  The classical 'buy or rent' ski-rental problem was recently considered in the
setting where multiple experts (such as Machine Learning algorithms) advice on
the length of the ski season. Here, robust algorithms were developed with
improved theoretical performance over adversarial scenarios where such expert
predictions were unavailable. We consider a variant of this problem which we
call the 'sequential ski-rental' problem. Here, a sequence of ski-rental
problems has to be solved in an online fashion where both the buy cost and the
length of the ski season are unknown to the learner. The learner has access to
two sets of experts, one set who advise on the true cost of buying the ski and
another set who advise on the length of the ski season. Under certain
stochastic assumptions on the experts who predict the buy costs, we develop
online algorithms and prove regret bounds for the same. Our experimental
evaluations confirm our theoretical results.
