  Consider a scenario in which we have a huge labeled dataset ${\cal D}$ and a
limited time to train some given learner using ${\cal D}$. Since we may not be
able to use the whole dataset, how should we proceed? Questions of this nature
motivate the definition of the Time-Constrained Learning Task (TCL): Given a
dataset ${\cal D}$ sampled from an unknown distribution $\mu$, a learner ${\cal
L}$ and a time limit $T$, the goal is to obtain in at most $T$ units of time
the classification model with highest possible accuracy w.r.t. to $\mu$, among
those that can be built by ${\cal L}$ using the dataset ${\cal D}$.
  We propose TCT, an algorithm for the TCL task designed based that on
principles from Machine Teaching. We present an experimental study involving 5
different Learners and 20 datasets where we show that TCT consistently
outperforms two other algorithms: the first is a Teacher for black-box learners
proposed in [Dasgupta et al., ICML 19] and the second is a natural adaptation
of random sampling for the TCL setting. We also compare TCT with Stochastic
Gradient Descent training -- our method is again consistently better.
  While our work is primarily practical, we also show that a stripped-down
version of TCT has provable guarantees. Under reasonable assumptions, the time
our algorithm takes to achieve a certain accuracy is never much bigger than the
time it takes the batch teacher (which sends a single batch of examples) to
achieve similar accuracy, and in some case it is almost exponentially better.
