ASSET Seminar: “Paths to AI Accountability” (Sarah Cen, Massachusetts Institute of Technology)
ABSTRACT: In the past decade, we have begun grappling with difficult questions related to the rise of AI, including: What rights do individuals have in the age of AI? When […]
ASSET Seminar: “Mathematical Foundations for Physical Agents” (Max Simchowitz, Massachusetts Institute of Technology, CSAIL)
ABSTRACT: From robotics to autonomous vehicles, machine learning agents deployed in the physical world (“physical agents”) promise to revolutionize endeavors ranging from manufacturing to agriculture to domestic labor. In this […]
ASSET Seminar: “Towards A New Frontier of Trustworthy AI: Interpretable Machine Learning Algorithms that Produce All Good Models” (Chudi Zhong, Duke University)
ABSTRACT: Machine learning has been increasingly deployed for high-stakes decisions that deeply impact people’s lives. My research focuses on developing interpretable algorithms and pipelines to ensure the safe and efficient utilization […]
ASSET Seminar: “Statistical Methods for Trustworthy Language Modeling” (Tatsu Hashimoto, Stanford University)
ABSTRACT: Language models work well, but they are far from trustworthy. Major open questions remain on high-stakes issues such as detecting benchmark contamination, identifying LM-generated text, and reliably generating factually […]
ASSET Seminar: “Scaling Your Large Language Models on a Budget” (Atlas Wang, University of Texas at Austin)
ABSTRACT: As the sizes of Large Language Models (LLMs) continue to grow exponentially, it becomes imperative to explore novel computing paradigms that can address the dual challenge of scaling these […]
ASSET Seminar: “Large Language Models in Medicine: Opportunities and Challenges” (Mark Dredze, Johns Hopkins University)
ABSTRACT: The rapid advance of AI driven by Large Language Models (LLMs), like ChatGPT, has led to impressive results across a range of different use cases. This has included several […]
ASSET Seminar: “Enforcing Right to Explanation: Algorithmic Challenges and Opportunities” (Himabindu Lakkaraju, Harvard University)
ABSTRACT: As predictive and generative models are increasingly being deployed in various high-stakes applications in critical domains including healthcare, law, policy and finance, it becomes important to ensure that relevant […]
ASSET Seminar: “Learning to Read X-Ray: Applications to Heart Failure Monitoring” (Polina Golland, Massachusetts Institute of Technology)
ABSTRACT: We propose and demonstrate a novel approach to training image classification models based on large collections of images with limited labels. We take advantage of availability of radiology reports […]
ASSET Seminar: “Robust Machine Learning with Foundation Models” (Aditi Raghunathan, Carnegie Mellon University)
ABSTRACT: In recent years, foundation models—large pretrained models that can be adapted for a wide range of tasks—have achieved state-of-the-art performance on a variety of tasks. While the pretrained models […]
ASSET Seminar: “Inherent Interpretability via Language Model Guided Bottleneck Design” (Mark Yatskar, Penn)
ABSTRACT: As deep learning systems improve, their applicability to critical domains is hampered because of a lack of transparency. Post-hoc explanations attempt to address this concern but they provide no […]