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Payam Barnaghi

2 accepted papers

2025

A Closer Look at Transformers for Time Series Forecasting: Understanding Why They Work and Where They Struggle

ICML 2025poster

Time-series forecasting is crucial across various domains, including finance, healthcare, and energy. Transformer models, originally developed for natural language processing, have demonstrated significant potential in addressing challenges associated with time-series data. These models utilize diff…

Cited by 0SourcePDFScholar
2025

AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling

ICML 2025poster

Large language models (LLMs) acquire a breadth of information across various domains. However, their computational complexity, cost, and lack of transparency often hinder their direct application for predictive tasks where privacy and interpretability are paramount. In fields such as healthcare, bio…