Schedule

Title: Synthetic Data: Powerful Creation, Not Second-Rate Copy

Abstract: In my talk, I will showcase how synthetic data, generated by deep generative models based on real-world data, enables solutions in healthcare that are unattainable with real data alone. I will discuss the transformation of biased datasets into unbiased ones using synthetic data. My talk will also explore how generative models facilitate transfer learning across various domains, enhancing the versatility of machine learning models. I will also cover the importance of data augmentation, where synthetic data enriches training sets for more comprehensive machine learning outcomes. Additionally, I will highlight the crucial role of synthetic data in the thorough testing and debugging of these models, ensuring their dependability in healthcare settings. 

Title: A look into generative models in healthcare with a dive into continuous time generative models

Abstract: This talk will discuss some of the uses of generative models in healthcare, dive into continuous time generative models, and as this is a workshop, step back to high level speculations about generative modeling and the needs of generative modeling in healthcare. Along the way, I will cover two developments in continuous time generative models 1) learning the noising process in a diffusion model to maximize likelihood and 2) choosing the base distribution in flows/interpolants to facilitate learning. References to what I will cover:

Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions: https://arxiv.org/abs/2302.07261

Stochastic interpolants with data-dependent couplings: https://arxiv.org/abs/2310.03725

On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease: https://arxiv.org/abs/2301.11962

Title: Modelling Cellular Perturbations With Deep Generative Models and their application to scientific discovery related to disease

Abstract: Studying biological systems is hard, since they are the domain of microscopic processes that are typically hard to measure and observe and mired in complexity. A typical approach towards studying systems of such complexity is to perform perturbations, study their outcomes, and try to understand the links to mechanisms we may want to control better. In this talk, we will talk about a class of deep generative models [1] that is tailored to this task, in that it studies readouts of cells and disentangles latent spaces suitably to isolate perturbation effects. We will introduce the model, how it can help us perform counterfactual reasoning over cells, discuss evaluation of such models, and sketch the work ahead to apply it fruitfully in service of discovery work.

[1] Modelling cellular perturbations with the sparse additive mechanism shift variational autoencoder, Michael Bereket, Theofanis Karaletsos, NeurIPS2023

Title: Validation with Large Generative Models: A Need for Human-Centric Approaches

Abstract: Especially in applications such as health, we really want to know whether or not our models will behave as we want them to.  And for smaller-surface models, including deep generative ones, we have a number of statistical and human-centered techniques to gain confidence that these models are doing largely reasonable things.  However, these techniques, already partial for smaller-surface models, are able to provide even fewer assurances in the context of larger-surface models.  In this talk, I will discuss how we must fundamentally re-think our approach to validation for larger-surface models.  In particular, much of the validation effort must shift from statistical checks done in advance to human-centered checks for a particular output at task-time.  I will discuss how this effort will require new methods and lay out some open questions and directions in this space.