From causal inference to autoencoders, memorization & gene regulation - Caroline Uhler, MIT

Recent progress in genomics makes it possible to perform perturbation experiments at a
very large scale. This motivates the development of a causal inference framework that is
based on observational and interventional data. We characterize the causal relationships
that are identifiable and present the first provably consistent algorithm for learning a
causal network from such data. I will then couple gene expression with the 3D genome
organization. In particular, we will discuss approaches for integrating different data
modalities such as sequencing or imaging via autoencoders. We end by a theoretical
analysis of autoencoders linking overparameterization to memorization. In particular, we
will show that overparameterized autoencoders trained using standard optimization
methods implement associative memory and provide a mechanism for memorization and
retrieval of real-valued data.

---

Recent years have witnessed an increased cross-fertilisation between the fields of statistics and computer science. In the era of Big Data, statisticians are increasingly facing the question of guaranteeing prescribed levels of inferential accuracy within certain time budget. On the other hand, computer scientists are progressively modelling data as noisy measurements coming from an underlying population, exploiting the statistical regularities of the data to save on computation.

This cross-fertilisation has led to the development and understanding of many of the algorithmic paradigms that underpin modern machine learning, including gradient descent methods and generalisation guarantees, implicit regularisation strategies, high-dimensional statistical models and algorithms.

About the event

This event will bring together experts to talk about advances at the intersection of statistics and computer science in machine learning. This two-day conference will focus on the underlying theory and the links with applications, and will feature 12 talks by leading international researchers.

The intended audience is faculty, postdoctoral researchers and Ph.D. students from the UK/EU, in order to introduce them to this area of research and to the Turing.