IDEAS on Generative AI Symposium
Online Registration Closed: Onsite registration opens on April 30 at the venue. Click here for virtual attendance on April 30 Webinar ID: 959 7142 6525 Invite Link: https://upenn.zoom.us/j/95971426525 Click here for all the […]
FOLDS seminar: Bridging Computational and Statistical Theories of Machine Learning
Zoom link: https://upenn.zoom.us/j/98220304722 There are two broad strands of literature on the theoretical underpinnings of machine learning. “Computational“ learning theory traces its origins to theoretical computer science, with an influential paper […]
FOLDS seminar: Surrogate-Model Approaches to Optimizers for LLM Training
Zoom link: https://upenn.zoom.us/j/98220304722 The recent empirical success of the Muon optimizer in training large language models has outpaced the theoretical understanding of its matrix-gradient orthogonalization design. To bridge this gap, […]
FOLDS seminar: Global Convergence of Gradient EM for Over-Parameterized Gaussian Mixtures
Zoom link: https://upenn.zoom.us/j/98220304722 Learning Gaussian Mixture Models (GMMs) is a fundamental problem in machine learning, and the Expectation-Maximization (EM) algorithm and its variant gradient-EM are the most widely used algorithms […]
FOLDS seminar: Differentially Private Space-Efficient Algorithms for Counting Distinct Elements in the Turnstile Model
ATTENTION: NEW DATE AND LOCATION Monday, March 23, 2026 (Noon – 1 pm) Glandt Forum, Singh Center 3205 Walnut St, Philadelphia, PA 19104 Zoom link: https://upenn.zoom.us/j/98220304722 The turnstile continual release […]
FOLDS seminar: Coherence Mechanisms for Provable Self-Improvement
Zoom link: https://upenn.zoom.us/j/98220304722 Large language models are increasingly trained to improve themselves, yet the mechanisms driving this, such as self-reflection or RLAIF, rely almost entirely on empirical heuristics. Is it possible […]
FOLDS seminar & PENN AI seminar: Optimization Challenges in Physics-Informed Neural Networks
Zoom link: https://upenn.zoom.us/j/98220304722 Physics-informed neural networks (PINNs) minimize composite losses that penalize PDE residuals alongside boundary and initial conditions. While this resembles multi-task learning, the optimization landscape is fundamentally different. […]
FOLDS seminar: Multi-step reasoning via curriculum learning
Zoom link: https://upenn.zoom.us/j/98220304722 Can multi-step reasoning be learned from data? We investigate this question in the context of a simple function composition task. We prove that this task is […]
FOLDS seminar: Fast Convergence of High-Order ODE Solvers for Diffusion Models
Zoom link: https://upenn.zoom.us/j/98220304722 Score-based diffusion models can be sampled efficiently by reformulating the reverse dynamics as a deterministic probability flow ODE and integrating it with high-order solvers. Since the […]
FOLDS seminar: Transformers Meet In-Context Learning: A Universal Approximation Theory
Zoom link: https://upenn.zoom.us/j/98220304722 Large language models are capable of in-context learning, the ability to perform new tasks at test time using a handful of input-output examples, without parameter updates. […]