These Q values are often objectives that offline algorithms aim to approximate.

Weintroduceaweak versionofexp-concavity; see Assumption (A2). Let 0 2 0 beapointinthegrid 0 minimizing D( , 0).

In the total least squares problem, one is given an m n matrix A, and an m d matrix B, and one seeks to "correct" both A and B, obtaining matrices  and B, so that there exists an X satisfying the equation ÂX = B. Typically the problem is overconstrained, meaning that m max(n, d).

Optimization is an integral part of most machine learning systems and most numerical optimization schemes relyonthecomputation ofderivatives.

However, these decisions may be imperfect due to limited experience, implicit biases, or faulty probabilistic reasoning.

Neural Information Processing Systems http://nips.cc/

Wepropose alearning algorithm which accounts for potential biases held by external decision-makers in a system.

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