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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 […]

ASSET Seminar: “Building a Foundation for Trustworthy Machine Learning” (Elan Rosenfeld, Carnegie Mellon University)

ABSTRACT: Artificial Intelligence is being increasingly relied on in safety-critical domains. But the predictive models underlying these systems are notoriously brittle, and trustworthy deployment remains a significant challenge. In this […]

ASSET Seminar: “Reasoning Myths about Language Models: What is Next?” (Dan Roth, University of Pennsylvania)

ABSTRACT:  The rapid progress made over the last few years in generating linguistically coherent natural language has blurred, in the mind of many, the difference between natural language generation, understanding, […]

ASSET Seminar: “Bridging the Gap Between Deep Learning Theory and Practice” (Micah Goldblum, New York University)

ABSTRACT:  Despite the widespread proliferation of neural networks, the mechanisms through which they operate so successfully are not well understood.  In this talk, we will first explore empirical and theoretical […]

ASSET Seminar: “Making Machine Learning Predictably Reliable” (Andrew Ilyas, Massachusetts Institute of Technology)

ABSTRACT:  Despite ML models’ impressive performance, training and deploying them is currently a somewhat messy endeavor. But does it have to be? In this talk, I overview my work on […]

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 […]

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