And of course because this is my first ever post I forgot to include hashtags! #ICML2025

Check out the paper if interested and come talk to me during the poster session (July 17, Thursday at 4:30pm) if in Vancouver! icml.cc/virtual/2025.... [11/n]

Finally, R-MDN, because it operates on the level of individual examples, can be integrated in both convolutional neural networks and vision transformers—which was one of the significant limitations of the MDN algorithm. [10/n]

And R-MDN makes equitable predictions across population groups, such as across both boys and girls when performing sex classification on the ABCD (Casey et al., 2008) dataset in the presence of pubertal development scores as the confounder. [9/n]

R-MDN can also remove the influence from multiple confounding variables, as seen when testing on the ADNI (Mueller et al., 2005) dataset. [8/n]

Since R-MDN is a normalization layer, it can be tacked on to various already-proposed model architectures. [7/n]

R-MDN effectively removes confounder influence from learned DNN features, as rigorously verified in both synthetically controlled environments and real-world datasets. [6/n]

We propose Recursive Metadata Normalization (R-MDN), a normalization layer that leverages the statistical recursive least squares algorithm to iteratively update its internal parameters based on previously computed values whenever new data are received. [5/n]

However, within continual learning, data becomes available sequentially, often over the span of several years, as in longitudinal studies. [4/n]

Prior work such as BR-Net (Adeli et al., 2020), MDN (Lu et al., 2021), and P-MDN (Vento et al., 2022) proposed to learn confounder-invariant representations in DNNs work within a static learning setting and assume that the algorithm has access to all data at the outset of training. [3/n]

Confounders are variables that influence both the outcome (i.e., the output) and the exposure (i.e., the input) in a study, causing spurious associations. [2/n]