3 papers
cs.LG2026
Sequential Causal Discovery with Noisy Language Model Priors
Prakhar Verma, David Arbour, Sunav Choudhary +3
Causal discovery from observational data typically assumes access to complete data and availability of perfect domain experts. In practice, data often arrive in batches, are subjec…
cs.LG2025
Relational Causal Discovery with Latent Confounders
Matteo Negro, Andrea Piras, Ragib Ahsan +2
Estimating causal effects from real-world relational data can be challenging when the underlying causal model and potential confounders are unknown. While several causal discovery…
cs.CL2025
Principled Content Selection to Generate Diverse and Personalized Multi-Document Summaries
Vishakh Padmakumar, Zichao Wang, David Arbour +1
While large language models (LLMs) are increasingly capable of handling longer contexts, recent work has demonstrated that they exhibit the "lost in the middle" phenomenon (Liu et…