Eytan Bakshy - RankMe

88 papers A* 28A 10Journal 46Unranked 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
2026	J	`jnl`	Empirical Gaussian Processes. CoRR Jihao Andreas Lin, Sebastian Ament, Louis C. Tiao, David Eriksson, Maximilian Balandat, 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`	Informed Initialization for Bayesian Optimization and Active Learning. CoRR Carl Hvarfner, David Eriksson, Eytan Bakshy, Maximilian Balandat
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	A*	`conf`	Scalable Gaussian Processes with Latent Kronecker Structure. ICML Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, David Eriksson, José Miguel Hernández-Lobato, Eytan Bakshy
2025	J	`jnl`	Scalable Gaussian Processes with Latent Kronecker Structure. CoRR Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, David Eriksson, José Miguel Hernández-Lobato, Eytan Bakshy
2024	A*	`conf`	Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes. NeurIPS Syrine Belakaria, Ben Letham, Jana Doppa, Barbara Engelhardt, Stefano Ermon, Eytan Bakshy
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`	Joint Composite Latent Space Bayesian Optimization. ICML Natalie Maus, Zhiyuan (Jerry) Lin, Maximilian Balandat, Eytan Bakshy
2024	A	`conf`	Response Time Improves Gaussian Process Models for Perception and Preferences. UAI Michael Shvartsman, Benjamin Letham, Eytan Bakshy, Stephen Keeley
2024	A*	`conf`	Robust Gaussian Processes via Relevance Pursuit. NeurIPS Sebastian Ament, Elizabeth Santorella, David Eriksson, Ben Letham, Maximilian Balandat, Eytan Bakshy
2024	J	`jnl`	Robust Gaussian Processes via Relevance Pursuit. CoRR Sebastian Ament, Elizabeth Santorella, David Eriksson, Ben Letham, Maximilian Balandat, Eytan Bakshy
2024	J	`jnl`	Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure. CoRR Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, Eytan Bakshy
2023	A	`conf`	Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings. AISTATS Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson
2023	J	`jnl`	Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings. CoRR Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson
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	J	`jnl`	Joint Composite Latent Space Bayesian Optimization. CoRR Natalie Maus, Zhiyuan (Jerry) Lin, Maximilian Balandat, Eytan Bakshy
2023	J	`jnl`	Practical Policy Optimization with Personalized Experimentation. CoRR Mia Garrard, Hanson Wang, Ben Letham, Shaun Singh, Abbas Kazerouni, Sarah Tan, Zehui Wang, Yin Huang, Yichun Hu, Chad Zhou, Norm Zhou, Eytan Bakshy
2023	A	`conf`	Sparse Bayesian optimization. AISTATS Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, 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
2023	A	`conf`	qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization. AISTATS Raul Astudillo, Zhiyuan (Jerry) Lin, Eytan Bakshy, Peter I. Frazier
2023	J	`jnl`	qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization. CoRR Raul Astudillo, Zhiyuan (Jerry) Lin, Eytan Bakshy, Peter I. Frazier
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`	Interpretable Personalized Experimentation. KDD Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, 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	A*	`conf`	Looper: An End-to-End ML Platform for Product Decisions. KDD Igor L. Markov, Hanson Wang, Nitya S. Kasturi, Shaun Singh, Mia R. Garrard, Yin Huang, Sze Wai Celeste Yuen, Sarah Tran, Zehui Wang, Igor Glotov, Tanvi Gupta, Peng Chen, Boshuang Huang, Xiaowen Xie, Michael Belkin, Sal Uryasev, Sam Howie, Eytan Bakshy, Norm Zhou
2022	A	`conf`	Multi-objective Bayesian optimization over high-dimensional search spaces. UAI Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2022	A	`conf`	Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes. AISTATS Zhiyuan (Jerry) Lin, Raul Astudillo, Peter I. Frazier, Eytan Bakshy
2022	J	`jnl`	Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes. CoRR Zhiyuan (Jerry) Lin, Raul Astudillo, Peter I. Frazier, 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
2022	J	`jnl`	Sparse Bayesian Optimization. CoRR Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy
2021	A*	`conf`	Bayesian Optimization with High-Dimensional Outputs. NeurIPS Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy
2021	J	`jnl`	Bayesian Optimization with High-Dimensional Outputs. CoRR Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy
2021	J	`jnl`	Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies. CoRR Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy
2021	J	`jnl`	Looper: An end-to-end ML platform for product decisions. CoRR Igor L. Markov, Hanson Wang, Nitya Kasturi, Shaun Singh, Sze Wai Yuen, Mia Garrard, Sarah Tran, Yin Huang, Zehui Wang, Igor Glotov, Tanvi Gupta, Boshuang Huang, Peng Chen, Xiaowen Xie, Michael Belkin, Sal Uryasev, Sam Howie, Eytan Bakshy, Norm Zhou
2021	J	`jnl`	Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces. CoRR Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy
2021	A*	`conf`	Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. NeurIPS Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier
2021	J	`jnl`	Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. CoRR Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier
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
2021	J	`jnl`	PlanAlyzer: assessing threats to the validity of online experiments. Commun. ACM Emma Tosch, Eytan Bakshy, Emery D. Berger, David D. Jensen, J. Eliot B. Moss
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	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
2020	J	`jnl`	Real-world Video Adaptation with Reinforcement Learning. CoRR Hongzi Mao, Shannon Chen, Drew Dimmery, Shaun Singh, Drew Blaisdell, Yuandong Tian, Mohammad Alizadeh, 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
2019	J	`jnl`	PlanAlyzer: Assessing Threats to the Validity of Online Experiments. CoRR Emma Tosch, Eytan Bakshy, Emery D. Berger, David D. Jensen, J. Eliot B. Moss
2019	J	`jnl`	PlanAlyzer: assessing threats to the validity of online experiments. Proc. ACM Program. Lang. Emma Tosch, Eytan Bakshy, Emery D. Berger, David D. Jensen, J. Eliot B. Moss
2019	A*	`conf`	Shrinkage Estimators in Online Experiments. KDD Drew Dimmery, Eytan Bakshy, Jasjeet S. Sekhon
2019	J	`jnl`	Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints. CoRR Samuel Daulton, Shaun Singh, Vashist Avadhanula, Drew Dimmery, Eytan Bakshy
2018	J	`jnl`	Scalable Meta-Learning for Bayesian Optimization. CoRR Matthias Feurer, Benjamin Letham, Eytan Bakshy
2018	A*	`conf`	Social Influence and Reciprocity in Online Gift Giving. CHI René F. Kizilcec, Eytan Bakshy, Dean Eckles, Moira Burke
2017	J	`jnl`	Bias and high-dimensional adjustment in observational studies of peer effects. CoRR Dean Eckles, Eytan Bakshy
2017	J	`jnl`	Constrained Bayesian Optimization with Noisy Experiments. CoRR Benjamin Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy
2015	A*	`conf`	Design and Analysis of Benchmarking Experiments for Distributed Internet Services. WWW Eytan Bakshy, Eitan Frachtenberg
2015	—	`conf`	Online Experiments for Computational Social Science. WWW (Companion Volume) Eytan Bakshy, Sean J. Taylor
2014	J	`jnl`	Designing and Deploying Online Field Experiments. CoRR Eytan Bakshy, Dean Eckles, Michael S. Bernstein
2014	A*	`conf`	Designing and deploying online field experiments. WWW Eytan Bakshy, Dean Eckles, Michael S. Bernstein
2014	J	`jnl`	Modeling the Transition to Public School Choice. J. Artif. Soc. Soc. Simul. Spiro Maroulis, Eytan Bakshy, Louis M. Gomez, Uri Wilensky
2013	A*	`conf`	Quantifying the invisible audience in social networks. CHI Michael S. Bernstein, Eytan Bakshy, Moira Burke, Brian Karrer
2013	J	`jnl`	Selection Effects in Online Sharing: Consequences for Peer Adoption. CoRR Sean J. Taylor, Eytan Bakshy, Sinan Aral
2013	A*	`conf`	Selection effects in online sharing: consequences for peer adoption. EC Sean J. Taylor, Eytan Bakshy, Sinan Aral
2013	A*	`conf`	Uncertainty in online experiments with dependent data: an evaluation of bootstrap methods. KDD Eytan Bakshy, Dean Eckles
2012	J	`jnl`	Social Influence in Social Advertising: Evidence from Field Experiments CoRR Eytan Bakshy, Dean Eckles, Rong Yan, Itamar Rosenn
2012	A*	`conf`	Social influence in social advertising: evidence from field experiments. EC Eytan Bakshy, Dean Eckles, Rong Yan, Itamar Rosenn
2012	J	`jnl`	The Role of Social Networks in Information Diffusion CoRR Eytan Bakshy, Itamar Rosenn, Cameron Marlow, Lada A. Adamic
2012	A*	`conf`	The role of social networks in information diffusion. WWW Eytan Bakshy, Itamar Rosenn, Cameron Marlow, Lada A. Adamic
2011	A	`conf`	Center of Attention: How Facebook Users Allocate Attention across Friends. ICWSM Lars Backstrom, Eytan Bakshy, Jon M. Kleinberg, Thomas M. Lento, Itamar Rosenn
2011	A	`conf`	Everyone's an influencer: quantifying influence on twitter. WSDM Eytan Bakshy, Jake M. Hofman, Winter A. Mason, Duncan J. Watts
2010	J	`jnl`	Seller activity in a virtual marketplace. First Monday David A. Huffaker, Matthew P. Simmons, Eytan Bakshy, Lada A. Adamic
2010	A	`conf`	The Social Dynamics of Economic Activity in a Virtual World. ICWSM Eytan Bakshy, Matthew P. Simmons, David A. Huffaker, Chun-Yuen Cheng, Lada A. Adamic
2009	A*	`conf`	Social influence and the diffusion of user-created content. EC Eytan Bakshy, Brian Karrer, Lada A. Adamic
2008	A*	`conf`	Knowledge sharing and yahoo answers: everyone knows something. WWW Lada A. Adamic, Jun Zhang, Eytan Bakshy, Mark S. Ackerman