Trustworthy and Scalable Machine Learning
Published: | 1:35 pm | Posted in: Events
Speaker: Yang Zhou Date: Feb 23, 11:45am–12:45pm Abstract: Machine learning (ML), a powerful tool for automatically extracting, managing, inferencing, and transferring knowledge, has been proven to be extremely useful in understanding the intrinsic nature of real-world big data. Despite achieving remarkable performance, ML models, especially deep learning models, suffer from severe trustworthiness and scalability challenges: […]
From Theory to Application: Overparameterization and Machine Learning at the Edge
Published: | 1:29 pm | Posted in: Events
Speaker: Peizhong Ju Date: Feb 21, 11:45am–12:45pm Abstract: Machine Learning (ML), a vital branch of Artificial Intelligence (AI), has seen rapid advancements in recent years. As ML continues to evolve, it faces two major challenges: the need for deeper theoretical understanding and the complexities of deployment at the edge. In this talk, I will present […]
Exploring the Adversarial Robustness of Language Models
Published: | 1:48 am | Posted in: Events
Speaker: Muchao Ye Date: Feb 19, 11:45am–12:45pm Abstract: Language models built by deep neural networks have achieved great success in various areas of artificial intelligence, which have played an increasingly vital role in profound applications including chatbots and smart healthcare. However, since deep neural networks are vulnerable to adversarial examples, there are still concerns about […]
Resource-Efficient Machine Learning: Reduce the Cost of Graph Learning and Beyond
Published: | 3:24 am | Posted in: Events
Speaker: Xiaotian (Max) Han Date: Feb 16, 11:45am–12:45pm Abstract: In this talk, I will present my research on resource-efficient machine learning techniques for graph neural networks (GNNs) and beyond. These techniques aim to reduce the computational resources required by these models, making them more practical for real-world applications. i) I will discuss accelerating the training […]
Backdoor in AI: Algorithms, Attacks, and Defenses
Published: | 1:47 pm | Posted in: Events
Speaker: Ruixiang Tang Date: Feb 14, 11:45am–12:45pm Abstract: As deep learning models are increasingly integrated into critical domains, their safety emerges as a critical concern. This talk delves into the emerging threat of backdoor attacks. These attacks involve embedding a backdoor function within the victim model, allowing attackers to manipulate the model’s behavior using specific […]
Exploring, Counteracting and Harnessing Adversarial Examples
Published: | 4:12 pm | Posted in: Events
Speaker: Han Xu Date: Feb 12, 11:45am–12:45pm Abstract: Recently, with the development of AI and ML, their corresponding safety problems, especially their vulnerability to adversarial attacks, have also become increasingly important. In order to enhance the ML safety, it is essential to discover sound solutions for (1) identifying adversarial examples to uncover the weakness of […]
Structuring Cooperative Teams for Multi-Agent Autonomy
Published: | 4:07 pm | Posted in: Events
Speaker: Qi Zhang Date: Feb 9, 11:45am–12:45pm Abstract: Cooperative artificial intelligence (AI) equips a team of autonomous agents with the capability of planning and learning to maximize their joint utility, which finds a wide range of applications. While being a promising paradigm, current solutions to cooperative AI, instantiated as cooperative multi-agent planning and learning frameworks, […]
Learning from Imperfect Data: Incremental Learning and Few-shot Learning
Published: | 1:36 pm | Posted in: Events
Speaker: Yaoyao Liu Date: Feb 7, 11:45am–12:45pm Abstract: In recent years, artificial intelligence (AI) has achieved great success in many fields. Although impressive advances have been made, AI algorithms still suffer from an important limitation: they rely on static and large-scale datasets. In contrast, human beings naturally possess the ability to learn novel knowledge from […]
Data-Centric AI: Taming AI-ready Feature Space From Decision-Making to Generative-AI
Published: | 2:57 pm | Posted in: Events
Speaker: Dongjie Wang Date: Feb 5, 11:45am–12:45pm Abstract: Unlike humans, AI systems are brittle and not robust. They often struggle when faced with novel situations, and are highly sensitive to small perturbations, which can lead to catastrophically poor performance. These systems comprise two main components: the model and the data. In recent decades, research primarily […]
Empowering Graph Neural Networks for Real-world Tasks
Published: | 4:24 pm | Posted in: Events
Speaker: Zhichun Guo Date: Feb 2, 11:45am–12:45pm Abstract: Graph neural networks (GNNs) have been widely used on graph-structured data, but they also face a series of challenges in solving real-world problems, including scarcity of labeled data, scalability issues, and potential bias. In this talk, I will recap my research efforts in tackling these challenges and […]