Entity linking (EL) is the task of linking the text segments to the referring entities in the knowledge graph, typically decomposed into mention detection, and entity disambiguation. Compared to traditional methods treating the two tasks separately, recent end-to-end entity linking methods exploit the mutual dependency between mentions and entities to achieve better performance. However, existing end-to-end EL methods have problems utilizing the dependency of mentions and entities in the task. To this end, we propose to model the EL task as a hierarchical decision-making process and design a hierarchical reinforcement learning algorithm to solve the problem. We conduct extensive experiments to show that the proposed method achieves state-of-the-art performance in several EL benchmark datasets.

End-to-end Entity Linking with Hierarchical Reinforcement Learning

Over the past few years, artificial intelligence (AI) has achieved great success in a variety of applications, such as image classification and recommendation systems. This suc- cess has often been achieved by training machine learning models on static datasets, where inputs and desired outputs are provided. However, we are now seeing a shift in this paradigm. Instead of learning from static datasets, machine learning models are increasingly being trained through feed- back from their interactions with the world. This is particu- larly important when machine learning models are deployed in the real world, as their decisions can often have an im- pact on other agents, turning the decision-making process into a multi-agent problem. As a result, multi-agent learning in complex environments is a critical area of research for the next generation of AI, particularly in the context of cooper- ative tasks. Cooperative multi-agent learning is an essential problem for practitioners to consider as it has the potential to enable a wide range of multi-agent tasks.
In this presentation, we will review the background and challenges of cooperative multi-agent learning. We will sur- vey our research that aims to address challenges in the fol- lowing areas:
• Scalability and cooperation in multi-agent learning, in- cluding studies on (Han et al. 2019; Du et al. 2019; Xu et al. 2020; Du et al. 2021a; Fang et al. 2019; Mguni et al. 2021). • Evaluating AI agents, including studies on (Yan et al. 2022; Du et al. 2021b).
• Human-in-the-loop interactive learning for robust and trusted decision making, including studies on (Kazantzidis et al. 2022; Gu et al. 2022; Liu et al. 2022).
• Various applications of cooperative multi-agent learning, including (Ruan et al. 2022; Xu et al. 2020; Wang et al. 2021; Chen et al. 2021).
We will also discuss future directions for research in this field.

Cooperative Multi-Agent Learning in a Complex World: Challenges and Solutions

Safe and reliable decision-making is critical for long-term deployment of autonomous systems. Despite the recent advances in artificial intelligence, ensuring safe and reliable operation of human-aligned autonomous systems in open-world environments remains a challenge. My research focuses on developing planning and learning algorithms that support reliable autonomy in fully and partially observable environments, in the presence of uncertainty, limited information, and limited resources. In this talk, I will cover (1) how agents can learn to recognize and plan to avoid the Markovian and non-Markovian negative side effects of their actions, and (2) how explanations can be used to verify and learn human-aligned rewards from demonstrations.

Planning and Learning for Reliable Autonomy in the Open World

Humans perceive surrounding scenes through multiple senses with multisensory integration. For example, hearing helps capture the spatial location of a racing car behind us; seeing peoples' talking faces can strengthen our perception of their speech. However, today's state-of-the-art scene understanding systems are usually designed to rely on a single audio or visual modality. Ignoring multisensory cooperation has become one of the key bottlenecks in creating intelligent systems with human-level perception capability, which impedes the real-world applications of existing scene understanding models. To address this limitation, my research has pioneered marrying computer vision with computer audition to create multimodal systems that can learn to understand audio and visual data. In particular, my current research focuses on asking and solving fundamental problems in a fresh research area: audio-visual scene understanding and strives to develop unified, explainable, and robust multisensory perception machines. The three themes are distinct yet interconnected, and all of them are essential for designing powerful and trustworthy perception systems. In my talk, I will give a brief overview about this new research area and then introduce our works in the research thrusts.

Audio-Visual Scene UnderstandingTowards Unified and Robust Multisensory Perception

Agent-based modeling and simulation can provide a powerful test environment for crisis management scenarios. Human agent interaction has limitations in representing norms issued by an agent to a human agent that has emotions. In this study, we present an approach to the interaction between a virtual normative agent and a human agent in an evacuation scenario. Through simulation comparisons, it is shown that the method used in this study can more fully simulate the real-life out come of an emergency situation and also improves the au thenticity of the agent interaction.

Development of a Human-Agent Interaction System including Norm and Emotion in an Evacuation Situation

A hallmark of human intelligence is that we continue to learn new information and then extrapolate the learned information onto new tasks and domains (Thrun and Pratt (1998)). While this is a fairly intuitive observation, formulating such ideas has proved to be a challenging research problem and continues to inspire new studies. Recently, there has been increasing interest in AI/ML about building models that generalize across tasks, even when they have some form of distribution shifts. How can we ground this research in a solid framework to develop principled methods for better practice? This talk will present my recent works addressing this research question, including understanding information transfer in multitask learning, identifying negative transfers in multitask learning using surrogate models, and positive and negative generalizations in data augmentation.

Information Transfer in Multitask Learning, Data Augmentation, and Beyond

Conversational Systems (CSys) represent practical and tangible outcomes of advances in NLP and AI. CSys see continuous improvements through unsupervised training of large language models (LLMs) on a humongous amount of generic training data. However, when these CSys are suggested for use in domains like Mental Health, they fail to match the acceptable standards of clinical care, such as the clinical process in Patient Health Questionnaire (PHQ-9). The talk will present, Knowledge-infused Learning (KiL), a paradigm within NeuroSymbolic AI that focuses on making machine/deep learning models (i) learn over knowledge-enriched data, (ii) learn to follow guidelines in process-oriented tasks for safe and reasonable genera-
tion, and (iii) learn to leverage multiple contexts and stratified knowledge to yield user-level explanations. KiL established Knowledge-Intensive Language Understanding, a set of tasks for assessing safety, explainability, and conceptual flow in CSys.

Targeted Knowledge Infusion To Make Conversational AI Explainable and Safe

Many real-world sequential decision problems can be decomposed into processes with independent dynamics that are coupled via the action structure. I discuss recent work on such problems and future directions.

Monitoring and Intervening on Large Populations of Weakly Coupled Processes with Social Impact Applications

Deep neural networks (DNNs) can easily be manipulated (by an adversary) to output drastically different predictions and can be done so in a controlled and directed way. This process is known as adversarial attack and is considered one of the major hurdles in using DNNs in high-stakes and real-world applications. Although developing methods to secure DNNs against adversaries is now a primary research focus, it suffers from limitations such as a lack of optimization generality and scalability. My research highlights will offer a holistic understanding of optimization foundations for robust AI, peer into their emerging challenges, and present recent solutions developed by my research group.

General and Scalable Optimization for Robust AI

Human intelligence is beyond pattern recognition. From a single image, we’re able to explain what we see, reconstruct the scene in 3D, predict what’s going to happen, and plan our actions accordingly. In this talk, I will present our recent work on physical scene understanding—building versatile, data-efficient, and generalizable machines that learn to see, reason about, and interact with the physical world. The core idea is to exploit the generic, causal structure behind the world, including knowledge from computer graphics, physics, and language, in the form of approximate simulation engines, and to integrate them with deep learning. Here, deep learning plays two major roles: first, it learns to invert simulation engines for efficient inference; second, it learns to augment simulation engines for constructing powerful forward models. I’ll focus on a few topics to demonstrate this idea: building scene representation for both object geometry and physics; learning expressive dynamics models for planning and control; perception and reasoning beyond vision.

Learning to See the Physical World

In recent years, disinformation and fake news have become a global phenomenon, particularly so during the pandemic, COVID-19. The wide dissemination of fake news can have detrimental societal effects on individuals and the society. Despite the recent progress in detecting fake news, it remains a challenging task due to its complexity, diversity, multi-modality, speed, and costs of fact-checking or annotation. In this talk, we look at some challenges and lessons learned when exploring effective and explainable detecting strategies of fake news, and discuss challenges in fake news research and the need for interdisciplinary research.

Combating Disinformation on Social Media and Its Challenges: A Computational Perspective

In the talk, I shall describe my lab’s recent advances in AI, applied machine learning, and data mining to combat malicious actors (sockpuppets, ban evaders, etc.) and dangerous content (misinformation, hate, etc.) on web and social media platforms. My vision is to create a trustworthy online ecosystem for everyone and create the next generation of socially-aware methods that promote health, equity, and safety. Broadly, in my research, I have created novel graph, content (NLP, multimodality), and adversarial machine learning methods leveraging terabytes of data to detect, predict, and mitigate online threats. My interdisciplinary research innovates socio-technical solutions that I achieve by amalgamating computer science with social science theories. I am also passionate about putting my re- search into practice – my lab’s models have been deployed on Flipkart, influenced Twitter’s Birdwatch, and now being deployed on Wikipedia. My research has started a paradigm shift from the current slow and reactive approach against online harms to agile, proactive, and whole-of-society solutions. My talk will outline four thrusts of my research:
(1) Detection of harmful content and malicious actors across platforms, languages, and modalities
(2) Robustifying detection models against adversarial actors by predicting future malicious activities
(3) Attributing the impact of harmful content and the role of recommender systems
(4) Mitigation techniques to counter misinformation by professionals and non-expert crowds

Underline digital video library

End-to-end Entity Linking with Hierarchical Reinforcement Learning

Cooperative Multi-Agent Learning in a Complex World: Challenges and Solutions

Planning and Learning for Reliable Autonomy in the Open World

Audio-Visual Scene UnderstandingTowards Unified and Robust Multisensory Perception

Development of a Human-Agent Interaction System including Norm and Emotion in an Evacuation Situation

Information Transfer in Multitask Learning, Data Augmentation, and Beyond

Targeted Knowledge Infusion To Make Conversational AI Explainable and Safe

Monitoring and Intervening on Large Populations of Weakly Coupled Processes with Social Impact Applications

General and Scalable Optimization for Robust AI

Learning to See the Physical World

Combating Disinformation on Social Media and Its Challenges: A Computational Perspective

Advances in AI for Safety, Equity, and Well-Being on Web and Social Media: Detection, Robustness, Attribution, and Mitigation

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