5 citations · 7 across the 6 of their papers we have counts for
6 papers
LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet?
Rushil Gupta, Jason Hartford, Bang Liu
Large language models (LLMs) have recently been proposed as general-purpose agents for experimental design, with claims that they can perform in-context experimental design. We eva…
Virtual Cells: Predict, Explain, Discover
Emmanuel Noutahi, Jason Hartford, Prudencio Tossou +12
Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simu…
Efficient Biological Data Acquisition through Inference Set Design
Ihor Neporozhnii, Julien Roy, Emmanuel Bengio +1
In drug discovery, highly automated high-throughput laboratories are used to screen a large number of compounds in search of effective drugs. These experiments are expensive, so on…
Automated Discovery of Pairwise Interactions from Unstructured Data
Zuheng, Xu, Moksh Jain +5
Pairwise interactions between perturbations to a system can provide evidence for the causal dependencies of the underlying underlying mechanisms of a system. When observations are…
Targeted Sequential Indirect Experiment Design
Elisabeth Ailer, Niclas Dern, Jason Hartford +1
Scientific hypotheses typically concern specific aspects of complex, imperfectly understood or entirely unknown mechanisms, such as the effect of gene expression levels on phenotyp…
Propensity Score Alignment of Unpaired Multimodal Data
Johnny Xi, Jana Osea, Zuheng Xu +1
Multimodal representation learning techniques typically rely on paired samples to learn common representations, but paired samples are challenging to collect in fields such as biol…