193 citations · 193 across the 9 of their papers we have counts for
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cs.LG2026
Can In-Context Learning Support Intrinsic Curiosity?
Eric Elmoznino, Sangnie Bhardwaj, Johannes von Oswald +5
Effective machine learning depends not only on how we model data, but also on what data we choose to collect. While large sequence models have revolutionized data modeling, the pro…
cs.CL2026
Simplifying the Modeling of Arbitrary Conditionals in Natural Language
Yinhan Lu, Eric Elmoznino, Léo Gagnon +3
Causal Transformers model sequences through an autoregressive factorization of the joint distribution, which enables efficient left-to-right decoding and conditional likelihood com…
stat.ME2026
Causal Network Discovery from Interventional Count Data with Latent Linear DAGs
Yijiao Zhang, Hongzhe Li
The increasing availability of interventional data offers new opportunities for causal discovery, with gene perturbation studies providing a prominent example. Such data are typica…