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.

MA TLAB, as shown in Table 2. To enhance the sensing quality, we have aggregated five adjacent frames into a new frame for use. WiFi CSI data, there are some "-inf" values in some sequences. The "-inf" number comes from the To facilitate the users, we have embedded these processing codes into our dataset tool. When the user loads our WiFi CSI data, these numbers will be handled by linear interpolation. As presented in Section 4.3, we provide the temporal Each sequence is annotated by at least 5 human annotators.

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.

We present PDLP, a practical first-order method for linear programming (LP) that can solve to the high levels of accuracy that are expected in traditional LP applications.