Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes. - Taylor W. Killian; Samuel Daulton; Finale Doshi-Velez; George Dimitri Konidaris (NIPS, 2017)

BoTorch: Programmable Bayesian Optimization in PyTorch. - Maximilian Balandat; Brian Karrer; Daniel R. Jiang; Samuel Daulton; Benjamin Letham; Andrew Gordon Wilson; Eytan Bakshy (CoRR, 2019)

Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints. - Samuel Daulton; Shaun Singh; Vashist Avadhanula; Drew Dimmery; Eytan Bakshy (CoRR, 2019)

BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. - Maximilian Balandat; Brian Karrer; Daniel R. Jiang; Samuel Daulton; Benjamin Letham; Andrew Gordon Wilson; Eytan Bakshy (NeurIPS, 2020)

Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. - Samuel Daulton; Maximilian Balandat; Eytan Bakshy (NeurIPS, 2020)

Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning. - Hongseok Namkoong; Samuel Daulton; Eytan Bakshy (CoRR, 2020)

Optimizing Coverage and Capacity in Cellular Networks using Machine Learning. - Ryan M. Dreifuerst; Samuel Daulton; Yuchen Qian; Paul Parayil Varkey; Maximilian Balandat; Sanjay Kasturia; Anoop Tomar; Ali Yazdan 001; Vish Ponnampalam; Robert W. Heath Jr. (ICASSP, 2021)

Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. - Samuel Daulton; Maximilian Balandat; Eytan Bakshy (NeurIPS, 2021)

Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization. - David Eriksson; Pierce I-Jen Chuang; Samuel Daulton; Peng Xia; Akshat Shrivastava; Arun Babu; Shicong Zhao; Ahmed Aly; Ganesh Venkatesh; Maximilian Balandat (CoRR, 2021)

Robust Multi-Objective Bayesian Optimization Under Input Noise. - Samuel Daulton; Sait Cakmak; Maximilian Balandat; Michael A. Osborne; Enlu Zhou; Eytan Bakshy (ICML, 2022)

Log-Linear-Time Gaussian Processes Using Binary Tree Kernels. - Michael K. Cohen; Samuel Daulton; Michael A. Osborne (NeurIPS, 2022)

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. - Samuel Daulton; Xingchen Wan; David Eriksson; Maximilian Balandat; Michael A. Osborne; Eytan Bakshy (NeurIPS, 2022)

Multi-objective Bayesian optimization over high-dimensional search spaces. - Samuel Daulton; David Eriksson; Maximilian Balandat; Eytan Bakshy (UAI, 2022)

Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information. - Samuel Daulton; Maximilian Balandat; Eytan Bakshy (ICML, 2023)

Unexpected Improvements to Expected Improvement for Bayesian Optimization. - Sebastian Ament; Samuel Daulton; David Eriksson; Maximilian Balandat; Eytan Bakshy (NeurIPS, 2023)

Bayesian Optimization of Function Networks with Partial Evaluations. - Poompol Buathong; Jiayue Wan; Raul Astudillo; Samuel Daulton; Maximilian Balandat; Peter I. Frazier (ICML, 2024)

Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments. - Qing Feng; Samuel Daulton; Benjamin Letham; Maximilian Balandat; Eytan Bakshy (KDD (1), 2025)