| Exposure to ideologically diverse news and opinion on Facebook E Bakshy, S Messing, L Adamic Science 348 (6239), 1130-1132, 2015 | 3526 | 2015 |
| Everyone's an influencer: Quantifying influence on Twitter E Bakshy, JM Hofman, WA Mason, DJ Watts Proceedings of the 4th ACM international conference on Web search and data …, 2011 | 2828 | 2011 |
| The role of social networks in information diffusion E Bakshy, I Rosenn, C Marlow, L Adamic Proceedings of the 21st international conference on World Wide Web, 519-528, 2012 | 2032 | 2012 |
| Knowledge sharing and Yahoo answers: Everyone knows something LA Adamic, J Zhang, E Bakshy, MS Ackerman Proceeding of the 17th international conference on World Wide Web, 665-674, 2008 | 1095 | 2008 |
| BoTorch: A framework for efficient Monte-Carlo Bayesian optimization M Balandat, B Karrer, D Jiang, S Daulton, B Letham, AG Wilson, E Bakshy Advances in Neural Information Processing Systems (NeurIPS), 2020 | 643* | 2020 |
| Quantifying the invisible audience in social networks MS Bernstein, E Bakshy, M Burke, B Karrer Proceedings of the ACM SIGCHI Conference on Human Factors in Computing …, 2013 | 499 | 2013 |
| Social influence and the diffusion of user-created content E Bakshy, B Karrer, LA Adamic Proceedings of the 10th ACM conference on Electronic commerce, 325-334, 2009 | 470 | 2009 |
| Social influence in social advertising: Evidence from field experiments E Bakshy, D Eckles, R Yan, I Rosenn Proceedings of the 13th ACM Conference on Electronic Commerce, 146-161, 2012 | 377 | 2012 |
| Constrained Bayesian optimization with noisy experiments B Letham, B Karrer, G Ottoni, E Bakshy Bayesian Analysis 14 (2), 495-519, 2019 | 299 | 2019 |
| Designing and deploying online field experiments E Bakshy, D Eckles, MS Bernstein Proceedings of the 23rd international conference on World wide web, 283-292, 2014 | 229 | 2014 |
| Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization S Daulton, M Balandat, E Bakshy Advances in Neural Information Processing Systems (NeurIPS), 2020 | 184 | 2020 |
| Estimating peer effects in networks with peer encouragement designs D Eckles, RF Kizilcec, E Bakshy Proceedings of the National Academy of Sciences 113 (27), 7316-7322, 2016 | 143 | 2016 |
| Social influence and political mobilization: Further evidence from a randomized experiment in the 2012 US presidential election JJ Jones, RM Bond, E Bakshy, D Eckles, JH Fowler PloS one 12 (4), e0173851, 2017 | 139 | 2017 |
| Center of attention: How facebook users allocate attention across friends L Backstrom, E Bakshy, J Kleinberg, TM Lento, I Rosenn Proc. 5th International Conference on Weblogs and Social Media, 2011 | 129 | 2011 |
| Scalable meta-learning for bayesian optimization using ranking-weighted gaussian process ensembles M Feurer, B Letham, E Bakshy ICML Workshop on AutoML, 2018 | 110* | 2018 |
| AE: A domain-agnostic platform for adaptive experimentation E Bakshy, L Dworkin, B Karrer, K Kashin, B Letham, A Murthy, S Singh NeurIPS Workshop on Systems for Machine Learning, 2018 | 96 | 2018 |
| Top challenges from the first practical online controlled experiments summit S Gupta, R Kohavi, D Tang, Y Xu, R Andersen, E Bakshy, N Cardin, ... ACM SIGKDD Explorations Newsletter 21 (1), 20-35, 2019 | 88 | 2019 |
| Re-examining linear embeddings for high-dimensional Bayesian optimization B Letham, R Calandra, A Rai, E Bakshy Advances in Neural Information Processing Systems (NeurIPS), 2020 | 84 | 2020 |
| Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement S Daulton, M Balandat, E Bakshy Advances in Neural Information Processing Systems (NeurIPS), 2021 | 82 | 2021 |
| Bias and high-dimensional adjustment in observational studies of peer effects D Eckles, E Bakshy Journal of the American Statistical Association, 1-30, 2020 | 79 | 2020 |
Lada AdamicComputational Social Scientist at FacebookVerified email at umich.edu
