Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable Unidirectional Pareto Optimality for Multi-Task Learning with
  Constraints   Multi-objective optimization (MOO) problems require balancing competing
objectives, often under constraints. The Pareto optimal solution set defines
all possible optimal trade-offs over such objectives. In this work, we present
a novel method for Pareto-front learning: inducing the full Pareto manifold at
train-time so users can pick any desired optimal trade-off point at run-time.
Our key insight is to exploit Fritz-John Conditions for a novel guided double
gradient descent strategy. Evaluation on synthetic benchmark problems allows us
to vary MOO problem difficulty in controlled fashion and measure accuracy vs.
known analytic solutions. We further test scalability and generalization in
learning optimal neural model parameterizations for Multi-Task Learning (MTL)
on image classification. Results show consistent improvement in accuracy and
efficiency over prior MTL methods as well as techniques from operations
research.

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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph
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The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: stat
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The categories of this paper are: astro-ph
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The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: physics
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The categories of this paper are: hep-lat
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The categories of this paper are: quant-ph