10 citations · 20 across the 5 of their papers we have counts for
5 papers
Neural Nonmyopic Bayesian Optimization in Dynamic Cost Settings
Sang T. Truong, Duc Q. Nguyen, Willie Neiswanger +4
Bayesian optimization (BO) is a common framework for optimizing black-box functions, yet most existing methods assume static query costs and rely on myopic acquisition strategies.…
Training a Scientific Reasoning Model for Chemistry
Siddharth M. Narayanan, James D. Braza, Ryan-Rhys Griffiths +6
Reasoning models are large language models that emit a long chain-of-thought before answering, providing both higher accuracy and explicit reasoning for their response. A major que…
Aviary: training language agents on challenging scientific tasks
Siddharth Narayanan, James D. Braza, Ryan-Rhys Griffiths +8
Solving complex real-world tasks requires cycles of actions and observations. This is particularly true in science, where tasks require many cycles of analysis, tool use, and exper…
Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning
Simon Frieder, Jonas Bayer, Sam Looi +13
The datasets and benchmarks commonly used to train and evaluate the mathematical capabilities of AI-based mathematical copilots (primarily large language models) exhibit several sh…
RealMedQA: A pilot biomedical question answering dataset containing realistic clinical questions
Gregory Kell, Angus Roberts, Serge Umansky +8
Clinical question answering systems have the potential to provide clinicians with relevant and timely answers to their questions. Nonetheless, despite the advances that have been m…