  We propose a new efficient online algorithm to learn the parameters governing
the purchasing behavior of a utility maximizing buyer, who responds to prices,
in a repeated interaction setting. The key feature of our algorithm is that it
can learn even non-linear buyer utility while working with arbitrary price
constraints that the seller may impose. This overcomes a major shortcoming of
previous approaches, which use unrealistic prices to learn these parameters
making them unsuitable in practice.
