5 papers
Who to Trust? Aggregating Client Predictions in Federated Distillation
Viktor Kovalchuk, Denis Son, Arman Bolatov +6
Under data heterogeneity (e.g., ), clients may produce unreliable predictions for instances belonging to unfamiliar classes. An equally weighted combinatio…
Byzantine-Robust Optimization under -Smoothness
Arman Bolatov, Samuel Horváth, Martin TakÃ¡Ä +1
We consider distributed optimization under Byzantine attacks in the presence of -smoothness, a generalization of standard -smoothness that captures functions with sta…
Simplex Deep Linear Discriminant Analysis
Maxat Tezekbayev, Arman Bolatov, Zhenisbek Assylbekov
We revisit Deep Linear Discriminant Analysis (Deep LDA) from a likelihood-based perspective. While classical LDA is a simple Gaussian model with linear decision boundaries, attachi…
Deep Linear Discriminant Analysis Revisited
Maxat Tezekbayev, Rustem Takhanov, Arman Bolatov +1
We show that for unconstrained Deep Linear Discriminant Analysis (LDA) classifiers, maximum-likelihood training admits pathological solutions in which class means drift together, c…
Overspecified Mixture Discriminant Analysis: Exponential Convergence, Statistical Guarantees, and Remote Sensing Applications
Arman Bolatov, Alan Legg, Igor Melnykov +3
This study explores the classification error of Mixture Discriminant Analysis (MDA) in scenarios where the number of mixture components exceeds those present in the actual data dis…