Eytan Bakshy

According to our database, Eytan Bakshy authored at least 51 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure.

Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, Eytan Bakshy

CoRR, 2024

Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes.

Syrine Belakaria, Ben Letham, Jana Doppa, Barbara Engelhardt, Stefano Ermon, Eytan Bakshy

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Robust Gaussian Processes via Relevance Pursuit.

Sebastian Ament, Elizabeth Santorella, David Eriksson, Ben Letham, Maximilian Balandat, Eytan Bakshy

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Joint Composite Latent Space Bayesian Optimization.

Natalie Maus, Zhiyuan (Jerry) Lin, Maximilian Balandat, Eytan Bakshy

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Practical Policy Optimization with Personalized Experimentation.

Mia Garrard, Hanson Wang, Ben Letham, Shaun Singh, Abbas Kazerouni, Sarah Tan, Zehui Wang, Yin Huang, Yichun Hu, Chad Zhou, Norm Zhou, Eytan Bakshy

CoRR, 2023

Unexpected Improvements to Expected Improvement for Bayesian Optimization.

Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information.

Samuel Daulton, Maximilian Balandat, Eytan Bakshy

Proceedings of the International Conference on Machine Learning, 2023

Sparse Bayesian optimization.

Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings.

Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization.

Raul Astudillo, Zhiyuan (Jerry) Lin, Eytan Bakshy, Peter I. Frazier

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Multi-objective Bayesian optimization over high-dimensional search spaces.

Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization.

Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Interpretable Personalized Experimentation.

Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Looper: An End-to-End ML Platform for Product Decisions.

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

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Robust Multi-Objective Bayesian Optimization Under Input Noise.

Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy

Proceedings of the International Conference on Machine Learning, 2022

Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes.

Zhiyuan (Jerry) Lin, Raul Astudillo, Peter I. Frazier, Eytan Bakshy

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation.

Benjamin Letham, Phillip Guan, Chase Tymms, Eytan Bakshy, Michael Shvartsman

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies.

Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy

CoRR, 2021

PlanAlyzer: assessing threats to the validity of online experiments.

Emma Tosch, Eytan Bakshy, Emery D. Berger, David D. Jensen, J. Eliot B. Moss

Commun. ACM, 2021

Bayesian Optimization with High-Dimensional Outputs.

Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement.

Samuel Daulton, Maximilian Balandat, Eytan Bakshy

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs.

Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning.

Hongseok Namkoong, Samuel Daulton, Eytan Bakshy

CoRR, 2020

Real-world Video Adaptation with Reinforcement Learning.

Hongzi Mao, Shannon Chen, Drew Dimmery, Shaun Singh, Drew Blaisdell, Yuandong Tian, Mohammad Alizadeh, Eytan Bakshy

CoRR, 2020

Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization.

Benjamin Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization.

Qing Feng, Benjamin Letham, Hongzi Mao, Eytan Bakshy

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization.

Samuel Daulton, Maximilian Balandat, Eytan Bakshy

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

2019

Bayesian Optimization for Policy Search via Online-Offline Experimentation.

Benjamin Letham, Eytan Bakshy

J. Mach. Learn. Res., 2019

Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints.

Samuel Daulton, Shaun Singh, Vashist Avadhanula, Drew Dimmery, Eytan Bakshy

CoRR, 2019

BoTorch: Programmable Bayesian Optimization in PyTorch.

Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy

CoRR, 2019

Shrinkage Estimators in Online Experiments.

Drew Dimmery, Eytan Bakshy, Jasjeet S. Sekhon

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

2018

Scalable Meta-Learning for Bayesian Optimization.

Matthias Feurer, Benjamin Letham, Eytan Bakshy

CoRR, 2018

Social Influence and Reciprocity in Online Gift Giving.

René F. Kizilcec, Eytan Bakshy, Dean Eckles, Moira Burke

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 2018

2017

Constrained Bayesian Optimization with Noisy Experiments.

Benjamin Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy

CoRR, 2017

Bias and high-dimensional adjustment in observational studies of peer effects.

Dean Eckles, Eytan Bakshy

CoRR, 2017

2015

Online Experiments for Computational Social Science.

Eytan Bakshy, Sean J. Taylor

Proceedings of the 24th International Conference on World Wide Web Companion, 2015

Design and Analysis of Benchmarking Experiments for Distributed Internet Services.

Eytan Bakshy, Eitan Frachtenberg

Proceedings of the 24th International Conference on World Wide Web, 2015

2014

Modeling the Transition to Public School Choice.

Spiro Maroulis, Eytan Bakshy, Louis M. Gomez, Uri Wilensky

J. Artif. Soc. Soc. Simul., 2014

Designing and deploying online field experiments.

Eytan Bakshy, Dean Eckles, Michael S. Bernstein

Proceedings of the 23rd International World Wide Web Conference, 2014

2013

Selection effects in online sharing: consequences for peer adoption.

Sean J. Taylor, Eytan Bakshy, Sinan Aral

Proceedings of the fourteenth ACM Conference on Electronic Commerce, 2013

Uncertainty in online experiments with dependent data: an evaluation of bootstrap methods.

Eytan Bakshy, Dean Eckles

Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013

Quantifying the invisible audience in social networks.

Michael S. Bernstein, Eytan Bakshy, Moira Burke, Brian Karrer

Proceedings of the 2013 ACM SIGCHI Conference on Human Factors in Computing Systems, 2013

2012

The role of social networks in information diffusion.

Eytan Bakshy, Itamar Rosenn, Cameron Marlow, Lada A. Adamic

Proceedings of the 21st World Wide Web Conference 2012, 2012

Social influence in social advertising: evidence from field experiments.

Eytan Bakshy, Dean Eckles, Rong Yan, Itamar Rosenn

Proceedings of the 13th ACM Conference on Electronic Commerce, 2012

2011

Everyone's an influencer: quantifying influence on twitter.

Eytan Bakshy, Jake M. Hofman, Winter A. Mason, Duncan J. Watts

Proceedings of the Forth International Conference on Web Search and Web Data Mining, 2011

Center of Attention: How Facebook Users Allocate Attention across Friends.

Lars Backstrom, Eytan Bakshy, Jon M. Kleinberg, Thomas M. Lento, Itamar Rosenn

Proceedings of the Fifth International Conference on Weblogs and Social Media, 2011

2010

Seller activity in a virtual marketplace.

David A. Huffaker, Matthew P. Simmons, Eytan Bakshy, Lada A. Adamic

First Monday, 2010

The Social Dynamics of Economic Activity in a Virtual World.

Eytan Bakshy, Matthew P. Simmons, David A. Huffaker, Chun-Yuen Cheng, Lada A. Adamic

Proceedings of the Fourth International Conference on Weblogs and Social Media, 2010

2009

Social influence and the diffusion of user-created content.

Eytan Bakshy, Brian Karrer, Lada A. Adamic

Proceedings of the Proceedings 10th ACM Conference on Electronic Commerce (EC-2009), 2009

2008

Knowledge sharing and yahoo answers: everyone knows something.

Lada A. Adamic, Jun Zhang, Eytan Bakshy, Mark S. Ackerman

Proceedings of the 17th International Conference on World Wide Web, 2008