96 citations · 223 across the 11 of their papers we have counts for
12 papers
On the Design Space of Discrete Diffusion Online Adaptation for Molecular Optimization
Trevor Chen, Ariel Dai, Jason Yang +8
Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures. At test time, however, the goal is not to sample…
Why Masking Diffusion Works: Condition on the Jump Schedule for Improved Discrete Diffusion
Alan N. Amin, Nate Gruver, Andrew Gordon Wilson
Discrete diffusion models, like continuous diffusion models, generate high-quality samples by gradually undoing noise applied to datapoints with a Markov process. Gradual generatio…
Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences
Alan Nawzad Amin, Nate Gruver, Yilun Kuang +6
To build effective therapeutics, biologists iteratively mutate antibody sequences to improve binding and stability. Proposed mutations can be informed by previous measurements or b…
Large Language Models Must Be Taught to Know What They Don't Know
Sanyam Kapoor, Nate Gruver, Manley Roberts +7
When using large language models (LLMs) in high-stakes applications, we need to know when we can trust their predictions. Some works argue that prompting high-performance LLMs is s…
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
Nate Gruver, Anuroop Sriram, Andrea Madotto +3
We propose fine-tuning large language models for generation of stable materials. While unorthodox, fine-tuning large language models on text-encoded atomistic data is simple to imp…
Large Language Models Are Zero-Shot Time Series Forecasters
Nate Gruver, Marc Finzi, Shikai Qiu +1
By encoding time series as a string of numerical digits, we can frame time series forecasting as next-token prediction in text. Developing this approach, we find that large languag…
Protein Design with Guided Discrete Diffusion
Nate Gruver, Samuel Stanton, Nathan C. Frey +6
A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling. The generative model samples plausible sequences while t…
On Feature Learning in the Presence of Spurious Correlations
Pavel Izmailov, Polina Kirichenko, Nate Gruver +1
Deep classifiers are known to rely on spurious features $\unicode{x2013}$ patterns which are correlated with the target on the training data but not inherently relevant to the lear…
The Lie Derivative for Measuring Learned Equivariance
Nate Gruver, Marc Finzi, Micah Goldblum +1
Equivariance guarantees that a model's predictions capture key symmetries in data. When an image is translated or rotated, an equivariant model's representation of that image will…
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders
Samuel Stanton, Wesley Maddox, Nate Gruver +4
Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous optimization. However, its adoption for drug design has been hindered by the discrete, high-dimen…
Deconstructing the Inductive Biases of Hamiltonian Neural Networks
Nate Gruver, Marc Finzi, Samuel Stanton +1
Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangian NNs, dramatically outperform other learned dynamics models by leveraging strong inductive biases. These mo…
Using Latent Variable Models to Observe Academic Pathways
Nate Gruver, Ali Malik, Brahm Capoor +3
Understanding large-scale patterns in student course enrollment is a problem of great interest to university administrators and educational researchers. Yet important decisions are…