4 accepted papers
Flow and diffusion models produce high-quality samples, but adapting them to user preferences or constraints post-training remains costly and brittle, a challenge commonly called reward alignment. We argue that efficient reward alignment should be a property of the generative model itself, not an af…
In generative modeling, we often wish to produce samples that satisfy a user-specified reward such as measurement consistency, aesthetic quality, or alignment with human intent, a problem known as inference-time guidance. While flow-based models enable high-quality generation, existing guidance meth…
Recent numerical experiments have demonstrated that the choice of optimization geometry used during training can impact generalization performance when learning expressive nonlinear model classes such as deep neural networks. These observations have important implications for modern deep learning, b…
Many existing tools in nonlinear control theory for establishing stability or safety of a dynamical system can be distilled to the construction of a certificate function which guarantees a desired property. However, algorithms for synthesizing certificate functions typically require a closed-form an