Michael Katz, Parikshit Ram, Shirin Sohrabi, Octavian Udrea |
PosterID:
72
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| While Machine Learning has achieved considerable success in recent years, this success crucially relies on human experts to select appropriate features, workflows, algorithms with their hyper-parameters, etc. Automating the role of the human expert has seen some attention from the Machine Learning community, with a dedicated workshop running since 2014 at one of the top Machine Learning conferences. In this work, we propose to exploit multiple AI Planning tools for automating the human expert, generating Machine Learning pipelines automatically. We start from a knowledge about possible valid pipelines encoded as context-free grammar, translate the problem of generating the corresponding language into Hierarchical Task Network (HTN) Planning model, further translate the HTN Planning model into a classical planning model. We use existing planners to produce multiple plans for the classical planning task, translate these plans into Machine Learning pipelines, train and evaluate these pipelines. Based on pipelines' accuracy feedback we update the classical planning model to improve the quality of pipelines obtained in next iterations. Using planning tools allows us to exploit the flexibility of model update instead of solution modification. We present an application that helps users to focus pipelines' exploration process by allowing to encode additional constraints on desired pipelines. Our experimental evaluation shows the feasibility of using planning techniques in this context. | |
| Canb | 10/29/2020, 10:00 – 11:00 |
| 10/31/2020, 03:00 – 04:00 |
| Paris | 10/29/2020, 00:00 – 01:00 |
| 10/30/2020, 17:00 – 18:00 |
| NYC | 10/28/2020, 19:00 – 20:00 |
| 10/30/2020, 12:00 – 13:00 |
| LA | 10/28/2020, 16:00 – 17:00 |
| 10/30/2020, 09:00 – 10:00 |
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