Kapoor, Sanyam

10 publications

ICLR 2025 Compute-Optimal LLMs Provably Generalize Better with Scale Marc Anton Finzi, Sanyam Kapoor, Diego Granziol, Anming Gu, Christopher De Sa, J Zico Kolter, Andrew Gordon Wilson

NeurIPS 2024 Large Language Models Must Be Taught to Know What They Don’t Know Sanyam Kapoor, Nate Gruver, Manley Roberts, Katherine Collins, Arka Pal, Umang Bhatt, Adrian Weller, Samuel Dooley, Micah Goldblum, Andrew Gordon Wilson

ICML 2023 Function-Space Regularization in Neural Networks: A Probabilistic Perspective Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson

NeurIPS 2023 Should We Learn Most Likely Functions or Parameters? Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew G Wilson

NeurIPS 2022 On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification Sanyam Kapoor, Wesley J Maddox, Pavel Izmailov, Andrew G Wilson

NeurIPS 2022 PAC-Bayes Compression Bounds so Tight That They Can Explain Generalization Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew G Wilson

ICMLW 2022 Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Prior Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew Gordon Wilson

NeurIPS 2022 Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann LeCun, Andrew G Wilson

ICML 2021 SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Gordon Wilson

ICML 2021 Variational Auto-Regressive Gaussian Processes for Continual Learning Sanyam Kapoor, Theofanis Karaletsos, Thang D Bui