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CIS Seminar: “Efficient Probabilistically Checkable Proofs from High-Dimensional Expanders”

Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

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 constant number of queries to the proof. This result is a significant milestone in computer science and has important implications for approximation algorithms, cryptography, and […]

ASSET Seminar: “Demystifying the Inner Workings of Language Models”

Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

Abstract: Large language models (LLMs) power a rapidly-growing and increasingly impactful suite of AI technologies. However, due to their scale and complexity, we lack a fundamental scientific understanding of much of LLMs’ behavior, even when they are open source. The “black-box” nature of LMs not only complicates model debugging and evaluation, but also limits trust […]

IDEAS/STAT Optimization Seminar: “Theoretical foundations for multi-agent learning”

Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

As learning algorithms become increasingly capable of acting autonomously, it is important to better understand the behavior that results from their interactions. For example, a pervasive challenge in multi-agent learning settings, which spans both theory and practice and dates back decades, has been the failure of convergence for iterative algorithms such as gradient descent. Accordingly, […]

CIS Seminar: “Learning Theoretic Foundations for Modern (Data) Science”

Levine 307 3330 Walnut Street, Philadelphia, PA, United States

In this talk, I will explain how fundamental problems in computational learning theory are at the heart of modern problems in machine learning and scientific applications and how algorithmic insights in mathematically tractable models can inspire new solutions in a wide variety of domains. I will explore two directions. First, I will explore algorithmic foundations […]

ESE Guest Seminar – “On Team Decision Problems with Nonclassical Information Structures”

Greenberg Lounge (Room 114), Skirkanich Hall 210 South 33rd Street, Philadelphia, PA, United States

Team theory is a mathematical formalism for decentralized stochastic control problems in which a “team,” consisting of a number of members, cooperates to achieve a common objective. It was developed to provide a rigorous mathematical framework of cooperating members in which all members have the same objective yet different information. In static team problems, the […]

CIS Seminar: ” Specializing LLMs for Reliability”

Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

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 do not do this reliably. They hallucinate facts, convincingly state incorrect deductions, and exhibit logical fallacies like confirmation bias. In this talk, I will describe […]

IDEAS/STAT Optimization Seminar: “ML for an Interactive World: From Learning to Unlearning”

Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

The remarkable recent success of Machine Learning (ML) is driven by our ability to develop and deploy interactive models that can solve complicated tasks by understanding and adapting to the ever-changing state of the world. However, the development of such models demands significant data and computing resources. Moreover, as these models increasingly interact with humans, […]

CIS Seminar: “Realizing the Promise of Language-level Security in Real Systems”

Levine 307 3330 Walnut Street, Philadelphia, PA, United States

Promises are cheap. Software vendors routinely describe their offerings as “secure”, but few are based on designs that can guarantee even the most basic security properties. To address this problem, services like Cloudflare, Android, and Firefox are increasingly relying on languages like Rust and WebAssembly to provide safety by design. But these promises too can […]

CIS Seminar: “Privacy, Copyright, and Data Integrity: The Cascading Implications of Generative AI”

Amy Gutmann Hall, Room 414 3333 Chestnut Street, Philadelphia, United States

The rapid adoption of generative AI has created a cycle where personal information cascades perpetually: from people to models to applications and online platforms, then back through scrapers into the system. Simple blanket rules such as "don't train on this data" or "don't share sensitive information" are inadequate, as we face training data scarcity while […]

CIS Seminar: “Intelligence Augmentation for Scientific Researchers”

Special location for this talk: 105  Amy Gutmann Hall Recent advances in Artificial Intelligence are powering revolutionary interactive tools that will transform the very nature of the scientific enterprise. We describe several large-scale projects at the Allen Institute for AI aimed at developing open models, agentic platforms, and novel interaction paradigms in order to amplify […]