12 papers
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…
-FUM: Federated Unlearning via min--max and -divergence
Radmehr Karimian, Amirhossein Bagheri, Meghdad Kurmanji +2
Federated Learning (FL) has emerged as a powerful paradigm for collaborative machine learning across decentralized data sources, preserving privacy by keeping data local. However,…
ReDiF: Reinforced Distillation for Few Step Diffusion
Amirhossein Tighkhorshid, Zahra Dehghanian, Gholamali Aminian +2
Distillation addresses the slow sampling problem in diffusion models by creating models with smaller size or fewer steps that approximate the behavior of high-step teachers. In thi…
FraudTransformer: Time-Aware GPT for Transaction Fraud Detection
Gholamali Aminian, Andrew Elliott, Tiger Li +8
Detecting payment fraud in real-world banking streams requires models that can exploit both the order of events and the irregular time gaps between them. We introduce FraudTransfor…
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…
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…