转载：Why machine learning algorithms are hard to tune and how to fix it
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Key point
- We often optimize a Linear combinations of losses and hope to simultaneously reduce both \\(L_1\\) and \\(L_0\\) losses, but that this linear combination is actually precarious and treacherous.
- Authours showed that when the pareto curve is concave, only one of these two losses was considered and this linear combination was valid in convex pareto curve.
- In fact, We can\'t figure out the property of the pareto curve.
- We can reformulate the linear combination losses as a constraint optimization problem, e.g., restricting \\(L_0\\leq \\epsilon\\)
- By doing so, authors gave three possible solutions.
References
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