5 citations · 5 across the 8 of their papers we have counts for
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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…
cs.LG2026
Beyond Distribution Sharpening: The Importance of Task Rewards
Sarthak Mittal, Leo Gagnon, Guillaume Lajoie
Frontier models have demonstrated exceptional capabilities following the integration of task-reward-based reinforcement learning (RL) into their training pipelines, enabling system…
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…