Benjamin Letham

34 papers A* 8A 3Journal 19Unranked 4

Year	Rank	Type	Title / Venue / Authors
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
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
2025	J	`jnl`	LILO: Bayesian Optimization with Interactive Natural Language Feedback. CoRR Katarzyna Kobalczyk, Zhiyuan Jerry Lin, Benjamin Letham, Zhuokai Zhao, Maximilian Balandat, Eytan Bakshy
2025	J	`jnl`	Mixed Likelihood Variational Gaussian Processes. CoRR Kaiwen Wu, Craig Sanders, Benjamin Letham, Phillip Guan
2024	J	`jnl`	Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes. CoRR Syrine Belakaria, Benjamin Letham, Janardhan Rao Doppa, Barbara Engelhardt, Stefano Ermon, Eytan Bakshy
2024	A	`conf`	Response Time Improves Gaussian Process Models for Perception and Preferences. UAI Michael Shvartsman, Benjamin Letham, Eytan Bakshy, Stephen Keeley
2023	A*	`conf`	A Semi-parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks. AAAI Stephen Keeley, Benjamin Letham, Craig Sanders, Chase Tymms, Michael Shvartsman
2023	A*	`conf`	Perceptual Requirements for World-Locked Rendering in AR and VR. SIGGRAPH Asia Phillip Guan, Eric Penner, Joel Hegland, Benjamin Letham, Douglas Lanman
2023	J	`jnl`	Perceptual Requirements for World-Locked Rendering in AR and VR. CoRR Phillip Guan, Eric Penner, Joel Hegland, Benjamin Letham, Douglas Lanman
2023	J	`jnl`	Response Time Improves Choice Prediction and Function Estimation for Gaussian Process Models of Perception and Preferences. CoRR Michael Shvartsman, Benjamin Letham, Stephen Keeley
2023	A	`conf`	Sparse Bayesian optimization. AISTATS Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy
2022	A	`conf`	Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation. AISTATS Benjamin Letham, Phillip Guan, Chase Tymms, Eytan Bakshy, Michael Shvartsman
2022	J	`jnl`	Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation. CoRR Benjamin Letham, Phillip Guan, Chase Tymms, Eytan Bakshy, Michael Shvartsman
2022	J	`jnl`	Sparse Bayesian Optimization. CoRR Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, 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`	High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization. NeurIPS Qing Feng, Benjamin Letham, Hongzi Mao, Eytan Bakshy
2020	A*	`conf`	Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization. NeurIPS Benjamin Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy
2020	J	`jnl`	Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization. CoRR Benjamin Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy
2019	J	`jnl`	Bayesian Optimization for Policy Search via Online-Offline Experimentation. CoRR Benjamin Letham, Eytan Bakshy
2019	J	`jnl`	Bayesian Optimization for Policy Search via Online-Offline Experimentation. J. Mach. Learn. Res. Benjamin Letham, Eytan Bakshy
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
2018	J	`jnl`	Scalable Meta-Learning for Bayesian Optimization. CoRR Matthias Feurer, Benjamin Letham, Eytan Bakshy
2017	J	`jnl`	Constrained Bayesian Optimization with Noisy Experiments. CoRR Benjamin Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy
2017	J	`jnl`	Forecasting at Scale. PeerJ Prepr. Sean J. Taylor, Benjamin Letham
2016	A*	`conf`	Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts. KDD Benjamin Letham, Lydia M. Letham, Cynthia Rudin
2015	J	`jnl`	Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model. CoRR Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan
2014	A*	`conf`	Latent Variable Copula Inference for Bundle Pricing from Retail Transaction Data. ICML Benjamin Letham, Wei Sun, Anshul Sheopuri
2013	—	`conf`	An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis. AAAI (Late-Breaking Developments) Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan
2013	J	`jnl`	Growing a list. Data Min. Knowl. Discov. Benjamin Letham, Cynthia Rudin, Katherine A. Heller
2013	J	`jnl`	Learning theory analysis for association rules and sequential event prediction. J. Mach. Learn. Res. Cynthia Rudin, Benjamin Letham, David Madigan
2013	J	`jnl`	Sequential event prediction. Mach. Learn. Benjamin Letham, Cynthia Rudin, David Madigan
2011	A*	`conf`	Sequential Event Prediction with Association Rules. COLT Cynthia Rudin, Benjamin Letham, Ansaf Salleb-Aouissi, Eugene Kogan, David Madigan

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.