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Oral
Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment
Chen Huang · Shuangfei Zhai · Walter Talbott · Miguel Angel Bautista Martin · Shih-Yu Sun · Carlos Guestrin · Joshua M Susskind

Wed Jun 12 04:25 PM -- 04:30 PM (PDT) @ Hall A
in Deep Learning Algorithms »

In most machine learning training paradigms a fixed, often handcrafted, loss function is assumed to be a good proxy for an underlying evaluation metric. In this work we assess this assumption by meta-learning an adaptive loss function to directly optimize the evaluation metric. We propose a sample efficient reinforcement learn- ing approach for adapting the loss dynamically during training. We empirically show how this formulation improves performance by simultaneously optimizing the evaluation metric and smoothing the loss land- scape. We verify our method in metric learning and classification scenarios, showing consider- able improvements over the state-of-the-art on a diverse set of tasks. Importantly, our method is applicable to a wide range of loss functions and evaluation metrics. Furthermore, the learned policies are transferable across tasks and data, demonstrating the versatility of the method.

[ Slides]  [ Video] 

Author Information

Chen Huang (Apple Inc.)
Shuangfei Zhai (Apple)
Walter Talbott (Apple)
Miguel Angel Bautista Martin (Apple Inc.)
Shih-Yu Sun (Apple)
Carlos Guestrin (Apple & Univesity of Washington)
Joshua M Susskind (Apple, Inc.)

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