Knowledge sharing and yahoo answers: everyone knows something. - Lada A. Adamic; Jun Zhang 008; Eytan Bakshy; Mark S. Ackerman (WWW, 2008)

Social influence and the diffusion of user-created content. - Eytan Bakshy; Brian Karrer; Lada A. Adamic (EC, 2009)

The Social Dynamics of Economic Activity in a Virtual World. - Eytan Bakshy; Matthew P. Simmons; David A. Huffaker; Chun-Yuen Cheng; Lada A. Adamic (ICWSM, 2010)

Seller activity in a virtual marketplace. - David A. Huffaker; Matthew P. Simmons; Eytan Bakshy; Lada A. Adamic (First Monday, 2010)

Center of Attention: How Facebook Users Allocate Attention across Friends. - Lars Backstrom; Eytan Bakshy; Jon M. Kleinberg; Thomas M. Lento; Itamar Rosenn (ICWSM, 2011)

Everyone's an influencer: quantifying influence on twitter. - Eytan Bakshy; Jake M. Hofman; Winter A. Mason; Duncan J. Watts (WSDM, 2011)

Social influence in social advertising: evidence from field experiments. - Eytan Bakshy; Dean Eckles; Rong Yan; Itamar Rosenn (EC, 2012)

The role of social networks in information diffusion. - Eytan Bakshy; Itamar Rosenn; Cameron Marlow; Lada A. Adamic (WWW, 2012)

Quantifying the invisible audience in social networks. - Michael S. Bernstein; Eytan Bakshy; Moira Burke; Brian Karrer (CHI, 2013)

Uncertainty in online experiments with dependent data: an evaluation of bootstrap methods. - Eytan Bakshy; Dean Eckles (KDD, 2013)

Selection effects in online sharing: consequences for peer adoption. - Sean J. Taylor; Eytan Bakshy; Sinan Aral (EC, 2013)

Designing and deploying online field experiments. - Eytan Bakshy; Dean Eckles; Michael S. Bernstein (WWW, 2014)

Modeling the Transition to Public School Choice. - Spiro Maroulis; Eytan Bakshy; Louis M. Gomez; Uri Wilensky (J. Artif. Soc. Soc. Simul., 2014)

Design and Analysis of Benchmarking Experiments for Distributed Internet Services. - Eytan Bakshy; Eitan Frachtenberg (WWW, 2015)

Online Experiments for Computational Social Science. - Eytan Bakshy; Sean J. Taylor (WWW (Companion Volume), 2015)

Bias and high-dimensional adjustment in observational studies of peer effects. - Dean Eckles; Eytan Bakshy (CoRR, 2017)

Constrained Bayesian Optimization with Noisy Experiments. - Benjamin Letham; Brian Karrer; Guilherme Ottoni; Eytan Bakshy (CoRR, 2017)

Social Influence and Reciprocity in Online Gift Giving. - René F. Kizilcec; Eytan Bakshy; Dean Eckles; Moira Burke (CHI, 2018)

Scalable Meta-Learning for Bayesian Optimization. - Matthias Feurer; Benjamin Letham; Eytan Bakshy (CoRR, 2018)

Shrinkage Estimators in Online Experiments. - Drew Dimmery; Eytan Bakshy; Jasjeet S. Sekhon (KDD, 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)

Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints. - Samuel Daulton; Shaun Singh; Vashist Avadhanula; Drew Dimmery; Eytan Bakshy (CoRR, 2019)

Bayesian Optimization for Policy Search via Online-Offline Experimentation. - Benjamin Letham; Eytan Bakshy (J. Mach. Learn. Res., 2019)

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 (NeurIPS, 2020)

Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization. - Samuel Daulton; Maximilian Balandat; Eytan Bakshy (NeurIPS, 2020)

High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization. - Qing Feng; Benjamin Letham; Hongzi Mao; Eytan Bakshy (NeurIPS, 2020)

Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization. - Benjamin Letham; Roberto Calandra; Akshara Rai; Eytan Bakshy (NeurIPS, 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)

Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning. - Hongseok Namkoong; Samuel Daulton; Eytan Bakshy (CoRR, 2020)

Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs. - Raul Astudillo; Daniel R. Jiang; Maximilian Balandat; Eytan Bakshy; Peter I. Frazier (NeurIPS, 2021)

Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement. - Samuel Daulton; Maximilian Balandat; Eytan Bakshy (NeurIPS, 2021)

Bayesian Optimization with High-Dimensional Outputs. - Wesley J. Maddox; Maximilian Balandat; Andrew Gordon Wilson; Eytan Bakshy (NeurIPS, 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)

Distilling Heterogeneity: From Explanations of Heterogeneous Treatment Effect Models to Interpretable Policies. - Han Wu; Sarah Tan; Weiwei Li 006; Mia Garrard; Adam Obeng; Drew Dimmery; Shaun Singh; Hanson Wang; Daniel R. Jiang; Eytan Bakshy (CoRR, 2021)

Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation. - Benjamin Letham; Phillip Guan; Chase Tymms; Eytan Bakshy; Michael Shvartsman (AISTATS, 2022)

Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes. - Zhiyuan (Jerry) Lin; Raul Astudillo; Peter I. Frazier; Eytan Bakshy (AISTATS, 2022)

Robust Multi-Objective Bayesian Optimization Under Input Noise. - Samuel Daulton; Sait Cakmak; Maximilian Balandat; Michael A. Osborne; Enlu Zhou; Eytan Bakshy (ICML, 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; et al. (KDD, 2022)

Interpretable Personalized Experimentation. - Han Wu; Sarah Tan; Weiwei Li 006; Mia Garrard; Adam Obeng; Drew Dimmery; Shaun Singh; Hanson Wang; Daniel R. Jiang; Eytan Bakshy (KDD, 2022)

Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. - Samuel Daulton; Xingchen Wan; David Eriksson; Maximilian Balandat; Michael A. Osborne; Eytan Bakshy (NeurIPS, 2022)

Multi-objective Bayesian optimization over high-dimensional search spaces. - Samuel Daulton; David Eriksson; Maximilian Balandat; Eytan Bakshy (UAI, 2022)

qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization. - Raul Astudillo; Zhiyuan (Jerry) Lin; Eytan Bakshy; Peter I. Frazier (AISTATS, 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 (AISTATS, 2023)

Sparse Bayesian optimization. - Sulin Liu; Qing Feng; David Eriksson; Benjamin Letham; Eytan Bakshy (AISTATS, 2023)

Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information. - Samuel Daulton; Maximilian Balandat; Eytan Bakshy (ICML, 2023)

Unexpected Improvements to Expected Improvement for Bayesian Optimization. - Sebastian Ament; Samuel Daulton; David Eriksson; Maximilian Balandat; Eytan Bakshy (NeurIPS, 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)

Joint Composite Latent Space Bayesian Optimization. - Natalie Maus; Zhiyuan (Jerry) Lin; Maximilian Balandat; Eytan Bakshy (ICML, 2024)

Robust Gaussian Processes via Relevance Pursuit. - Sebastian Ament; Elizabeth Santorella; David Eriksson; Ben Letham; Maximilian Balandat; Eytan Bakshy (NeurIPS, 2024)

Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes. - Syrine Belakaria; Ben Letham; Jana Doppa; Barbara Engelhardt; Stefano Ermon; Eytan Bakshy (NeurIPS, 2024)

Response Time Improves Gaussian Process Models for Perception and Preferences. - Michael Shvartsman; Benjamin Letham; Eytan Bakshy; Stephen Keeley (UAI, 2024)

Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure. - Jihao Andreas Lin; Sebastian Ament; Maximilian Balandat; Eytan Bakshy (CoRR, 2024)

Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments. - Qing Feng; Samuel Daulton; Benjamin Letham; Maximilian Balandat; Eytan Bakshy (KDD (1), 2025)

Scalable Gaussian Processes with Latent Kronecker Structure. - Jihao Andreas Lin; Sebastian Ament; Maximilian Balandat; David Eriksson; José Miguel Hernández-Lobato; Eytan Bakshy (CoRR, 2025)