ASSET Seminar: “Wood Wide Models”
Abstract: Foundation models are monolithic models that are trained on a broad set of data, and which are then in principle fine-tuned to various specific tasks. But they are ill-suited […]
ASSET Seminar: “Towards Pluralistic Alignment: Foundations for Learning from Diverse Human Preferences”
Abstract: Large pre-trained models trained on internet-scale data are often not ready for safe deployment out-of-the-box. They are heavily fine-tuned and aligned using large quantities of human preference data, usually […]
ASSET Seminar: “Robustness in the Era of LLMs: Jailbreaking Attacks and Defenses”
Abstract: Despite efforts to align large language models (LLMs) with human intentions, popular LLMs such as chatGPT, Llama, Claude, and Gemini are susceptible to jailbreaking attacks, wherein an adversary fools a targeted LLM into […]
ASSET Seminar: “Representation-based Learning and Control for Dynamical Systems”
Abstract: The explosive growth of machine learning and data-driven methodologies have revolutionized numerous fields. Yet, the translation of these successes to the domain of dynamical physical systems remains a significant […]
ASSET Seminar: “Bridging the Gap Between Learning and Programming”
Abstract: For decades, we have built software by writing code, but in recent years machine learning has emerged as a new approach to create software with features that would be […]