized around four emerging research directions: (i) knowledge compilation-based approaches, (ii) counterexample-guided techniques, (iii) data-driven synthesis and (iv) grammar-based search. It will cover theory and selected tool demonstrations and also include an open problem session.

Kuldeep S. Meel

Kuldeep S. Meel

National University of Singapore

Kuldeep S. Meel holds the NUS Presidential Young Professorship in the School of Computing at the National University of Singapore. He works at the intersection of formal methods and artificial intelligence. He was named AI’s 10 to watch by IEEE Intelligent Systems in 2020.

Web: https://www.comp.nus.edu.sg/~meel/

Supratik Chakraborty

Supratik Chakraborty

Indian Institute of Technology Bombay, India

Supratik Chakraborty is the Bajaj Group Chair Professor of Computer Science and Engineering at I.I.T. Bombay. His research interests include automated symbolic reasoning, counting and sampling, with applications to synthesis, verification and learning. He is a Fellow of the Indian National Academy of Engineering.

Web: https://www.cse.iitb.ac.in/~supratik/

S Akshay

S Akshay

Indian Institute of Technology Bombay, India

S Akshay is an Associate Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay. His research interests span formal methods and AI with a focus on quantitative verification and automated synthesis.

Web: https://www.cse.iitb.ac.in/~akshayss/

 

Priyanka Golia

Priyanka Golia

Indian Institute of Technology Kanpur, India

Priyanka Golia is a fifth-year Ph.D. student at the Indian Institute of Technology Kanpur and the National University of Singapore, advised by Prof. Subhajit Roy and Prof. Kuldeep S. Meel. She is the lead designer of the state-of-the-art functional synthesis engine, Manthan.

Web:https://priyanka-golia.github.io/

Subhajit Roy

Subhajit Roy

Indian Institute of Technology Kanpur, India

Subhajit Roy is an Associate Professor at the Indian Institute of Technology Kanpur, India. His research is at the intersection of Programming Languages, Formal Methods, Artificial Intelligence and Software Engineering. Currently he is investigating algorithms on automated verification, synthesis, repair, debugging and testing of computer programs.

Web: https://www.cse.iitk.ac.in/users/subhajit/

MH3: Bayesian Optimization: From Foundations to Advanced Topics
Jana Doppa, Aryan Deshwal and Syrine Belakaria

Many engineering and scientific applications including automated machine learning (e.g., neural architecture search and hyper-parameter tuning) involve making design choices to optimize one or more expensive to evaluate objectives. Some examples include tuning the knobs of a compiler to optimize performance and efficiency of a set of software programs; designing new materials to optimize strength, elasticity, and durability; and designing hardware to optimize performance, power, and area. Bayesian Optimization (BO) is an effective framework to solve black-box optimization problems with expensive function evaluations. The key idea behind BO is to build a cheap surrogate statistical model (e.g., Gaussian Process) using the real experimental data; and employ it to intelligently select the sequence of experiments or function evaluations using an acquisition function, e.g., expected improvement (EI) and upper-confidence bound (UCB).

There is a large body of work on BO for single-objective optimization in the single-fidelity setting (i.e., experiments are expensive and accurate in function evaluation) for continuous input spaces. However, BO work in recent years has focused on more challenging problem settings including optimization of multiple objectives; optimization with multi-fidelity function evaluations (vary in resource cost and accuracy of evaluation); optimization with black-box constraints with applications to safety; optimization of combinatorial spaces (e.g., sequences, trees, and graphs); and optimization of hybrid spaces (mixture of discrete and continuous input variables. The goal of this tutorial is to present a comprehensive survey of BO starting from foundations to these recent advances by focusing on challenges, principles, algorithmic ideas and their connections, and important real-world applications.

Aryan Deshwal

Aryan Deshwal

Washington State University

​Aryan Deshwal is a senior PhD student in Computer Science at Washington State University. His research focuses on probabilistic modeling and optimization to support decision-making under uncertainty in structured domains. He won the Outstanding TA award from College of Engineering (2020), and three Outstanding Reviewer Awards from ICML and ICLR.

Syrine Belakaria

Syrine Belakaria

Washington State University

Syrine Belakaria is a senior PhD student in Computer Science at Washington State University. Her research focuses on adaptive experiment design for science and engineering applications. She has been awarded the IBM PhD Fellowship (2021-2023) and was selected as a Rising Star in EECS by MIT in 2021.

Jana Doppa

Jana Doppa

Washington State University

Jana Doppa is a George and Joan Berry Distinguished Associate Professor in Computer Science at Washington State University. His current research focuses on AI to accelerate science and engineering. He won an NSF CAREER Award and was selected for a Early Career Spotlight by IJCAI Conference (2021)

MH4: Deep Learning on Graphs for Natural Language Processing
Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li and Bang Liu

Recently, there has been a surge of interest in applying deep learning on graphs techniques (i.e., Graph Neural Networks (GNNs)) to NLP, and has achieved considerable success in many NLP tasks. Despite these successes, deep learning on graphs for NLP still face many challenges, including automatically transforming textual data into highly graph-structured data, and effectively modeling complex data that involves mapping between graph-based inputs and other highly structured output data such as sequences, trees, and graph data with multi-types in both nodes and edges.

This tutorial will cover relevant and interesting topics on applying deep learning on graphs techniques to NLP, including automatic graph construction for NLP, graph representation learning for NLP, GNN-based encoder-decoder models for NLP, and the applications of GNNs in various NLP tasks (e.g., information extraction, machine translation and question answering). In addition, a hands-on demo session will be included to help the audience gain practical experience on applying GNNs to solve challenging NLP problems using our recently developed open source library – Graph4NLP, the first library for researchers and practitioners for easy use of GNNs for various NLP tasks.

Lingfei Wu

Lingfei Wu

JD.COM

Lingfei Wu is the Principal Scientist at JD.COM Silicon Valley Research Center, leading a team of 30+ machine learning/natural language processing/recommendation system scientists and software engineers to build next generation intelligent ecommerce systems for personalized and interactive online shopping experience in JD.COM. He has given more than 10+ Tutorials/Keynote Speeches on deep learning on graphs and natural language processing.

Yu Chen

Yu Chen

Facebook AI

Yu Chen is a Research Scientist at Facebook AI. He got his PhD degree in Computer Science from RPI. His research interests lie at the intersection of Deep Learning and NLP. His work has been published at many top-ranked conferences. He won the Best Student Paper Award of AAAI DLGMA’20.

Bang Liu

Bang Liu

University of Montreal

Bang Liu is an Assistant Professor at the University of Montreal and Mila. His research focuses on NLP and text mining. He has published 25+ papers in top-tier academic venues and his research works have been deployed in multiple mobile applications that involve more than a billion daily active users.

Yunyao Li

Yunyao Li

IBM Research

Yunyao Li is a Distinguished Research Staff Member and Senior Research Manager at IBM Research – Almaden. She has published over peer-reviewed 80 articles in top research venues and a book in the areas of NLP, Databases, and HCI. She has delivered core technologies to over 20 IBM products.

Heng Ji

Heng Ji

University of Illinois, Urbana-Champaign

Heng Ji is a professor at the Computer Science Department of University of Illinois Urbana-Champaign, and an Amazon Scholar. The awards she received include “Young Scientist” by the World Economic Forum, “AI’s 10 to Watch” Award, NSF CAREER award, ACL2020 Best Demo Paper Award, and NAACL2021 Best Demo Paper Award.

MH5: Gromov-Wasserstein Learning for Structured Data Modeling
Hongteng Xu

Many real-world data like networks, 3D meshes, and molecules are structured data in different scales, which are represented as graphs with optional node and/or edge attributes. From the viewpoint of machine learning, the tasks focusing on these structured data, such as network alignment, molecule analysis, and so on, can often be formulated as graph representation, matching, partitioning, and clustering problems. Unfortunately, due to their NP-completeness, we have to rely on heuristic methods to solve these problems in practice, without theoretical support on stability and rationality. This tutorial will introduce recent advances on machine learning-based structured data modeling, especially the Gromov-Wasserstein learning (GWL) paradigm based on the theory of Gromov-Wasserstein (GW) distance (a widely-used optimal transport distance for structured data like graphs). In particular, this tutorial consists of the following three parts: (i) The theoretical fundamentals of GWL, including the properties of GW distance and its connections to other optimal transport distance; (ii) the computational methods of GW distance and its variants, the GW-based machine learning models and algorithms; (iii) the innovative downstream applications of GWL and the challenges of optimal transport and structured data modeling. The content above relates to several areas within the AAAI community, such as machine learning and its applications, nonconvex optimization, stochastic algorithms, and graph modeling and analysis.

Hongteng Xu

Hongteng Xu

Renmin University of China (RUC)

Dr. Hongteng Xu is an Associate Professor, with the Gaoling school of artificial intelligence, Renmin University of China (RUC). Before joining RUC, Hongteng earned his Ph.D. in ECE from Georgia Tech and worked as a senior research scientist with Infinia ML Inc. and a visiting researcher with Duke University from 2018 to 2020. Currently, Hongteng’s research interests include computational optimal transport and its applications to machine learning.

MH6: Neuro-Symbolic Methods for Language and Vision
Hamid Palangi, Antoine Bosselut and Pradeep Dasigi

Neuro-symbolic methods for representing knowledge combine compositionality and interpretability of symbolic methods with the ease of training of neural networks. These methods have been successfully applied in various language and vision tasks for learning useful intermediate representations that facilitate complex reasoning and for incorporating semi-structured external knowledge into task-specific models. We will provide a comprehensive overview of these methods, while drawing similarities between the learning algorithms used for various tasks wherever appropriate. From this tutorial, the audience can expect to get a good understanding of how neuro-symbolic methods work, their applicability to various language and vision tasks, and the open challenges that remain. This tutorial will help NLP or ML practitioners unfamiliar with neuro-symbolic methods learn the foundations in this growing area of research.

Antoine Bosselut

Antoine Bosselut

EPFL

Antoine Bosselut is an assistant professor at the École Polytechnique Fédéral de Lausanne (EPFL). Previously, he was a postdoctoral scholar at Stanford University and a Young Investigator at the Allen Institute for AI (AI2). He completed his PhD at the University of Washington.

Pradeep Dasigi

Pradeep Dasigi

Allen Institute for AI

Pradeep Dasigi is a Research Scientist at the Allen Institute for AI. He works on Natural Language Processing, and is particularly interested in Question Answering and Semantic Parsing. He has a PhD from Carnegie Mellon University and a Masters in Computer Science from Columbia University.

Hamid Palangi

Hamid Palangi

Microsoft Research Lab in Redmond, Washington

Hamid Palangi is a Senior Researcher at Microsoft Research Lab in Redmond, Washington. His current research interests are in the areas of Natural Language Processing and Language Grounding with a focus on Robustness. He is a Senior member of IEEE and has a PhD from the University of British Columbia.

MH7: Reasoning on Knowledge Graphs: Symbolic or Neural?
Meng Qu, Zhaocheng Zhu and Jian Tang

Knowledge graphs encode real-world facts and are critical in a variety of applications and domains such as natural language understanding, recommender systems, drug discovery, and image understanding. A fundamental problem on knowledge graphs is to predict missing facts by reasoning with existing facts, a.k.a. knowledge graph reasoning. This problem has been extensively studied in different communities of AI including general AI community, machine learning community, data mining community, and NLP community, which either focus on development of fundamental methodology or solutions to important real-world problems. Therefore, a systematic introduction to knowledge graph reasoning summarizing the progress across different communities would benefit a broad audience. In this tutorial, we plan to give a comprehensive introduction to different methods of knowledge graph reasoning, including traditional symbolic logic rule-based methods, neural-based methods, neural-symbolic methods, and different applications. This tutorial will benefit both junior and senior researchers and researchers interested in both methodology development and applications.

This tutorial covers techniques for knowledge graph reasoning in a self-contained manner. We only assume the audience is familiar with empirical risk minimization, back propagation and stochastic gradient descent, as well as basic models like embeddings (e.g. word2vec or TransE), LSTMs and GNNs.

Meng Qu

Meng Qu

Mila - Quebec AI Institute

Meng Qu is a Ph.D. candidate at Mila – Quebec AI Institute, supervised by Prof. Jian Tang. He is interested in reasoning on graph-structured data such as knowledge graphs. He has published several papers on combining deep learning and statistical relational learning for knowledge reasoning, including GMNN, pLogicNet, and RNNLogic.

Zhaocheng Zhu

Zhaocheng Zhu

Mila - Quebec AI Institute

Zhaocheng Zhu is a Ph.D. candidate at Mila – Quebec AI Institute, supervised by Prof. Jian Tang. He is interested in algorithms and systems for knowledge graph reasoning. He has published 3 papers on knowledge graphs. He led the development of 2 open-source projects, including GraphVite and TorchDrug.

Jian Tang

Jian Tang

Mila - Quebec AI Institute

Jian Tang is an assistant professor at Mila – Quebec AI Institute, starting from 2017. He is interested in graph representation learning, graph neural networks, drug discovery and knowledge graphs. He was named the first cohort of Canada CIFAR Artificial Intelligence Chairs. He has published many papers on knowledge graphs.

MH8: Subset Selection in Machine Learning: Theory, Applications, and Hands On
Rishabh Iyer, Abir De, Ganesh Ramakrishnan and Jeff Bilmes

The goal of this tutorial is to provide a gentle introduction to ideas in combinatorial optimization and submodularity to the broader machine learning and deep learning researchers, and ground this in applications. Specifically, we believe that the applications presented in this tutorial in areas such as label efficient, compute efficient, robust, fair and personalized learning will enable researchers to think beyond just improving the model accuracy and in broader yet important aspects like Green AI, fairness, robustness, personalization, data efficiency and so on. Furthermore, the hands-on demonstrations will also be useful to students and researchers from industry to get oriented in and practically stated with these topics. Another goal of this tutorial is to connect researchers working on theoretical and algorithmic areas to the numerous applications where their work can have impact, and vice versa. The target audience of this tutorial are practitioners in deep learning and machine learning as well as researchers working on more theoretical areas in optimization in machine learning.

Rishabh Iyer

Rishabh Iyer

University of Texas at Dallas

Rishabh Iyer is currently an Assistant Professor at University of Texas at Dallas where he heads the Machine Learning and Optimization Lab. Prior to this, he was a Research Scientist at Microsoft where he spent three years. His work has received best paper awards at ICML 2013 and NIPS (now NeurIPS), 2013.

Abir De

Abir De

Indian Institute of Technology, Bombay, India

Abir is an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Technology, Bombay, India. Prior to this, he was a postdoctoral researcher at the Max Planck Institute for Software Systems. His main research interests broadly lie in the area of machine learning and its applications on human assisted learning and on networks.

Ganesh Ramakrishnan

Ganesh Ramakrishnan

Indian Institute of Technology, Bombay, India

Ganesh Ramakrishnan is currently serving as a Professor at the Department of Computer Science and Engineering, IIT Bombay and is Professor-in-charge of the Koita Centre for Digital Health, IIT Bombay. He completed his PhD also at the department of CSE, IIT Bombay, worked at IBM India Research labs and thereafter joined back IIT Bombay as a faculty member of the Dept of CSE in 2009.

Jeff Bilmes

Jeff Bilmes

University of Washington

Jeff Bilmes is a professor at the ECE department at the University of Washington, Seattle, where he leads the MELODI lab. Prof. Bilmes is a 2001 NSF Career award winner, a 2002 CRA Digital Government Fellow, a 2008 NAE Gilbreth Lectureship award recipient, and a 2012/2013 ISCA Distinguished Lecturer.

AQ1: Adversarial Machine Learning for Good
Pin-Yu Chen

Unlike conventional tutorials on adversarial machine learning (AdvML) that focus on adversarial attacks, defenses, or verification methods, this tutorial aims to provide a fresh overview of how the same technique can be used in totally different manners to benefit mainstream machine learning tasks and to facilitate sustainable growth in this research field. This tutorial will start by reviewing the recent advances in AdvML and then delve into novel innovations to other domains (beyond adversarial robustness) inspired from AdvML. In particular, we will cover several noteworthy innovations proposed in recent years and relate their success to AdvML, including (i) generation of contrastive explanations and counterfactual examples; (ii) model reprogramming for data-efficient transfer learning; (iii) model watermarking and fingerprinting for AI governance and ownership regulation; (iv) data cloaking for enhanced privacy Second; and (v) data augmentation for improving model generalization. Finally, this tutorial will discuss the sustainability of this research field towards continuous and organic growth, in terms of research norms and ethics, current trends, open challenges, and future directions.

Pin-Yu Chen

Pin-Yu Chen

IBM Research

Dr. Pin-Yu Chen is a Research Staff Member at IBM Research. He is also the Chief Scientist of RPI-IBM AI Research Collaboration program and PI of MIT-IBM Watson AI Lab. His recent research focus has been on adversarial machine learning and robustness of neural networks, and more broadly, making machine learning trustworthy. More details can be found at www.pinyuchen.com.

AQ2: Recent Advances in Multi-Agent Path Finding
Jiaoyang Li, Daniel Harabor, Sven Koenig and Ariel Felner

Navigating a team of agents through a shared environment is an important problem for many existing and emerging application areas. Examples include warehouse logistics, mail sortation, autonomous intersection management, and the coordination of drone swarms. In each of these settings, practitioners must tackle a challenging combinatorial problem known as Multi-Agent Path Finding (MAPF). Studies on this topic appear often in the literature of Artificial Intelligence and in the proceedings of flagship conferences, such as AAAI. These works are also of interest to researchers in adjacent areas, such as Robotics and Discrete Optimisation.

In this tutorial, we propose to overview the core MAPF problem and to summarise recent progress in this fast-moving research area. Our objective is to give a holistic perspective that covers theoretical foundations as well as practical algorithms: for planning, execution, and handling a variety of operational issues that commonly arise in practice. Our target audience is anyone interested in planning for and coordination of multiple agents. The tutorial will particularly benefit people who are interested in MAPF and its many applications.

Jiaoyang Li

Jiaoyang Li

University of Southern California

Jiaoyang Li is a Ph.D. student in computer science at the University of Southern California. She received a Bachelor’s degree in automation from Tsinghua University in 2017. Her research focuses on developing efficient planning algorithms for large-scale multi-agent coordination problems. See more information about her at https://jiaoyangli.me/.

Daniel Harabor

Daniel Harabor

Monash University

Daniel Harabor is a Senior Lecturer at Monash University. His research interests include single and multi-agent pathfinding, with applications for games and robotics. For more information please visit https://harabor.net/daniel

 Sven Koenig

Sven Koenig

University of Southern California

​Sven Koenig is a professor of computer science at the University of Southern California. Most of his research centers around planning techniques for single agents (such as robots) and multi-agent systems. Additional information on him can be found at idm-lab.org.

Ariel Felner

Ariel Felner

Ben-Gurion University, Israel

Ariel Felner is a professor of computer science at Ben-Gurion University, Israel. He is interested in all aspects of Heuristic Search: Algorithms, Heuristics and Applications. Multi-agent pathfinding has been at the core of his research for more than a decade. Additional information on him can be found at https://felner.wixsite.com/home.

AH1: AI Planning: Theory and Practice
Shirin Sohrabi, Michael Katz and Octavian Udrea

AI Planning is a long-standing sub-area of AI, dealing with sequential decision making, a sister field to Reinforcement Learning. Mature industrial applications of planning technology can be seen in various fields, such as dialog systems, cybersecurity, transportation and logistics, as well as IT. While model-based planning tools allow to solve problems of practical size, applying AI planning research in practice faces several challenges, preventing its widespread use. The alternative of using model-free methods, however, often proves infeasible for real-life size problems. The aim of this tutorial is to provide the audience with the necessary theoretical background knowledge, as well as hands-on experience to allow for using planning tools for solving everyday challenges.

In this tutorial, we will provide an overview of the field of planning, including recent advances in the field. We will then dive into three challenges:

(1) modeling: how to represent, extract, and learn the knowledge;

(2) theory: formal definition of the computational problems;

(3) tooling: how to solve computational problems.

We will have a hands-on session to exemplify the use of planning tools for solving an example application. Our aim is to give AAAI attendees the necessary means for using AI planning tools in their applications.

Michael Katz

Michael Katz

IBM T.J. Watson Research Center

Michael is a research staff member at IBM T.J. Watson Research Center, with interests in automated planning and autonomous systems, integration of planning and RL. Michael is an ICAPS executive council member. He received numerous awards, was ICAPS2021 program co-chair and organizer of PRL and HSDIP workshops.

Octavian Udrea

Octavian Udrea

Dataminr

Octavian Udrea is a Principal Research Scientist at Dataminr, where his research focuses on knowledge representation and reasoning. Before that, he was a Research Scientist at IBM Research AI, where he designed and built the award winning IBM Scenario Planning Advisor, as well as multiple systems to automate the creation of data science and ML pipelines.

Shirin Sohrabi

Shirin Sohrabi

IBM Research

Shirin Sohrabi is a research staff member and research manager at IBM Research. Her research interests are in the area of Artificial Intelligence with a focus on AI planning and its applications. She is an ACM senior member and a member of ICAPS executive council.

AH2: Automated Learning from Graph-Structured Data
Quanming Yao, Huan Zhao and Yongqi Zhang

Graph-structured data (GSD) is ubiquitous in real-life applications, which appears in many learning applications such as property prediction for molecular graphs, product recommendations from heterogeneous information networks, and logical queries from knowledge graphs. Recently, learning from graph-structured data has also become a research focus in the machine learning community. However, again due to such diversities in GSD, there are no universal learning models that can perform well and consistently across different learning applications based on graphs. In sharp contrast to this, convolutional neural networks work well on natural images, and transformers are good choices for text data. In this tutorial, we will talk about using automated machine learning (AutoML) as a tool to design learning models for GSD. Specifically, we will elaborate on what is AutoML, what kind of prior information from graphs can be explored by AutoML, and how insights can be generated from the searched models.

Quanming Yao

Quanming Yao

Tsinghua University

Dr. Quanming Yao is a tenure-track assistant professor at EE, Tsinghua University. He obtained his Ph.D. degree at the CSE of HKUST and was the founding leader of 4Paradigm INC’s machine learning research team. He is a recipient of Forbes-30-Under-30 (China), Young Scientist Awards (HKIS), and Google Fellowship (machine learning).

Huan Zhao

Huan Zhao

4Paradigm

Dr. Huan Zhao is a senior researcher in 4Paradigm, leading the research on automated graph representation learning (AutoGraph) and real-world applications like retailing recommendation and bioinformatics. He has published various papers on top-tier venues like KDD, CIKM, AAAI, and TKDE. He obtained his Doctor Degree from HKUST in Jan. 2019.

Yongqi Zhang

Yongqi Zhang

4Paradigm

Dr. Yongqi Zhang is a senior researcher in 4Paradigm. He obtained the Ph.D. degree at CSE of HKUST. He has published many top-tier conference/journal papers as first-author in NeurIPS, ICDE, VLDB-J. His research interests focus on KG embedding and AutoML. He has been a reviewer for AAAI, IJCAI, CIKM and TKDE.

AH3: Causal Inference from Relational Data
Elena Zheleva and David Arbour

This tutorial will present state-of-the-art research in causal inference from relational data, also known as causal inference with interference. The tutorial will start by motivating research in this area with real-world applications, such as measuring influence in social networks and market experimentation. We will discuss the challenges of applying existing causal inference techniques designed for i.i.d. data to relational data, some of the solutions that currently exist and the gaps and opportunities for future research. Since randomized controlled trials are considered the gold standard in causal inference, we will present existing network experiment designs for measuring different possible effects of interest in networks. Then we will focus on causal inference from observational data, its representation, identification, and estimation. Finally, we will present how causal structure learning can help with learning causal models and causal discovery in networks. The target audience is relational learning and graph mining researchers looking to understand the research landscape in causal inference and causal inference researchers looking to expand their knowledge in network settings. Prior knowledge in causal inference is helpful but not required for the tutorial.
We aim to make these findings accessible to a general audience, including not only researchers who want to further engage with this area, but also practitioners who stand to benefit from the results, and policymakers who want to deepen their technical understanding of these important issues. Audience members should be familiar with supervised learning and have a working knowledge of gradient-based optimization techniques. Familiarity with Python and at least one deep learning framework is recommended but not required.

Elena Zheleva

Elena Zheleva

University of Illinois at Chicago

Elena Zheleva is an assistant professor of Computer Science at the University of Illinois at Chicago. Her research spans machine learning, causal inference and graph mining, with applications in computational social science and privacy. She is the recipient of a NSF CAREER and an Adobe Data Science Research Award.

David Arbour

David Arbour

Adobe Research

David Arbour is a research scientist at Adobe Research where he primarily works on problems in causal inference and discovery with a particular focus on solutions employing machine learning and settings involving dependent data. Prior to Adobe, he worked in the core data science team at Facebook.

AH4: Ethics in Sociotechnical Systems
Nirav Ajmeri, Pradeep Kumar Murukannaiah and Munindar Singh

The surprising capabilities of AI overlaid on detailed data and fine-grained control give cause for concern that agents can wield enormous power over human welfare, drawing increasing attention to ethics in AI. Ethics is inherently a multiagent concern—an amalgam of (1) one party’s concern for another and (2) a notion of justice. To capture the multiagent conception, this tutorial introduces ethics as a sociotechnical construct. Specifically, we demonstrate how ethics can be modeled and analyzed, and requirements on ethics (value preferences) can be elicited, in a sociotechnical system (STS).

The tutorial is designed for a broad audience, with an undergraduate-level understanding of AI. The topics are self-contained—foundations are introduced before diving into research challenges and emerging topics. A familiarity with multiagent systems is useful but not assumed. The participants familiar with MAS will benefit by learning research challenges and emerging topics. Those not familiar with MAS will gain a new perspective on engineering AI systems and why the MAS perspective facilitates ethics.

Nirav Ajmeri

Nirav Ajmeri

University of Bristol

Nirav Ajmeri is a Lecturer in Artificial Intelligence at the University of Bristol in the United Kingdom. His research interests are in intelligent agents and multiagent systems with an emphasis on ethics, privacy, and security. Contact Nirav at nirav.ajmeri@bristol.ac.uk.

Pradeep Murukanniah

Pradeep Murukanniah

Delft University of Technology

Pradeep Murukanniah is an Assistant Professor in the Interactive Intelligence group at the Delft University of Technology in the Netherlands. Engineering socially intelligent agents is the overarching theme of Pradeep’s research. Contact Pradeep at p.k.murukannaiah@tudelft.nl.

Munindar P. Singh

Munindar P. Singh

North Carolina State University

Munindar P. Singh is a Professor in Computer Science and a co-director of the Science of Security Lablet at North Carolina State University. His research interests include multiagent systems and software engineering with a special emphasis on the engineering of systems consisting of autonomous parties. Contact Munindar at singh@ncsu.edu.

AH5: Fairness in Clustering
Brian Brubach, Deeparnab Chakrabarty, John P. Dickerson, Seyed Esmaeili, Matthäus Kleindessner, Marina Knittel, Jamie Morgenstern, Samira Samadi, Aravind Srinivasan and Leonidas Tsepenekas

The goal of this tutorial is to introduce a wide audience interested in algorithmic fairness to the nascent research area of fair clustering. Specifically, we wish to present a variety of fairness notions used in the context of clustering, argue about the necessity of each of those through corresponding applications, discuss the relationships between different notions, sketch the algorithmic ideas that were developed in order to address the corresponding computational problems, and finally share our thoughts about the future of research in algorithmic fairness. By the end of the tutorial, the audience will have achieved a significant level of familiarity with multiple definitions of fairness in unsupervised learning, and we hope that researchers will use these ideas in contexts both within and adjacent to the clustering context, in both industrial and academic applications.

We will cater our tutorial to the modal junior participant at AAAI. That is, we will assume the audience has a basic CS and AI/ML background, but not necessarily deep clustering experience. Any participant who has had an undergraduate- or early graduate-level Algorithms/Machine Learning course will be able to follow the entirety of the tutorial.

Brian Brubach

Brian Brubach

Wellesley College

Brian Brubach is an Assistant Professor of Computer Science at Wellesley College and an Affiliate of the Institute for Mathematics and Democracy. His research focuses on algorithms and theoretical computer science with broad applications in areas such as e-commerce, algorithmic fairness, and electoral systems.

Deeparnab Chakrabarty

Deeparnab Chakrabarty

Dartmouth

Deeparnab Chakrabarty is an associate professor of computer science at Dartmouth. His research interests involve the interplay of optimization and algorithms, with applications in data science, machine learning, and algorithmic economics.

John P Dickerson

John P Dickerson

Computer Science at Maryland

John P Dickerson is Assistant Professor of Computer Science at Maryland, as well as Chief Scientist of Arthur AI. He is recipient of the NSF CAREER Award, IEEE Intelligent Systems AI’s 10 to Watch, and a Google Faculty Research Award, among others. His research centers on solving economic problems using techniques from stochastic optimization and machine learning.

Seyed Esmaeili

Seyed Esmaeili

University of Maryland, College Park

Seyed Esmaeili is a 5th year PhD student at the University of Maryland, College Park. His research interests are in machine learning, algorithms, and fairness. More specifically, his work focuses on producing algorithms with theoretical guarantees that address fairness issues in various topics such as clustering, matching, and redistricting.

Matthäus Kleindessner

Matthäus Kleindessner

Amazon AWS in Tübingen, Germany

Matthäus Kleindessner is a Research Scientist at Amazon AWS in Tübingen, Germany, working on algorithmic fairness. He has a PhD from the University of Tübingen and did postdocs at Rutgers University and the University of Washington.

Marina Knittel

Marina Knittel

University of Maryland

Marina Knittel is a fourth year PhD student at the University of Maryland studying theoretical computer science and artificial intelligence. Her research focuses on fairness and scalability for algorithms on massive networks, including adaptive and non-adaptive massively parallel computation (AMPC and MPC), fairness in graph partitioning, and mechanism design.

Jamie Morgenstern

Jamie Morgenstern

University of Washington

Jamie Morgenstern is an assistant professor at the University of Washington. She studies the social impact of machine learning and the impact of social behavior on ML’s guarantees. How should machine learning be made robust to the behavior of the people generating training or test data for it? How should we ensure that the models we design do not exacerbate inequalities already present in society?

Samira Samadi

Samira Samadi

Max Planck Institute for Intelligent Systems in Tübingen, Germany

Samira Samadi is a research group leader at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. She studies the interactions between humans and ML and uses her findings to design ML systems that augment humans’ abilities. She got her Ph.D. from the School of Computer Science at Georgia Tech.

Aravind Srinivasan

Aravind Srinivasan

University of Maryland, College Park

Aravind Srinivasan is a Distinguished University Professor and Professor of Computer Science at the University of Maryland, College Park. His research interests include algorithms, probabilistic methods, data science, network science, and machine learning: theory, and applications in areas including health, E-commerce, cloud computing, Internet advertising, and fairness.

Leonidas Tsepenekas

Leonidas Tsepenekas

University of Maryland, College Park

Leonidas Tsepenekas is a 5th year PhD student at the University of Maryland, College Park. Leonidas’ research interests revolve around Algorithmic Fairness and Stochastic Models for Combinatorial Optimization. Specifically, he is interested in identifying meaningful notions of fairness, translating them into rigorous mathematical objects, and incorporating them in classical algorithmic problems.

AH6: New Trends in Mechanism Design for Considering Participants’ Interactions
Dengji Zhao

​This tutorial introduces a novel mechanism design framework by specifically considering the connections/interactions between participants. Traditionally, the settings of mechanism design mostly assumed that the participants are independent, which is not the case nowadays as people are well-connected via the Internet especially the social networks. This gives us a new dimension for the design. One important usage of their connections is to incentivize existing participants to invite more participants to join the game via their connections. This is significant in both theory and practice. In resource allocation (auctions), a larger market will discover more participants’ valuations/demand and increase social welfare or the seller’s revenue. In task allocation (coalitional games), a larger group of participants creates larger coalitions (better outcomes/utilities). In matching, a larger group of participants makes more satisfactory matchings. However, in all these examples, participants are competitors and they have no incentives to invite each other. We discuss how to utilize the conflict of their interests to design the incentives. This is a new trend in mechanism design. We will highlight the early solutions and open the floor for discussing the fundamental open questions in the settings of auctions, coalitional games, and matching.

Dengji Zhao

Dengji Zhao

ShanghaiTech University, China

Dengji Zhao is a Tenure-track Assistant Professor at ShanghaiTech University, China. He received double Ph.D. degrees in Computer Science from Western Sydney University and the University of Toulouse in 2012. Before joining ShanghaiTech, he worked as a postdoc at Kyushu University, Japan, and the University of Southampton, UK. Most of Zhao’s research is on algorithmic game theory, especially mechanism design and its applications in the sharing economy. His recent research has stimulated the study of mechanism design by considering the interactions between participants.

AH7: On Explainable AI: From Theory to Motivation, Industrial Applications, XAI Coding & Engineering Practices
Freddy Lecue, Pasquale Minervini, Fosca Giannotti and Riccardo Guidotti

The future of AI lies in enabling people to collaborate with machines to solve complex problems. Like any efficient collaboration, this requires good communication, trust, clarity and understanding. XAI (eXplainable AI) aims at addressing such challenges by combining the best of symbolic AI and traditional Machine Learning. Such a topic has been studied for years by all different communities of AI, with different definitions, evaluation metrics, motivations and results.

This tutorial is a snapshot on the work of XAI to date, and surveys the work achieved by the AI community with a focus on machine learning and symbolic AI related approaches (given the half day format). We will motivate the needs of XAI in real-world and large-scale application, while presenting state-of-the-art techniques, with best XAI coding practices. In the first part of the tutorial, we give an introduction to the different aspects of explanations in AI. We then focus the tutorial on two specific approaches: (i) XAI using machine learning and (ii) XAI using a combination of graph-based knowledge representation and machine learning. For both we get into the specifics of the approach, the state of the art and the research challenges for the next steps. The final part of the tutorial gives an overview of real-world applications of XAI as well as best XAI coding practices.

Freddy Lecue

Freddy Lecue

Accenture Technology Labs, Dublin

Freddy Lecue (PhD 2008, Habilitation 2015) is Chief AI scientist at Thales, Montreal, Canada. He was a principal scientist and research manager in Artificial Intelligent systems, systems combining learning and reasoning capabilities, in Accenture Technology Labs, Dublin – Ireland. He is also a research associate at INRIA, in WIMMICS, Sophia Antipolis – France. Before joining Accenture Labs, he was a Research Scientist at IBM Research, Smarter Cities Technology Center (SCTC) in Dublin, Ireland, and lead investigator of the Knowledge Representation and Reasoning group. His main research interests are Explainable AI systems. The application domain of his current research is Smarter Cities, with a focus on Smart Transportation and Building. In particular, he is interested in exploiting and advancing Knowledge Representation and Reasoning methods for representing and inferring actionable insight from large, noisy, heterogeneous and big data. He has over 40 publications in refereed journals and conferences related to Artificial Intelligence (AAAI, ECAI, IJCAI, IUI) and Semantic Web (ESWC, ISWC), all describing new systems to handle expressive semantic representation and reasoning. He co-organized the first three workshops on semantic cities (AAAI 2012, 2014, 2015, IJCAI 2013), and the first two tutorials on smart cities at AAAI 2015 and IJCAI 2016. Prior to joining IBM, Freddy Lecue was a Research Fellow (2008-2011) with the Centre for Service Research at The University of Manchester, UK. He has been awarded a second prize for his Ph.D. thesis by the French Association for the Advancement of Artificial Intelligence in 2009, and has been recipient of the Best Research Paper Award at the ACM/IEEE Web Intelligence conference in 2008.

Pasquale Minervini

Pasquale Minervini

University College London

Pasquale is a Senior Research Fellow at University College London (UCL). He received a PhD in Computer Science from University of Bari, Italy, with a thesis on relational learning. After his PhD, he worked as a postdoc researcher at the University of Bari, and at the INSIGHT Centre for Data Analytics (INSIGHT), where he worked in a group composed by researchers and engineers from INSIGHT and Fujitsu Ireland Research and Innovation. Pasquale published peer-reviewed papers in top-tier AI conferences, receiving two best paper awards, participated to the organisation of tutorials on Explainable AI and relational learning (three for AAAI, one for ECML, and others), and was a guest lecturer at UCL and at the Summer School on Statistical Relational Artificial Intelligence. He is the main inventor of a patent application assigned to Fujitsu Ltd, and recently he was awarded a seven-figures H2020 research grant involving applications of relational learning to cancer research. His website is neuralnoise.com.

Fosca Giannotti

Fosca Giannotti

National Research Council, Pisa, Italy

Fosca Giannotti is Director of Research at the Information Science and Technology Institute “A. Faedo” of the National Research Council, Pisa, Italy. Fosca is a scientist in Data mining and Machine Learning and Big Data Analytics. She leads the Pisa KDD Lab – Knowledge Discovery and Data Mining Laboratory http://kdd.isti.cnr.it, a joint research initiative of the University of Pisa and ISTI-CNR, founded in 1994 as one of the earliest research labs centered on data mining. Fosca’s research focus is on social mining from big data: human dynamics, social networks, diffusion of innovation, privacy enhancing technology and explainable AI. She has coordinated tens of research projects and industrial collaborations. Fosca is now the coordinator of SoBigData, the European research infrastructure on Big Data Analytics and Social Mining, an ecosystem of ten cutting edge European research centers providing an open platform for interdisciplinary data science and data-driven innovation http://www.sobigdata.eu. In 2012-2015 Fosca has been general chair of the Steering board of ECML-PKDD (European conference on Machine Learning) and is currently member of the steering committee EuADS (European Association on Data Science) and of the AIIS: Italian Lab. of Artificial Intelligence and Autonomous Systems

Riccardo Guidotti

Riccardo Guidotti

University of Pisa, Italy

Riccardo Guidotti is currently a post-doc researcher at the Department of Computer Science, University of Pisa, Italy and a member of the Knowledge Discovery and Data Mining Laboratory (KDDLab), a joint research group with the Information Science and Technology Institute of the National Research Council in Pisa. Riccardo Guidotti was born in 1988 in Pitigliano (GR) Italy. In 2013 and 2010 he graduated cum laude in Computer Science (MS and BS) at University of Pisa. He received his PhD in Computer Science with a thesis on Personal Data Analytics in the same institution. He won the IBM fellowship program and was an intern at IBM Research Dublin, Ireland in 2015. His research interests are in personal data mining, clustering, explainable models, analysis of transactional data related to recipes and to migration flows.

AH8: Pre-Trained Language Representations for Text Mining
Yu Meng, Jiaxin Huang, Yu Zhang and Jiawei Han

This tutorial aims to introduce recent advances in pre-trained text embeddings and language models (e.g., BERT and GPT), as well as their applications to a wide range of text mining tasks. The audiences will be provided with a systematic introduction of (1) the development of pre-trained text representation learning, (2) how the pre-trained models effectively empower fundamental text mining applications and (3) new techniques and approaches to tame pre-trained text representations for text mining tasks with few human annotations.
Target audiences include any researchers and practitioners who are interested in artificial intelligence (AI) and machine learning (ML) technologies for natural language and data mining applications using state-of-the-art pre-trained language models. The audiences will learn not only the background and history of text representation learning and text mining, but also the most recent models and methods along with their applications. Our tutorial has a special focus on weakly-supervised methods in text mining which require minimal human efforts for model learning. We will also demonstrate with real-world datasets how pre-trained text representations help mitigate the human annotation burden and facilitate automatic, accurate and efficient text analyses.

The target audience includes typical practitioners of AI who might have a high-level idea about preference learning but are not typically aware of the various challenging facets of the problem. The novelty of the tutorial lies in translating the different paradigms across communities into the language of AI so that it will benefit the ML/AI community. The tutorial will be self-contained and does not expect any prerequisites. Audience with basic knowledge of AI/ML would be able to follow most of the material.

Yu Meng

Yu Meng

UIUC

Yu Meng, Ph.D. student, Computer Science, UIUC. His research focuses on mining structured knowledge from massive text corpora with minimum human supervision. He received the Google PhD Fellowship (2021) in Structured Data and Database Management. He has delivered tutorials in VLDB’19, KDD’20 and KDD’21.

Jiaxin Huang

Jiaxin Huang

UIUC

Jiaxin Huang, Ph.D. student, Computer Science, UIUC. Her research focuses on mining structured knowledge from massive text corpora. She received the Microsoft Research PhD Fellowship (2021) and the Chirag Foundation Graduate Fellowship (2018) in Computer Science, UIUC. She has delivered tutorials in VLDB’19, KDD’20 and KDD’21.
Yu Zhang

Yu Zhang

UIUC

Yu Zhang, Ph.D. student, Computer Science, UIUC. His research focuses on weakly supervised text mining with structural information. He received WWW’18 Best Poster Award Honorable Mention. He has delivered tutorials in IEEE BigData’19 and KDD’21.
Jiawei Han

Jiawei Han

UIUC

Jiawei Han, Michael Aiken Chair Professor, Computer Science, UIUC. He has over 900 research publications. He is Fellow of ACM and IEEE, and has received numerous prominent awards, including ACM SIGKDD Innovation Award (2004) and IEEE Computer Society W. Wallace McDowell Award (2009). He delivered 50+ conference tutorials or keynote speeches.

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