  Symbolic regression, the task of extracting mathematical expressions from the
observed data $\{ \vx_i, y_i \}$, plays a crucial role in scientific discovery.
Despite the promising performance of existing methods, most of them conduct
symbolic regression in an \textit{offline} setting. That is, they treat the
observed data points as given ones that are simply sampled from uniform
distributions without exploring the expressive potential of data. However, for
real-world scientific problems, the data used for symbolic regression are
usually actively obtained by doing experiments, which is an \textit{online}
setting. Thus, how to obtain informative data that can facilitate the symbolic
regression process is an important problem that remains challenging.
  In this paper, we propose QUOSR, a \textbf{qu}ery-based framework for
\textbf{o}nline \textbf{s}ymbolic \textbf{r}egression that can automatically
obtain informative data in an iterative manner. Specifically, at each step,
QUOSR receives historical data points, generates new $\vx$, and then queries
the symbolic expression to get the corresponding $y$, where the $(\vx, y)$
serves as new data points. This process repeats until the maximum number of
query steps is reached. To make the generated data points informative, we
implement the framework with a neural network and train it by maximizing the
mutual information between generated data points and the target expression.
Through comprehensive experiments, we show that QUOSR can facilitate modern
symbolic regression methods by generating informative data.
