4 papers
LLMs can construct powerful representations and streamline sample-efficient supervised learning
Ilker Demirel, Lawrence Shi, Zeshan Hussain +1
As real-world datasets become more complex and heterogeneous, supervised learning is often bottlenecked by input representation design. Modeling multimodal data, such as time-serie…
Uncovering Bias Mechanisms in Observational Studies
Ilker Demirel, Zeshan Hussain, Piersilvio De Bartolomeis +1
Observational studies are a key resource for causal inference but are often affected by systematic biases. Prior work has focused mainly on detecting these biases, via sensitivity…
Evaluating Physician-AI Interaction for Cancer Management: Paving the Path towards Precision Oncology
Zeshan Hussain, Barbara D. Lam, Fernando A. Acosta-Perez +4
As machine learning (ML)-based decision support tools proliferate in clinical practice, understanding how clinicians integrate personalized ML predictions alongside randomized cont…
Benchmarking Observational Studies with Experimental Data under Right-Censoring
Ilker Demirel, Edward De Brouwer, Zeshan Hussain +3
Drawing causal inferences from observational studies (OS) requires unverifiable validity assumptions; however, one can falsify those assumptions by benchmarking the OS with experim…