3 papers
cs.LG2025
Does TabPFN Understand Causal Structures?
Omar Swelam, Lennart Purucker, Jake Robertson +3
Causal discovery is fundamental for multiple scientific domains, yet extracting causal information from real world data remains a significant challenge. Given the recent success on…
cs.CL2025
Tuning LLM Judge Design Decisions for 1/1000 of the Cost
David Salinas, Omar Swelam, Frank Hutter
Evaluating Large Language Models (LLMs) often requires costly human annotations. To address this, LLM-based judges have been proposed, which compare the outputs of two LLMs enablin…
cs.LG2024
Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models
Sathya Kamesh Bhethanabhotla, Omar Swelam, Julien Siems +2
This paper introduces Mamba4Cast, a zero-shot foundation model for time series forecasting. Based on the Mamba architecture and inspired by Prior-data Fitted Networks (PFNs), Mamba…