Samuel Daulton

32 papers A* 9A 1Misc 1Journal 17Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	BONSAI: Bayesian Optimization with Natural Simplicity and Interpretability. CoRR Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2026	—	`conf`	Case Study: Learning Robust, Long-run Surrogate Metrics with Modeling and Instrumental Variables. KDD (1) Daniel Day, Matthew Dalton, Samuel Daulton, Benjamin Letham, Xiao Cai, Aojia Zhao, Eytan Bakshy
2026	J	`jnl`	Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning. Trans. Mach. Learn. Res. Hongseok Namkoong, Samuel Daulton, Eytan Bakshy
2025	—	`conf`	Ax: A Platform for Adaptive Experimentation. AutoML Miles Olson, Elizabeth Santorella, Louis C. Tiao, Sait Cakmak, Mia Garrard, Samuel Daulton, Zhiyuan Jerry Lin, Sebastian Ament, Bernard Beckerman, Eric Onofrey, Paschal Igusti, Cristian Lara, Benjamin Letham, Cesar Cardoso, Shiyun Sunny Shen, Andy Chenyuan Lin, Matthew Grange, Elena Kashtelyan, David Eriksson, Maximilian Balandat, Eytan Bakshy
2025	—	`conf`	Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments. KDD (1) Qing Feng, Samuel Daulton, Benjamin Letham, Maximilian Balandat, Eytan Bakshy
2025	J	`jnl`	Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments. CoRR Qing Feng, Samuel Daulton, Benjamin Letham, Maximilian Balandat, Eytan Bakshy
2024	A*	`conf`	Bayesian Optimization of Function Networks with Partial Evaluations. ICML Poompol Buathong, Jiayue Wan, Raul Astudillo, Samuel Daulton, Maximilian Balandat, Peter I. Frazier
2023	J	`jnl`	Bayesian Optimization of Function Networks with Partial Evaluations. CoRR Poompol Buathong, Jiayue Wan, Samuel Daulton, Raul Astudillo, Maximilian Balandat, Peter I. Frazier
2023	A*	`conf`	Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information. ICML Samuel Daulton, Maximilian Balandat, Eytan Bakshy
2023	A*	`conf`	Unexpected Improvements to Expected Improvement for Bayesian Optimization. NeurIPS Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2023	J	`jnl`	Unexpected Improvements to Expected Improvement for Bayesian Optimization. CoRR Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2022	A*	`conf`	Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. NeurIPS Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy
2022	J	`jnl`	Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. CoRR Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy
2022	A*	`conf`	Log-Linear-Time Gaussian Processes Using Binary Tree Kernels. NeurIPS Michael K. Cohen, Samuel Daulton, Michael A. Osborne
2022	J	`jnl`	Log-Linear-Time Gaussian Processes Using Binary Tree Kernels. CoRR Michael K. Cohen, Samuel Daulton, Michael A. Osborne
2022	A	`conf`	Multi-objective Bayesian optimization over high-dimensional search spaces. UAI Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2022	A*	`conf`	Robust Multi-Objective Bayesian Optimization Under Input Noise. ICML Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy
2022	J	`jnl`	Robust Multi-Objective Bayesian Optimization Under Input Noise. CoRR Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy
2021	J	`jnl`	Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization. CoRR David Eriksson, Pierce I-Jen Chuang, Samuel Daulton, Peng Xia, Akshat Shrivastava, Arun Babu, Shicong Zhao, Ahmed Aly, Ganesh Venkatesh, Maximilian Balandat
2021	J	`jnl`	Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces. CoRR Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2021	Misc	`conf`	Optimizing Coverage and Capacity in Cellular Networks using Machine Learning. ICASSP Ryan M. Dreifuerst, Samuel Daulton, Yuchen Qian, Paul Parayil Varkey, Maximilian Balandat, Sanjay Kasturia, Anoop Tomar, Ali Yazdan, Vish Ponnampalam, Robert W. Heath Jr.
2021	A*	`conf`	Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. NeurIPS Samuel Daulton, Maximilian Balandat, Eytan Bakshy
2021	J	`jnl`	Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. CoRR Samuel Daulton, Maximilian Balandat, Eytan Bakshy
2020	A*	`conf`	BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. NeurIPS Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy
2020	A*	`conf`	Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. NeurIPS Samuel Daulton, Maximilian Balandat, Eytan Bakshy
2020	J	`jnl`	Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. CoRR Samuel Daulton, Maximilian Balandat, Eytan Bakshy
2020	J	`jnl`	Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning. CoRR Hongseok Namkoong, Samuel Daulton, Eytan Bakshy
2020	J	`jnl`	Optimizing Coverage and Capacity in Cellular Networks using Machine Learning. CoRR Ryan M. Dreifuerst, Samuel Daulton, Yuchen Qian, Paul Parayil Varkey, Maximilian Balandat, Sanjay Kasturia, Anoop Tomar, Ali Yazdan, Vish Ponnampalam, Robert W. Heath Jr.
2019	J	`jnl`	BoTorch: Programmable Bayesian Optimization in PyTorch. CoRR Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy
2019	J	`jnl`	Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints. CoRR Samuel Daulton, Shaun Singh, Vashist Avadhanula, Drew Dimmery, Eytan Bakshy
2017	—	`conf`	Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes. NIPS Taylor W. Killian, Samuel Daulton, Finale Doshi-Velez, George Dimitri Konidaris
2017	J	`jnl`	Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision Processes. CoRR Taylor W. Killian, Samuel Daulton, George Dimitri Konidaris, Finale Doshi-Velez

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.