Traditional discriminative Machine-Learning approaches often formulate problems as optimization of a parameterized function mapping from a given input space to some desired output space.
While this formulation is applicable to many theoretical and practical problems, it is inherently reliant on the assumption that the dataset X is constant. The world around us is abundant with scenarios where this is either not the case, or where dropping this assumption could help achieve better solutions.
Namely, in many scenarios one can gain added benefit from starting from one step before the data is acquired - if the way data is perceived by our sensors can be modeled, with a correct model it can be parameterized. If it can be parameterized, with the right mathematical tools (e.g. Deep Learning), it can be optimized to gives us datasets more useful for achieving the downstream task at
hand. As we are solving task-specific different optimization of the acquired data, a joint optimization of the downstream task model and data acquisition model is usually called for. This type of joint optimization calls for the delicate balancing in optimization of the two systems (acquisition and downstream), bringing upon a rich and interesting range of problems, coming from a diverse set of fields in science and engineering.
In our research we identify and study a collection of such problems, and offer modeling and optimization schemes to solve them.
