6 papers
Geometry-Adaptive Mechanisms for Private Synthetic Data
Raoof Zare Moayedi, Amir R. Asadi, Mohammad Hossein Yassaee +1
Generating differentially private synthetic data with meaningful Wasserstein utility guarantees is challenging in high dimensions. For datasets of size \(n\) on with $d\g…
Exact Recovery in the Data Block Model
Amir R. Asadi, Akbar Davoodi, Ramin Javadi +1
Community detection in networks is a fundamental problem in machine learning and statistical inference, with applications in social networks, biological systems, and communication…
Hierarchical Maximum Entropy via the Renormalization Group
Amir R. Asadi
Hierarchical structures, which include multiple levels, are prevalent in statistical and machine-learning models as well as physical systems. Extending the foundational result that…
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…
KL-Regularized RLHF with Multiple Reference Models: Exact Solutions and Sample Complexity
Gholamali Aminian, Amir R. Asadi, Idan Shenfeld +1
Recent methods for aligning large language models (LLMs) with human feedback predominantly rely on a single reference model, which limits diversity, model overfitting, and underuti…
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{…