Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
The Web Conference (WWW)
We present a method for implementing shrinkage of treatment effect estimators, and hence improving their precision, via experiment splitting. Experiment splitting reduces shrinkage to a standard prediction problem. The method makes minimal distributional assumptions, and allows for the degree of shrinkage in one metric to depend on other metrics. Using a dataset of 226 Facebook News Feed A/B tests, we show that a lasso estimator based on repeated experiment splitting has a 44% lower mean squared predictive error than the conventional, unshrunk treatment effect estimator, a 18% lower mean squared predictive error than the James-Stein shrinkage estimator, and would lead to substantially improved launch decisions over both.
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Kiwan Maeng, Manoj Chakkaravarthy, Udit Gupta, David Brooks, Carole-Jean Wu
Harjasleen Malvai, Lefteris Kokoris-Kogias, Alberto Sonnino, Esha Ghosh, Ercan Ozturk, Kevin Lewi, Sean Lawlor