828 citations · 5k across the 89 of their papers we have counts for
30 papers · 1 filter
Sample, estimate, aggregate: A recipe for causal discovery foundation models
Menghua Wu, Yujia Bao, Regina Barzilay +1
Causal discovery, the task of inferring causal structure from data, has the potential to uncover mechanistic insights from biological experiments, especially those involving pertur…
Risk-Controlling Model Selection via Guided Bayesian Optimization
Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay +1
Adjustable hyperparameters of machine learning models typically impact various key trade-offs such as accuracy, fairness, robustness, or inference cost. Our goal in this paper is t…
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models
Gabriele Corso, Yilun Xu, Valentin de Bortoli +2
In light of the widespread success of generative models, a significant amount of research has gone into speeding up their sampling time. However, generative models are often sample…
Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design
Hannes Stärk, Bowen Jing, Regina Barzilay +1
A significant amount of protein function requires binding small molecules, including enzymatic catalysis. As such, designing binding pockets for small molecules has several impactf…
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang, Limei Wang, Jacob Helwig +60
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences. Today, AI has started to advance natural sciences by improving, accelerating…
PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels
Homa Esfahanizadeh, Adam Yala, Rafael G. L. D'Oliveira +8
Allowing organizations to share their data for training of machine learning (ML) models without unintended information leakage is an open problem in practice. A promising technique…