2 citations · 18 across the 31 of their papers we have counts for
8 papers · 1 filter
On the Generalization and Robustness in Conditional Value-at-Risk
Dinesh Karthik Mulumudi, Piyushi Manupriya, Gholamali Aminian +1
Conditional Value-at-Risk (CVaR) is a widely used risk-sensitive objective for learning under rare but high-impact losses, yet its statistical behavior under heavy-tailed data rema…
Best-of-N through the Smoothing Lens: KL Divergence and Regret Analysis
Gholamali Aminian, Idan Shenfeld, Amir R. Asadi +2
A simple yet effective method for inference-time alignment of generative models is Best-of- (BoN), where outcomes are sampled from a reference policy, evaluated using a prox…
Generalization Error of -Divergence Stabilized Algorithms via Duality
Francisco Daunas, Iñaki Esnaola, Samir M. Perlaza +1
The solution to empirical risk minimization with -divergence regularization (ERM-DR) is extended to constrained optimization problems, establishing conditions for equivalence…
Private Synthetic Graph Generation and Fused Gromov-Wasserstein Distance
Leoni Carla Wirth, Gholamali Aminian, Gesine Reinert
Networks are popular for representing complex data. In particular, differentially private synthetic networks are much in demand for method and algorithm development. The network ge…
Understanding Transfer Learning via Mean-field Analysis
Gholamali Aminian, Łukasz Szpruch, Samuel N. Cohen
We propose a novel framework for exploring generalization errors of transfer learning through the lens of differential calculus on the space of probability measures. In particular,…
Generalization and Robustness of the Tilted Empirical Risk
Gholamali Aminian, Amir R. Asadi, Tian Li +3
The generalization error (risk) of a supervised statistical learning algorithm quantifies its prediction ability on previously unseen data. Inspired by exponential tilting, \citet{…
Generalization Error of Graph Neural Networks in the Mean-field Regime
Gholamali Aminian, Yixuan He, Gesine Reinert +2
This work provides a theoretical framework for assessing the generalization error of graph neural networks in the over-parameterized regime, where the number of parameters surpasse…
Mean-field Analysis of Generalization Errors
Gholamali Aminian, Samuel N. Cohen, Łukasz Szpruch
We propose a novel framework for exploring weak and generalization errors of algorithms through the lens of differential calculus on the space of probability measures. Specif…