  In this paper, we consider the revealed preferences problem from a learning
perspective. Every day, a price vector and a budget is drawn from an unknown
distribution, and a rational agent buys his most preferred bundle according to
some unknown utility function, subject to the given prices and budget
constraint. We wish not only to find a utility function which rationalizes a
finite set of observations, but to produce a hypothesis valuation function
which accurately predicts the behavior of the agent in the future. We give
efficient algorithms with polynomial sample-complexity for agents with linear
valuation functions, as well as for agents with linearly separable, concave
valuation functions with bounded second derivative.
