ASSET Seminar: “Some Displaced Vignettes on Generalized Notions of Equivariance”
Abstract: The explicit incorporation of task-specific inductive biases through symmetry has emerged as a crucial design precept in the development of high-performance machine learning models. Symmetry-aware neural networks, such as […]
ASSET Seminar: “What’s In my Network? On Learned Proximals and Testing for Explanations”
Abstract: Modern machine learning methods are revolutionizing what we can do with data, from tiktok video recommendations to biomarkers discovery in cancer research. Yet, the complexity of these deep models […]
ASSET Seminar: “Advancing Diffusion Models for Text Generation”
Abstract: Transformer-based language models have undoubtedly become the dominant and favorite architecture for language generation of our time. However, although they provide impressive text quality, they tend to be hard […]
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 […]
ASSET Seminar: “Lifelong Learning for Autonomous Systems: Progress and Challenges” (Eric Eaton, University of Pennsylvania)
ABSTRACT: Research in lifelong or continual machine learning has advanced rapidly over the past few years, primarily focusing on enabling learned models to acquire new tasks over time while avoiding […]
ASSET Seminar: “What Should We “Trust” in Trustworthy Machine Learning?” (Aaron Roth, University of Pennsylvania)
ABSTRACT: “Trustworthy Machine Learning” has become a buzz-word in recent years. But what exactly are the semantics of the promise that we are supposed to trust? In this talk we will make a proposal, through the lens […]