Information Diffusion and Social Influence in Online Networks.

The explosive growth of online social systems has changed how individuals consume and disseminate information. In this thesis, we conduct large-scale observational and experimental studies that allow us to determine the role that social networks play in information diffusion online, and the factors that mediate this influence. We first examine the adoption of user-created content in a virtual world, and find that social transmission appears to play a prominent role in the adoption of content. Ultimately, we are faced with a critical problem that underlies all contemporary empirical research on social influence: how do we measure whether individuals in a network influence one another, when the basis for their interaction rests upon commonalities that are predictive of their future behavior? We use two coupled experiments to address this question. In our first experiment, we randomize exposure to social signals about friends' information sharing behavior to determine the causal effect of networks on diffusion among 253 million subjects in situ. Our second experiment further tests how social information affects individual sharing decisions when viewing content. Finally, this thesis concludes with a study that examines how individuals allocate attention across their network of contacts, which has implications for influence and information diversity in networks.Ph.D.InformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89838/1/ebakshy_1.pd

Designing and Deploying Online Field Experiments

Online experiments are widely used to compare specific design alternatives, but they can also be used to produce generalizable knowledge and inform strategic decision making. Doing so often requires sophisticated experimental designs, iterative refinement, and careful logging and analysis. Few tools exist that support these needs. We thus introduce a language for online field experiments called PlanOut. PlanOut separates experimental design from application code, allowing the experimenter to concisely describe experimental designs, whether common "A/B tests" and factorial designs, or more complex designs involving conditional logic or multiple experimental units. These latter designs are often useful for understanding causal mechanisms involved in user behaviors. We demonstrate how experiments from the literature can be implemented in PlanOut, and describe two large field experiments conducted on Facebook with PlanOut. For common scenarios in which experiments are run iteratively and in parallel, we introduce a namespaced management system that encourages sound experimental practice.Comment: Proceedings of the 23rd international conference on World wide web, 283-29

Scalable Meta-Learning for Bayesian Optimization

Bayesian optimization has become a standard technique for hyperparameter optimization, including data-intensive models such as deep neural networks that may take days or weeks to train. We consider the setting where previous optimization runs are available, and we wish to use their results to warm-start a new optimization run. We develop an ensemble model that can incorporate the results of past optimization runs, while avoiding the poor scaling that comes with putting all results into a single Gaussian process model. The ensemble combines models from past runs according to estimates of their generalization performance on the current optimization. Results from a large collection of hyperparameter optimization benchmark problems and from optimization of a production computer vision platform at Facebook show that the ensemble can substantially reduce the time it takes to obtain near-optimal configurations, and is useful for warm-starting expensive searches or running quick re-optimizations