Future projection of climate is typically obtained by combining outputs from multiple Earth System Models (ESMs) for several climate variables such as temperature and precipitation. While IPCC has traditionally used a simple model output average, recent work has illustrated potential advantages of using a multitask learning (MTL) framework for projections of individual climate variables. In this paper we introduce a framework for hierarchical multitask learning (HMTL) with two levels of tasks such that each super-task, i.e., task at the top level, is itself a multitask learning problem over sub-tasks. For climate projections, each super-task focuses on projections of specific climate variables spatially using an MTL formulation. For the proposed HMTL approach, a group lasso regularization is added to couple parameters across the super-tasks, which in the climate context helps exploit relationships among the behavior of different climate variables at a given spatial location. We show that some recent works on MTL based on learning task dependency structures can be viewed as special cases of HMTL. Experiments on synthetic and real climate data show that HMTL produces better results than decoupled MTL methods applied separately on the super-tasks and HMTL significantly outperforms baselines for climate projection.
|Original language||English (US)|
|Number of pages||7|
|State||Published - 2017|
|Event||31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States|
Duration: Feb 4 2017 → Feb 10 2017
|Other||31st AAAI Conference on Artificial Intelligence, AAAI 2017|
|Period||2/4/17 → 2/10/17|
Bibliographical noteFunding Information:
FJVZ thanks CNPq for the financial support (Process no. 309115/2014-0).
We thank the anonymous reviewers for their valuable comments. AB was supported by NSF grants IIS-1563950, IIS-1447566, IIS-1447574, IIS-1422557, CCF-1451986, CNS-1314560, IIS-0953274, IIS-1029711, NASA grant NNX12AQ39A, and gifts from Adobe, IBM, and Yahoo.