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2020 – today
- 2024
[c35]Natalie Maus, Zhiyuan (Jerry) Lin, Maximilian Balandat, Eytan Bakshy:
Joint Composite Latent Space Bayesian Optimization. ICML 2024- [i34]Syrine Belakaria, Benjamin Letham, Janardhan Rao Doppa, Barbara Engelhardt, Stefano Ermon, Eytan Bakshy:
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes. CoRR abs/2407.09739 (2024) - [i33]Jihao Andreas Lin, Sebastian Ament, Maximilian Balandat, Eytan Bakshy:
Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure. CoRR abs/2410.09239 (2024) - [i32]Sebastian Ament, Elizabeth Santorella, David Eriksson, Ben Letham, Maximilian Balandat, Eytan Bakshy:
Robust Gaussian Processes via Relevance Pursuit. CoRR abs/2410.24222 (2024) - 2023
- [c34]Raul Astudillo, Zhiyuan (Jerry) Lin, Eytan Bakshy, Peter I. Frazier:
qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization. AISTATS 2023: 1093-1114 - [c33]Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy:
Sparse Bayesian optimization. AISTATS 2023: 3754-3774 - [c32]Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson:
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings. AISTATS 2023: 7021-7039 - [c31]Samuel Daulton, Maximilian Balandat, Eytan Bakshy:
Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information. ICML 2023: 7167-7204 - [c30]Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy:
Unexpected Improvements to Expected Improvement for Bayesian Optimization. NeurIPS 2023 - [i31]Aryan Deshwal, Sebastian Ament, Maximilian Balandat, Eytan Bakshy, Janardhan Rao Doppa, David Eriksson:
Bayesian Optimization over High-Dimensional Combinatorial Spaces via Dictionary-based Embeddings. CoRR abs/2303.01774 (2023) - [i30]Raul Astudillo, Zhiyuan (Jerry) Lin, Eytan Bakshy, Peter I. Frazier:
qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization. CoRR abs/2303.15746 (2023) - [i29]Mia Garrard, Hanson Wang, Ben Letham, Shaun Singh, Abbas Kazerouni, Sarah Tan, Zehui Wang, Yin Huang, Yichun Hu, Chad Zhou, Norm Zhou, Eytan Bakshy:
Practical Policy Optimization with Personalized Experimentation. CoRR abs/2303.17648 (2023) - [i28]Sebastian Ament, Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy:
Unexpected Improvements to Expected Improvement for Bayesian Optimization. CoRR abs/2310.20708 (2023) - [i27]Natalie Maus, Zhiyuan (Jerry) Lin, Maximilian Balandat, Eytan Bakshy:
Joint Composite Latent Space Bayesian Optimization. CoRR abs/2311.02213 (2023) - 2022
- [c29]Zhiyuan (Jerry) Lin, Raul Astudillo, Peter I. Frazier, Eytan Bakshy:
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes. AISTATS 2022: 4235-4258 - [c28]Benjamin Letham, Phillip Guan, Chase Tymms, Eytan Bakshy, Michael Shvartsman:
Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation. AISTATS 2022: 8493-8513 - [c27]Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy:
Robust Multi-Objective Bayesian Optimization Under Input Noise. ICML 2022: 4831-4866 - [c26]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:
Looper: An End-to-End ML Platform for Product Decisions. KDD 2022: 3513-3523 - [c25]Han Wu
, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy:
Interpretable Personalized Experimentation. KDD 2022: 4173-4183 - [c24]Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy:
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. NeurIPS 2022 - [c23]Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy:
Multi-objective Bayesian optimization over high-dimensional search spaces. UAI 2022: 507-517 - [i26]Samuel Daulton, Sait Cakmak, Maximilian Balandat, Michael A. Osborne, Enlu Zhou, Eytan Bakshy:
Robust Multi-Objective Bayesian Optimization Under Input Noise. CoRR abs/2202.07549 (2022) - [i25]Sulin Liu, Qing Feng, David Eriksson, Benjamin Letham, Eytan Bakshy:
Sparse Bayesian Optimization. CoRR abs/2203.01900 (2022) - [i24]Benjamin Letham, Phillip Guan, Chase Tymms, Eytan Bakshy, Michael Shvartsman:
Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation. CoRR abs/2203.09751 (2022) - [i23]Zhiyuan (Jerry) Lin, Raul Astudillo, Peter I. Frazier, Eytan Bakshy:
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes. CoRR abs/2203.11382 (2022) - [i22]Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy:
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. CoRR abs/2210.10199 (2022) - 2021
- [j5]Emma Tosch, Eytan Bakshy, Emery D. Berger, David D. Jensen, J. Eliot B. Moss:
PlanAlyzer: assessing threats to the validity of online experiments. Commun. ACM 64(9): 108-116 (2021) - [c22]Samuel Daulton, Maximilian Balandat, Eytan Bakshy:
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. NeurIPS 2021: 2187-2200 - [c21]Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy:
Bayesian Optimization with High-Dimensional Outputs. NeurIPS 2021: 19274-19287 - [c20]Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier:
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. NeurIPS 2021: 20197-20209 - [i21]Samuel Daulton, Maximilian Balandat, Eytan Bakshy:
Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. CoRR abs/2105.08195 (2021) - [i20]Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy:
Bayesian Optimization with High-Dimensional Outputs. CoRR abs/2106.12997 (2021) - [i19]Samuel Daulton, David Eriksson, Maximilian Balandat, Eytan Bakshy:
Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces. CoRR abs/2109.10964 (2021) - [i18]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:
Looper: An end-to-end ML platform for product decisions. CoRR abs/2110.07554 (2021) - [i17]Han Wu, Sarah Tan, Weiwei Li, Mia Garrard, Adam Obeng, Drew Dimmery, Shaun Singh, Hanson Wang, Daniel R. Jiang, Eytan Bakshy:
Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies. CoRR abs/2111.03267 (2021) - [i16]Raul Astudillo, Daniel R. Jiang, Maximilian Balandat, Eytan Bakshy, Peter I. Frazier:
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. CoRR abs/2111.06537 (2021) - 2020
- [c19]Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy:
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization. NeurIPS 2020 - [c18]Samuel Daulton, Maximilian Balandat, Eytan Bakshy:
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. NeurIPS 2020 - [c17]Qing Feng, Benjamin Letham, Hongzi Mao, Eytan Bakshy:
High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization. NeurIPS 2020 - [c16]Benjamin Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy:
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization. NeurIPS 2020 - [i15]Benjamin Letham, Roberto Calandra, Akshara Rai, Eytan Bakshy:
Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization. CoRR abs/2001.11659 (2020) - [i14]Samuel Daulton, Maximilian Balandat, Eytan Bakshy:
Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. CoRR abs/2006.05078 (2020) - [i13]Hongzi Mao, Shannon Chen, Drew Dimmery, Shaun Singh, Drew Blaisdell, Yuandong Tian, Mohammad Alizadeh, Eytan Bakshy:
Real-world Video Adaptation with Reinforcement Learning. CoRR abs/2008.12858 (2020) - [i12]Hongseok Namkoong, Samuel Daulton, Eytan Bakshy:
Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning. CoRR abs/2011.14266 (2020)
2010 – 2019
- 2019
- [j4]Benjamin Letham, Eytan Bakshy:
Bayesian Optimization for Policy Search via Online-Offline Experimentation. J. Mach. Learn. Res. 20: 145:1-145:30 (2019) - [j3]Emma Tosch, Eytan Bakshy, Emery D. Berger, David D. Jensen, J. Eliot B. Moss:
PlanAlyzer: assessing threats to the validity of online experiments. Proc. ACM Program. Lang. 3(OOPSLA): 182:1-182:30 (2019) - [c15]Drew Dimmery, Eytan Bakshy, Jasjeet S. Sekhon:
Shrinkage Estimators in Online Experiments. KDD 2019: 2914-2922 - [i11]Benjamin Letham, Eytan Bakshy:
Bayesian Optimization for Policy Search via Online-Offline Experimentation. CoRR abs/1904.01049 (2019) - [i10]Emma Tosch, Eytan Bakshy, Emery D. Berger, David D. Jensen, J. Eliot B. Moss:
PlanAlyzer: Assessing Threats to the Validity of Online Experiments. CoRR abs/1909.13649 (2019) - [i9]Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy:
BoTorch: Programmable Bayesian Optimization in PyTorch. CoRR abs/1910.06403 (2019) - [i8]Samuel Daulton, Shaun Singh, Vashist Avadhanula, Drew Dimmery, Eytan Bakshy:
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints. CoRR abs/1911.00638 (2019) - 2018
- [c14]René F. Kizilcec, Eytan Bakshy, Dean Eckles, Moira Burke:
Social Influence and Reciprocity in Online Gift Giving. CHI 2018: 126 - [i7]Matthias Feurer, Benjamin Letham, Eytan Bakshy:
Scalable Meta-Learning for Bayesian Optimization. CoRR abs/1802.02219 (2018) - 2017
- [i6]Dean Eckles, Eytan Bakshy:
Bias and high-dimensional adjustment in observational studies of peer effects. CoRR abs/1706.04692 (2017) - [i5]Benjamin Letham, Brian Karrer, Guilherme Ottoni, Eytan Bakshy:
Constrained Bayesian Optimization with Noisy Experiments. CoRR abs/1706.07094 (2017) - 2015
- [c13]Eytan Bakshy, Eitan Frachtenberg:
Design and Analysis of Benchmarking Experiments for Distributed Internet Services. WWW 2015: 108-118 - [c12]Eytan Bakshy, Sean J. Taylor:
Online Experiments for Computational Social Science. WWW (Companion Volume) 2015: 1543 - 2014
- [j2]Spiro Maroulis, Eytan Bakshy, Louis M. Gomez, Uri Wilensky:
Modeling the Transition to Public School Choice. J. Artif. Soc. Soc. Simul. 17(2) (2014) - [c11]Eytan Bakshy, Dean Eckles, Michael S. Bernstein:
Designing and deploying online field experiments. WWW 2014: 283-292 - [i4]Eytan Bakshy, Dean Eckles, Michael S. Bernstein:
Designing and Deploying Online Field Experiments. CoRR abs/1409.3174 (2014) - 2013
- [c10]Michael S. Bernstein, Eytan Bakshy, Moira Burke, Brian Karrer:
Quantifying the invisible audience in social networks. CHI 2013: 21-30 - [c9]Eytan Bakshy, Dean Eckles:
Uncertainty in online experiments with dependent data: an evaluation of bootstrap methods. KDD 2013: 1303-1311 - [c8]Sean J. Taylor, Eytan Bakshy, Sinan Aral:
Selection effects in online sharing: consequences for peer adoption. EC 2013: 821-836 - [i3]Sean J. Taylor, Eytan Bakshy, Sinan Aral:
Selection Effects in Online Sharing: Consequences for Peer Adoption. CoRR abs/1311.2878 (2013) - 2012
- [c7]Eytan Bakshy, Dean Eckles, Rong Yan, Itamar Rosenn:
Social influence in social advertising: evidence from field experiments. EC 2012: 146-161 - [c6]Eytan Bakshy, Itamar Rosenn, Cameron Marlow, Lada A. Adamic:
The role of social networks in information diffusion. WWW 2012: 519-528 - [i2]Eytan Bakshy, Itamar Rosenn, Cameron Marlow, Lada A. Adamic:
The Role of Social Networks in Information Diffusion. CoRR abs/1201.4145 (2012) - [i1]Eytan Bakshy, Dean Eckles, Rong Yan, Itamar Rosenn:
Social Influence in Social Advertising: Evidence from Field Experiments. CoRR abs/1206.4327 (2012) - 2011
- [c5]Lars Backstrom, Eytan Bakshy, Jon M. Kleinberg, Thomas M. Lento, Itamar Rosenn:
Center of Attention: How Facebook Users Allocate Attention across Friends. ICWSM 2011 - [c4]Eytan Bakshy, Jake M. Hofman, Winter A. Mason, Duncan J. Watts:
Everyone's an influencer: quantifying influence on twitter. WSDM 2011: 65-74 - 2010
- [j1]David A. Huffaker, Matthew P. Simmons, Eytan Bakshy, Lada A. Adamic:
Seller activity in a virtual marketplace. First Monday 15(7) (2010) - [c3]Eytan Bakshy, Matthew P. Simmons, David A. Huffaker, Chun-Yuen Cheng, Lada A. Adamic:
The Social Dynamics of Economic Activity in a Virtual World. ICWSM 2010
2000 – 2009
- 2009
- [c2]Eytan Bakshy, Brian Karrer, Lada A. Adamic:
Social influence and the diffusion of user-created content. EC 2009: 325-334 - 2008
- [c1]Lada A. Adamic, Jun Zhang, Eytan Bakshy, Mark S. Ackerman:
Knowledge sharing and yahoo answers: everyone knows something. WWW 2008: 665-674
Coauthor Index
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