24 papers
PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling
Yuche Gao, José Miguel Hernández-Lobato, Siyuan Guo
Predicting cellular responses to unseen chemical perturbations is challenging due to unknown targets and mechanisms, high-dimensional expression responses, and limited experimental…
Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
RuiKang OuYang, Hanlin Yu, Xinyue Ai +7
Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, t…
Towards Diverse Scientific Hypothesis Search with Large Language Models
Haorui Wang, Parshin Shojaee, Kazem Meidani +7
Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses. Yet in many disc…
Free energy Estimation on Any State Space
Jiajun He, Zijing Ou, Francisco Vargas +4
Free energy estimation is a fundamental yet challenging problem, from physics to statistics. Classical approaches rely on thermodynamic transformations, ranging from direct estimat…
BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching
RuiKang OuYang, Bo Qiang, José Miguel Hernández-Lobato
Developing an efficient sampler capable of generating independent and identically distributed (IID) samples from a Boltzmann distribution is a crucial challenge in scientific resea…
Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research
VÃctor Sabanza-Gil, Riccardo Barbano, Daniel Pacheco Gutiérrez +4
Multi-fidelity Bayesian Optimization (MFBO) is a promising framework to speed up materials and molecular discovery as sources of information of different accuracies are at hand at…