4 accepted papers
Weighted sampling--sampling from a probability density function (PDF) proportional to the product of a base PDF and a weight function--is a fundamental technique with wide-ranging applications in variance reduction, biased sampling, data augmentation, and more. Leveraging the increasing availability
Risk-averse modeling is critical in safety-sensitive and high-stakes applications. Conditional Value-at-Risk (CVaR) quantifies such risk by measuring the expected loss in the tail of the loss distribution, and minimizing it provides a principled framework for training robust models. However, direct…
Clustered Federated Learning (CFL) is a promising distributed learning framework that addresses data heterogeneity issues across multiple clients by grouping clients and providing a shared generalized model for each group. However, under privacy-preserving federated learning protocols where there is…
Weak submodular optimization underpins many problems in signal processing and machine learning. For such problems, under a cardinality constraint, a simple greedy algorithm is guaranteed to find a solution with a value no worse than 1 − e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xli…