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Nate Gruver
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2020 – today
- 2024
- [c10]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 - [i11]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 abs/2402.04379 (2024) - [i10]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 abs/2406.08391 (2024) - 2023
- [c9]Nate Gruver, Marc Anton Finzi, Micah Goldblum, Andrew Gordon Wilson:
The Lie Derivative for Measuring Learned Equivariance. ICLR 2023 - [c8]Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson:
Large Language Models Are Zero-Shot Time Series Forecasters. NeurIPS 2023 - [c7]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 - [i9]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 abs/2305.20009 (2023) - [i8]Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson:
Large Language Models Are Zero-Shot Time Series Forecasters. CoRR abs/2310.07820 (2023) - 2022
- [c6]Nate Gruver, Marc Anton Finzi, Samuel Don Stanton, Andrew Gordon Wilson:
Deconstructing the Inductive Biases of Hamiltonian Neural Networks. ICLR 2022 - [c5]Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip M. Maffettone
, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson:
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. ICML 2022: 20459-20478 - [c4]Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson:
On Feature Learning in the Presence of Spurious Correlations. NeurIPS 2022 - [i7]Nate Gruver, Marc Finzi, Samuel Stanton, Andrew Gordon Wilson:
Deconstructing the Inductive Biases of Hamiltonian Neural Networks. CoRR abs/2202.04836 (2022) - [i6]Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip M. Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson:
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. CoRR abs/2203.12742 (2022) - [i5]Nate Gruver, Marc Finzi, Micah Goldblum, Andrew Gordon Wilson:
The Lie Derivative for Measuring Learned Equivariance. CoRR abs/2210.02984 (2022) - [i4]Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson:
On Feature Learning in the Presence of Spurious Correlations. CoRR abs/2210.11369 (2022) - 2020
- [c3]Shushman Choudhury, Nate Gruver, Mykel J. Kochenderfer:
Adaptive Informative Path Planning with Multimodal Sensing. ICAPS 2020: 57-65 - [c2]Nate Gruver, Jiaming Song, Mykel J. Kochenderfer, Stefano Ermon:
Multi-agent Adversarial Inverse Reinforcement Learning with Latent Variables. AAMAS 2020: 1855-1857 - [i3]Shushman Choudhury, Nate Gruver, Mykel J. Kochenderfer:
Adaptive Informative Path Planning with Multimodal Sensing. CoRR abs/2003.09746 (2020)
2010 – 2019
- 2019
- [c1]Nate Gruver, Ali Malik, Brahm Capoor, Chris Piech, Mitchell L. Stevens, Andreas Paepcke:
Using Latent Variable Models to Observe Academic Pathways. EDM 2019 - [i2]Nate Gruver, Ali Malik, Brahm Capoor, Chris Piech, Mitchell L. Stevens, Andreas Paepcke:
Using Latent Variable Models to Observe Academic Pathways. CoRR abs/1905.13383 (2019) - 2018
- [i1]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 abs/1807.00082 (2018)
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