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2020 – today
- 2024
[i13]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
- [c7]Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson:
Function-Space Regularization in Neural Networks: A Probabilistic Perspective. ICML 2023: 29275-29290 - [c6]Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson:
Should We Learn Most Likely Functions or Parameters? NeurIPS 2023 - [i12]Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson:
Should We Learn Most Likely Functions or Parameters? CoRR abs/2311.15990 (2023) - [i11]Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson:
Function-Space Regularization in Neural Networks: A Probabilistic Perspective. CoRR abs/2312.17162 (2023) - 2022
- [c5]Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson:
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. NeurIPS 2022 - [c4]Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson:
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization. NeurIPS 2022 - [c3]Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson:
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. NeurIPS 2022 - [i10]Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov
, Andrew Gordon Wilson:
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. CoRR abs/2203.16481 (2022) - [i9]Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson:
Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors. CoRR abs/2205.10279 (2022) - [i8]Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson:
PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization. CoRR abs/2211.13609 (2022) - 2021
- [c2]Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson:
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes. ICML 2021: 5279-5289 - [c1]Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui:
Variational Auto-Regressive Gaussian Processes for Continual Learning. ICML 2021: 5290-5300 - [i7]Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson:
SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes. CoRR abs/2106.06695 (2021) - [i6]Sanyam Kapoor, Valerio Perrone:
A Simple and Fast Baseline for Tuning Large XGBoost Models. CoRR abs/2111.06924 (2021) - [i5]Wesley J. Maddox, Sanyam Kapoor, Andrew Gordon Wilson:
When are Iterative Gaussian Processes Reliably Accurate? CoRR abs/2112.15246 (2021) - 2020
- [i4]Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui:
Variational Auto-Regressive Gaussian Processes for Continual Learning. CoRR abs/2006.05468 (2020)
2010 – 2019
- 2019
- [i3]Ted Moskovitz, Rui Wang, Janice Lan, Sanyam Kapoor, Thomas Miconi, Jason Yosinski, Aditya Rawal:
First-Order Preconditioning via Hypergradient Descent. CoRR abs/1910.08461 (2019) - 2018
- [i2]Cinjon Resnick, Roberta Raileanu, Sanyam Kapoor, Alex Peysakhovich, Kyunghyun Cho, Joan Bruna:
Backplay: "Man muss immer umkehren". CoRR abs/1807.06919 (2018) - [i1]Sanyam Kapoor:
Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches. CoRR abs/1807.09427 (2018)
Coauthor Index
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