CIS Seminar: ” Specializing LLMs for Reliability”
Large language models (LLMs) have advanced the frontiers of AI reasoning: they can synthesize information from multiple sources, derive new conclusions, and explain those conclusions to their users. However, LLMs […]
CIS Seminar: “Efficient Probabilistically Checkable Proofs from High-Dimensional Expanders”
The PCP theorem, proved in the 90’s, shows how to encode a proof for any theorem into a format where the theorem’s correctness can be verified by making only a […]
CIS Seminar: “Bridging Informal and Formal AI Reasoning”
Neural language models have opened a fascinating, flexible platform for reasoning in mathematics, programming, and beyond. This talk will explore the intersection of these models and the rigor of formal […]
ASSET Seminar: “Algorithmic Stability for Trustworthy Machine Learning and Statistics”
Abstract: Data-driven systems hold immense potential to positively impact society, but their reliability remains a challenge. Their outputs are often too brittle to changes in their training data, leaving them […]
CIS Seminar: “Leveraging the Wisdom of Clouds for Internet Security”
Over the past decade, networked systems have consolidated under just a handful of hyperscale cloud providers (e.g., AWS, Azure). While this offers logistical and economic advantages, attackers specifically target providers […]
IDEAS/STAT Optimization Seminar: Jason Altschuler
Zoom link: https://upenn.zoom.us/j/98220304722
IDEAS/STAT Optimization Seminar: Angelia Nedich
Zoom link: https://upenn.zoom.us/j/98220304722
IDEAS/STAT Optimization Seminar: “Gradient Equilibrium in Online Learning”
We present a new perspective on online learning that we refer to as gradient equilibrium: a sequence of iterates achieves gradient equilibrium if the average of gradients of losses along […]
IDEAS/STAT Optimization Seminar: Frank E. Curtis
Zoom link: https://upenn.zoom.us/j/98220304722