Machine Intelligence and Information Theory Lab. 기계지능 및 정보학습 연구실

We are mainly interested in learning theory to provide the fundamental theoretical understanding of machine intelligence ('MI') via information theory ('IT'). Based on theory, we further develop robust/reliable intelligent systems from the perspectives, including generalization, robustness, safety, etc. See the details below. Modern deep AI requires sufficient generalization across different tasks and demands to be robust against out-of-distribution (e.g., long-tail problems). Our group tries to establish principles of Generalization and Robustness of deep models across frameworks, including reinforcement learning models, deep generative models, multimodal models, vision-language and action models. Another branch is the Reliability of deep models against unintended or adversarial disruptions, including data corruptions and model/data adversary. Our group focuses on lifelong learning algorithms that can expand learned knowledge with a few shots of training data samples while enhancing past knowledge. To this end, we are researching Meta-learning, Continual learning, Incremental learning, Few-shot learning, and any related topics. Our group develops decentralized learning frameworks utilizing distributed computing/communication/storage resources in complicated networks. To this end, we are studying Federated learning (FL), Decentralized learning, Distributed systems, and any related fields. For another branch, we aim to understand privacy and security issues raised from the perspectives of data/knowledge/model. This includes our research topics of Unlearning, Inversion-attack, and theoretical analysis on the privacy of modern deep models. To be specific, we are studying RAN-agnostic intelligent communications, Semantic communications, ML-based RAN/Protocols, etc. Beyond t