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Nate Gruver

11 accepted papers

2026

Triangle Multiplication is All You Need for Biomolecular Structure Representations

Jeffrey Ouyang-Zhang, Pranav Murugan, Daniel Jesus Diaz, Gianluca Scarpellini, Richard Strong Bowen, Nate Gruver +4
ICLR 2026poster

AlphaFold has transformed protein structure prediction, but emerging applications such as virtual ligand screening, proteome-wide folding, and de novo binder design demand predictions at a massive scale, where runtime and memory costs become prohibitive. A major bottleneck lies in the Pairformer bac…

Cited by 0Source ↗code ↗Scholar ↗
2025

Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences

Alan Nawzad Amin, Nate Gruver, Yilun Kuang, Yucen Lily Li, Hunter Elliott, Calvin McCarter +3
ICLR 2025spotlight

To build effective therapeutics, biologists iteratively mutate antibody sequences to improve binding and stability. Proposed mutations can be informed by previous measurements or by learning from large antibody databases to predict only typical antibodies. Unfortunately, the space of typical antibod…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2025

Why Masking Diffusion Works: Condition on the Jump Schedule for Improved Discrete Diffusion

Alan Nawzad Amin, Nate Gruver, Andrew Gordon Wilson
NeurIPS 2025poster

Discrete diffusion models, like continuous diffusion models, generate high-quality samples by gradually undoing noise applied to datapoints with a Markov process. Gradual generation in theory comes with many conceptual benefits; for example, inductive biases can be incorporated into the noising Mark…

Cited by 0Source ↗code ↗Scholar ↗
2024

Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary Ward Ulissi
ICLR 2024poster

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 implement yet reliable, with around 90\% of sampled structures obeying physical constraints on atom positions and charges.…

Cited by 77Source ↗PDF ↗code ↗Scholar ↗
2024

Large Language Models Must Be Taught to Know What They Don’t Know

Sanyam Kapoor, Nate Gruver, Manley Roberts, Katherine M. Collins, Arka Pal, Umang Bhatt +4
NeurIPS 2024poster

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 sufficient to produce calibrated uncertainties, while others introduce sampling methods that can be prohibitively expensi…

Cited by 18Source ↗PDF ↗code ↗Scholar ↗
2023

Large Language Models Are Zero-Shot Time Series Forecasters

Nate Gruver, Marc Anton Finzi, Shikai Qiu, Andrew Gordon Wilson
NeurIPS 2023poster

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 language models (LLMs) such as GPT-3 and LLaMA-2 can surprisingly zero-shot extrapolate time series at a level comparable to or…

Cited by 438Source ↗PDF ↗code ↗Scholar ↗
2023

Protein Design with Guided Discrete Diffusion

Nate Gruver, Samuel Don Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hotzel, Julien Lafrance-Vanasse +3
NeurIPS 2023spotlight

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 the discriminative model guides a search for sequences with high fitness. Given its broad success in conditional sampling…

Cited by 130Source ↗PDF ↗code ↗Scholar ↗
2023

The Lie Derivative for Measuring Learned Equivariance

Nate Gruver, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson
ICLR 2023top-5%

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 translate or rotate accordingly. The success of convolutional neural networks has historically been tied to translation…

Cited by 44Source ↗PDF ↗code ↗Scholar ↗
2022

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders

Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside +1
ICML 2022spotlight

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-dimensional nature of the decision variables. We develop a new approach (LaMBO) which jointly trains a denoising autoencoder…

Cited by 113Source ↗PDF ↗code ↗Scholar ↗
2022

Deconstructing the Inductive Biases of Hamiltonian Neural Networks

Nate Gruver, Marc Anton Finzi, Samuel Don Stanton, Andrew Gordon Wilson
ICLR 2022spotlight

Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangian NNs, dramatically outperform other learned dynamics models by leveraging strong inductive biases. These models, however, are challenging to apply to many real world systems, such as those that don’t conserve energy or contain…

Cited by 46Source ↗PDF ↗code ↗Scholar ↗
2022

On Feature Learning in the Presence of Spurious Correlations

Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson
NeurIPS 2022accept

Deep classifiers are known to rely on spurious features — patterns which are correlated with the target on the training data but not inherently relevant to the learning problem, such as the image backgrounds when classifying the foregrounds. In this paper we evaluate the amount of information about…

Cited by 146Source ↗PDF ↗code ↗Scholar ↗