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Much of the recent progress has been achieved by taking advantage of continuous relaxations of the system, which are not always available or even possible.

We investigate the problem of reliably assessing group fairness when labeled examples are few but unlabeled examples are plentiful.

However, existing solutions which mainly rely on cameras and wearable devices are either privacy intrusive or inconvenient to use.

Policy optimization, i.e. algorithms that learn to make sequential decisions by local search on the agent's policy directly, is a widely used class of algorithms in reinforcement learning [40, 44, 45].

Stochastic gradient methods have been a widespread practice in machine learning.