5 citations · 5 across the 3 of their papers we have counts for
3 papers
eess.SY2026
Flow-Corrected Thompson Sampling for Non-Stationary Contextual Bandits
Ali Baheri
We study non-stationary linear contextual bandits where the reward model drifts over time, rendering classical contextual bandit algorithms brittle because historical data becomes…
cs.LG2024★ 5 cited
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal +141
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hyb…
cs.LG2024
In-Context Learning of Physical Properties: Few-Shot Adaptation to Out-of-Distribution Molecular Graphs
Grzegorz Kaszuba, Amirhossein D. Naghdi, Dario Massa +3
Large language models manifest the ability of few-shot adaptation to a sequence of provided examples. This behavior, known as in-context learning, allows for performing nontrivial…