Name
Abstract | ||
|---|---|---|
In real world systems, the predictions of deployed Machine Learned models affect the training data available to build subsequent models. This introduces a bias in the training data that needs to be addressed. Existing solutions to this problem attempt to resolve the problem by either casting this in the reinforcement learning framework or by quantifying the bias and re-weighting the loss functions. In this work, we develop a novel Adversarial Neural Network (ANN) model, an alternative approach which creates a representation of the data that is invariant to the bias. We take the Paid Search auction as our working example and ad display position features as the confounding features for this setting. We show the success of this approach empirically on both synthetic data as well as real world paid search auction data from a major search engine. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Training set,Search engine,Synthetic data,Artificial intelligence,Invariant (mathematics),Artificial neural network,Mathematics,Machine learning,Adversarial system,Reinforcement learning |
DocType | Volume | Citations |
Journal | abs/1804.06909 | 0 |
PageRank | References | Authors |
0.34 | 6 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| John Moore | 1 | 1 | 1.74 |
| Joel Pfeiffer | 2 | 0 | 0.34 |
| Kai Wei | 3 | 143 | 9.34 |
| Rishabh K. Iyer | 4 | 34 | 8.80 |
| Denis Charles | 5 | 171 | 8.73 |
| Ran Gilad-Bachrach | 6 | 10 | 1.89 |
| Levi Boyles | 7 | 55 | 4.50 |
| Eren Manavoglu | 8 | 0 | 1.69 |