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stat.ML2026
Is In-Context Reinforcement Learning A Posterior Sampler? An Empirical Study
Weiqin Chen, Xinlong Du, Gesualdo Scutari +1
In-context reinforcement learning (ICRL) exploits the impressive in-context learning ability of large language models to zero-shot generalize to new, unseen tasks at inference time…
astro-ph.EP2026
Narrow-Shell Stochasticity in Source-Sink Models of the Low Earth Orbit Environment
Jaewon Choi, Souvik Dhara, Harsha Honnappa
Deterministic source-sink models are widely used to assess the long-term evolution, capacity, and sustainability of the low Earth orbit (LEO) environment. These models propagate sh…
cs.LG2026
Neural Diffusion Intensity Models for Point Process Data
Xinlong Du, Harsha Honnappa, Vinayak Rao
Cox processes model overdispersed point process data via a latent stochastic intensity, but both nonparametric estimation of the intensity model and posterior inference over intens…