Backplay: "Man muss immer umkehren". - Cinjon Resnick; Roberta Raileanu; Sanyam Kapoor; Alex Peysakhovich; Kyunghyun Cho; Joan Bruna (CoRR, 2018)

Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches. - Sanyam Kapoor (CoRR, 2018)

First-Order Preconditioning via Hypergradient Descent. - Ted Moskovitz; Rui Wang 052; Janice Lan; Sanyam Kapoor; Thomas Miconi; Jason Yosinski; Aditya Rawal (CoRR, 2019)

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

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

A Simple and Fast Baseline for Tuning Large XGBoost Models. - Sanyam Kapoor; Valerio Perrone (CoRR, 2021)

When are Iterative Gaussian Processes Reliably Accurate? - Wesley J. Maddox; Sanyam Kapoor; Andrew Gordon Wilson (CoRR, 2021)

On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. - Sanyam Kapoor; Wesley J. Maddox; Pavel Izmailov; Andrew Gordon 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 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 001; Yann LeCun; Andrew Gordon Wilson (NeurIPS, 2022)

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

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

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 (ML4H@NeurIPS, 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 (NeurIPS, 2024)

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 (ICLR, 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, 2025)