5 accepted papers
Ensemble methods, particularly boosting, have established themselves as highly effective and widely embraced machine learning techniques for tabular data. In this paper, we aim to leverage the robust predictive power of traditional boosting methods while enhancing fairness and interpretability. To a…
Characterizing interactions between brain areas is a fundamental goal of systems neuroscience. While such analyses are possible when areas are recorded simultaneously, it is rare to observe all combinations of areas of interest within a single animal or recording session. How can we leverage multi-a…
Elucidating functional connectivity reveals interactions between brain regions based on their in vivo activity. However, a gap exists in current statistical methods’ ability to untangle direct interactions from confounding activity due to stimuli and physiological effects, leading to a bottleneck in…
In this work, we propose data augmentation via pairwise mixup across subgroups to improve group fairness. Many real-world applications of machine learning systems exhibit biases across certain groups due to underrepresentation or training data that reflects societal biases. Inspired by the successes…
High-dimensional Gaussian graphical models have been a powerful tool for learning connections or interaction patterns among a large number of variables. While most prior work focuses on the case when all variables are measured simultaneously, we consider a practically common but new setting when no…