Nate Gruver

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 21

Top Topics

Language Modelling

Top Venues

Publications

Sanyam Kapoor, Nate Gruver, Manley Roberts, Katherine M. Collins, Arka Pal, Umang Bhatt, Adrian Weller, Samuel Dooley, Micah Goldblum, Andrew Gordon Wilson
Large Language Models Must Be Taught to Know What They Don't Know. CoRR (2024)
Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary W. Ulissi
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text. ICLR (2024)
Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary W. Ulissi
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text. CoRR (2024)
Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson
Protein Design with Guided Discrete Diffusion. NeurIPS (2023)
Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hötzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson
Protein Design with Guided Discrete Diffusion. CoRR (2023)
Nate Gruver, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson
The Lie Derivative for Measuring Learned Equivariance. ICLR (2023)
Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson
Large Language Models Are Zero-Shot Time Series Forecasters. NeurIPS (2023)
Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson
Large Language Models Are Zero-Shot Time Series Forecasters. CoRR (2023)
Nate Gruver, Marc Anton Finzi, Samuel Don Stanton, Andrew Gordon Wilson
Deconstructing the Inductive Biases of Hamiltonian Neural Networks. ICLR (2022)
Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson
On Feature Learning in the Presence of Spurious Correlations. CoRR (2022)
Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. ICML (2022)
Nate Gruver, Marc Finzi, Micah Goldblum, Andrew Gordon Wilson
The Lie Derivative for Measuring Learned Equivariance. CoRR (2022)
Nate Gruver, Marc Finzi, Samuel Stanton, Andrew Gordon Wilson
Deconstructing the Inductive Biases of Hamiltonian Neural Networks. CoRR (2022)
Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson
On Feature Learning in the Presence of Spurious Correlations. NeurIPS (2022)
Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. CoRR (2022)
Shushman Choudhury, Nate Gruver, Mykel J. Kochenderfer
Adaptive Informative Path Planning with Multimodal Sensing. CoRR (2020)
Shushman Choudhury, Nate Gruver, Mykel J. Kochenderfer
Adaptive Informative Path Planning with Multimodal Sensing. ICAPS (2020)
Nate Gruver, Jiaming Song, Mykel J. Kochenderfer, Stefano Ermon
Multi-agent Adversarial Inverse Reinforcement Learning with Latent Variables. AAMAS (2020)
Nate Gruver, Ali Malik, Brahm Capoor, Chris Piech, Mitchell L. Stevens, Andreas Paepcke
Using Latent Variable Models to Observe Academic Pathways. EDM (2019)
Nate Gruver, Ali Malik, Brahm Capoor, Chris Piech, Mitchell L. Stevens, Andreas Paepcke
Using Latent Variable Models to Observe Academic Pathways. CoRR (2019)
Thomas Dean, Maurice Chiang, Marcus Gomez, Nate Gruver, Yousef Hindy, Michelle Lam, Peter Lu, Sophia Sanchez, Rohun Saxena, Michael Smith, Lucy Wang, Catherine Wong
Amanuensis: The Programmer's Apprentice. CoRR (2018)