2 citations · 4 across the 16 of their papers we have counts for
16 papers · 1 filter
Regime-aware financial volatility forecasting via in-context learning
Saba Asaad, Shayan Mohajer Hamidi, Ali Bereyhi
This work introduces a regime-aware in-context learning framework that leverages large language models (LLMs) for financial volatility forecasting under nonstationary market condit…
Coupled Data and Measurement Space Dynamics for Enhanced Diffusion Posterior Sampling
Shayan Mohajer Hamidi, En-Hui Yang, Ben Liang
Inverse problems, where the goal is to recover an unknown signal from noisy or incomplete measurements, are central to applications in medical imaging, remote sensing, and computat…
Information-Guided Diffusion Sampling for Dataset Distillation
Linfeng Ye, Shayan Mohajer Hamidi, Guang Li +3
Dataset distillation aims to create a compact dataset that retains essential information while maintaining model performance. Diffusion models (DMs) have shown promise for this tas…
Towards Undistillable Models by Minimizing Conditional Mutual Information
Linfeng Ye, Shayan Mohajer Hamidi, En-hui Yang
A deep neural network (DNN) is said to be undistillable if, when used as a black-box input-output teacher, it cannot be distilled through knowledge distillation (KD). In this case,…
Distributed Quasi-Newton Method for Fair and Fast Federated Learning
Shayan Mohajer Hamidi, Linfeng Ye
Federated learning (FL) is a promising technology that enables edge devices/clients to collaboratively and iteratively train a machine learning model under the coordination of a ce…
Coded Deep Learning: Framework and Algorithm
En-hui Yang, Shayan Mohajer Hamidi
The success of deep learning (DL) is often achieved with large models and high complexity during both training and post-training inferences, hindering training in resource-limited…