We study approximations of optimization problems with probabilistic constraints in which the original distribution of the underlying random vector is replaced with an empirical dis...
Linear Logic is based on the analogy between algebraic linearity (i.e. commutation with sums and scalar products) and the computer science linearity (i.e. calling inputs only once...
We give a randomized protocol for the classic cake cutting problem that guarantees approximate proportional fairness, and with high probability uses a linear number of cuts.
Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizatio...
Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizatio...