Sanyam Kapoor

According to our database, Sanyam Kapoor authored at least 16 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

When Should We Orchestrate Multiple Agents?

Umang Bhatt, Sanyam Kapoor, Mihir Upadhyay, Ilia Sucholutsky, Francesco Quinzan, Katherine M. Collins, Adrian Weller, Andrew Gordon Wilson, Muhammad Bilal Zafar

CoRR, March, 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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

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

Sanyam Kapoor, Nate Gruver, Manley Roberts, Katie Collins, Arka Pal, Umang Bhatt, Adrian Weller, Samuel Dooley, Micah Goldblum, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Uncertainty Quantification for Conditional Treatment Effect Estimation under Dynamic Treatment Regimes.

Leon Deng, Hong Xiong, Feng Wu, Sanyam Kapoor, Soumya Gosh, Zach Shahn, Li-Wei H. Lehman

Proceedings of the Machine Learning for Health, 2024

2023

Should We Learn Most Likely Functions or Parameters?

Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Function-Space Regularization in Neural Networks: A Probabilistic Perspective.

Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson

Proceedings of the International Conference on Machine Learning, 2023

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 Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization.

Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification.

Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

When are Iterative Gaussian Processes Reliably Accurate?

Wesley J. Maddox, Sanyam Kapoor, Andrew Gordon Wilson

CoRR, 2021

A Simple and Fast Baseline for Tuning Large XGBoost Models.

Sanyam Kapoor, Valerio Perrone

CoRR, 2021

Variational Auto-Regressive Gaussian Processes for Continual Learning.

Sanyam Kapoor, Theofanis Karaletsos, Thang D. Bui

Proceedings of the 38th International Conference on Machine Learning, 2021

SKIing on Simplices: Kernel Interpolation on the Permutohedral Lattice for Scalable Gaussian Processes.

Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson

Proceedings of the 38th International Conference on Machine Learning, 2021

2019

First-Order Preconditioning via Hypergradient Descent.

Ted Moskovitz, Rui Wang, Janice Lan, Sanyam Kapoor, Thomas Miconi, Jason Yosinski, Aditya Rawal

CoRR, 2019

2018

Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches.

Sanyam Kapoor

CoRR, 2018

Backplay: "Man muss immer umkehren".

Cinjon Resnick, Roberta Raileanu, Sanyam Kapoor, Alex Peysakhovich, Kyunghyun Cho, Joan Bruna

CoRR, 2018