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stat.ML2026
Federated Nonparametric Regression with Heterogeneous Clients
Xiaoran Jiang, Anand N. Vidyashankar
Whether a federated predictor benefits an individual client depends on both population heterogeneity and estimation uncertainty. We study this question for Nadaraya--Watson regress…
stat.ML2026
Domain Adaptation with Target Information via Doubly-Anchored Distributionally Robust Optimization
David Kepplinger, Anand N. Vidyashankar
Domain Adaptation (DA) often lacks worst-case guarantees, while Distributionally Robust Optimization (DRO) based only on source data centers its ambiguity set at the source law and…
stat.ML2025
Hellinger loss function for Generative Adversarial Networks
Giovanni Saraceno, Anand N. Vidyashankar, Claudio Agostinelli
We propose Hellinger-type loss functions for training Generative Adversarial Networks (GANs), motivated by the boundedness, symmetry, and robustness properties of the Hellinger dis…