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The problem of maximizing a sum of sigmoidal functions over a convex constraint set arises in many application areas. This objective captures the idea of decreasing marginal returns to investment, and has applications in mathematical marketing, network bandwidth allocation, revenue optimization, optimal bidding, and lottery design. We define the sigmoidal… (More)

Doctors prescribe drugs for indications that are not FDA approved. Research indicates that 21% of prescriptions filled are for off-label indications. Of those, more than 73% lack supporting scientific evidence. Traditional drug safety alerts may not cover usages that are not FDA approved. Therefore, analyzing patterns of off-label drug usage in the clinical… (More)

We consider the problem of minimizing a sum of non-convex functions over a compact domain, subject to linear inequality and equality constraints. Approximate solutions can be found by solving a convex-ified version of the problem, in which each function in the objective is replaced by its convex envelope. We propose a randomized algorithm to solve the… (More)

- C Udell, R Horn, S Zadeh, Boyd, Madeleine Udell, Corinne Horn +2 others
- 2016

We consider the problem of minimizing a sum of non-convex functions over a compact domain, subject to linear inequality and equality constraints. We consider approximate solutions obtained by solving a convexified problem, in which each function in the objective is replaced by its convex envelope. We propose a randomized algorithm to solve the convexified… (More)

- Madeleine Udell, Reza Takapoui
- 2013

The matrix completion paradigm has received much attention as a solution to the collaborative filtering problem in recommendation systems. The essential idea is that the user-by-item matrix of user preferences may be well modeled as a low rank matrix, and that this assumption may be used to impute unobserved entries, with good performance even when very few… (More)

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