8 papers
Can Tabular In-Context Learners Generalize to Biomolecular Property Prediction?
Davy Guan, Lu Zhang, Asiri Wijesinghe +7
Predicting biomolecular properties from limited labeled data is a central bottleneck in protein engineering and small-molecule design. As strong pretrained encoders now supply rich…
Causal Preference Elicitation
Edwin V. Bonilla, He Zhao, Daniel M. Steinberg
We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relations to concentrate a posterior over di…
Flowette: Flow Matching with Graphette Priors for Graph Generation
Asiri Wijesinghe, Sevvandi Kandanaarachchi, Daniel M. Steinberg +1
We study generative modeling of graphs with recurring subgraph motifs. We propose Flowette, a continuous flow matching framework that employs a graph neural network-based transform…
Generative Bayesian Optimization: Generative Models as Acquisition Functions
Rafael Oliveira, Daniel M. Steinberg, Edwin V. Bonilla
We present a general strategy for turning generative models into candidate solution samplers for batch Bayesian optimization (BO). The use of generative models for BO enables large…
Arrow: A Foundation Model for Causal Discovery
Ryan Thompson, He Zhao, Daniel M. Steinberg +1
We introduce Arrow, a foundation model for zero-shot causal discovery on observational tabular data. Arrow factorizes a directed acyclic graph into an undirected skeleton and a top…
Active Flow Matching
Yashvir S. Grewal, Daniel M. Steinberg, Thang D. Bui +2
Discrete diffusion and flow matching models capture complex, non-additive and non-autoregressive structure in high-dimensional objective landscapes through parallel, iterative refi…