Interactive Robot Learning from Non-Expert Human Teachers Abstract: Today’s general-purpose robot learning policies are limited to 50-80% zero-shot performance on downstream tasks. To close this [...]

Polynomial Graph Neural Networks: Theoretical Limits and Graph Noise Impact Abstract: This talk examines the theoretical foundations of Graph Neural Networks (GNNs), focusing on polynomial [...]

Avoiding Spurious Sharpness Minimization Abstract: Curvature regularization techniques like Sharpness Aware Minimization (SAM) have shown great promise in improving generalization on vision tasks. However, we [...]

Reception with light refreshments in the lobby at 3:30 PM, followed by lecture How to Appease a Majority? Abstract: In 1785, Condorcet established a frustrating [...]

Towards More Reliable Generative AI: Probing Failure Modes, Harnessing Test-Time Inference, and Interpreting Diffusion Models Abstract: In this talk, we explore strategies for making generative [...]

Learning From Aggregated Responses: Improving Model Utility Under Privacy Constraints Abstract: In many real-world scenarios, training data is aggregated before being shared with the learner [...]