The Unofficial Google Data Science Blog

OCTOBER 7, 2015

This is essentially the same as finding a truly useful objective to optimize. Recently, we presented some basic insights from our effort to measure and predict long-term effects at KDD 2015 [1]. We use this knowledge to define objective functions to optimize our ads system with a view towards the long-term.

Modeling 61

Modeling Experimentation Knowledge Discovery Testing 61

The Unofficial Google Data Science Blog

JULY 22, 2020

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. 2015): 37-45. [3] An efficient bandit algorithm for realtime multivariate optimization." Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2] Scott, Steven L.

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

NOVEMBER 4, 2015

Limitations Second order calibration, like ordinary calibration, is intended to be easy and useful, not comprehensive or optimal, and it shares some of ordinary calibration’s limitations. References [1] Omkar Muralidharan, Amir Najmi "Second Order Calibration: A Simple Way To Get Approximate Posteriors" , Technical Report, Google, 2015. [2]

KDD 40

KDD Testing Machine Learning Modeling 40

Domino Data Lab

AUGUST 1, 2021

2015) for additional details. Conference on Knowledge Discovery and Data Mining, pp. Neural machine translation by jointly learning to align and translate , ICLR, 2015. def create_model(): sgd = optimizers.SGD(lr=0.01, decay=0, momentum=0.9, Skater uses different techniques depending on the type of the model (e.g.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139