A recent paper by Daubechies et al. claims that two independent component analysis (ICA)
algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance …

For the 24th Machine Learning for Signal Processing competition, participants were asked
to automatically diagnose schizophrenia using multimodal features derived from MRI scans. …

Devising procedures for downstream task-oriented generative model selections is an unresolved
problem of practical importance. Existing studies focused on the utility of a single family …

Synthetic data has made tremendous strides in various commercial settings including finance,
healthcare, and virtual reality. We present a broad overview of prototypical applications of …

Identifying the complex activity relationships present in rich, modern neuroimaging data sets
remains a key challenge for neuroscience. The problem is hard because (a) the underlying …

Large-scale graphs with node attributes are fundamental in real-world scenarios, such as
social and financial networks. The generation of synthetic graphs that emulate real-world ones …

Recent years have seen a surge of machine learning approaches aimed at reducing disparities
in model outputs across different subgroups. In many settings, training data may be used …

Creation of a synthetic dataset that faithfully represents the data distribution and simultaneously
preserves privacy is a major research challenge. Many space partitioning based …

Goal recognition aims at predicting human intentions from a trace of observations. This
ability allows people or organizations to anticipate future actions and intervene in a positive (…

Privacy concerns have led to a surge in the creation of synthetic datasets, with diffusion
models emerging as a promising avenue. Although prior studies have performed empirical …