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- Madeleine Udell, Corinne Horn, Reza Bosagh Zadeh, Stephen P. Boyd
- Foundations and Trends in Machine Learning
- 2016

Principal components analysis (PCA) is a well-known technique for approximating a data set represented by a matrix by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets consisting of numerical, Boolean, categorical, ordinal, and other data types. This framework encompasses many well known techniques in data analysis, such as… (More)

- Elsa W Birch, Madeleine Udell, Markus W Covert
- Journal of theoretical biology
- 2014

We present two modifications of the flux balance analysis (FBA) metabolic modeling framework which relax implicit assumptions of the biomass reaction. Our flexible flux balance analysis (flexFBA) objective removes the fixed proportion between reactants, and can therefore produce a subset of biomass reactants. Our time-linked flux balance analysis (tFBA)… (More)

- Damek Davis, Brent Edmunds, Madeleine Udell
- NIPS
- 2016

We introduce the Stochastic Asynchronous Proximal Alternating Linearized Minimization (SAPALM) method, a block coordinate stochastic proximal-gradient method for solving nonconvex, nonsmooth optimization problems. SAPALM is the first asynchronous parallel optimization method that provably converges on a large class of nonconvex, nonsmooth problems. We prove… (More)

- Madeleine Udell, Karanveer Mohan, David Zeng, Jenny Hong, Steven Diamond, Stephen P. Boyd
- 2014 First Workshop for High Performance…
- 2014

This paper describes Convex, a convex optimization modeling framework in Julia. Convex translates problems from a user-friendly functional language into an abstract syntax tree describing the problem. This concise representation of the global structure of the problem allows Convex to infer whether the problem complies with the rules of disciplined convex… (More)

- Joel A. Tropp, Alp Yurtsever, Madeleine Udell, Volkan Cevher
- ArXiv
- 2017

Several important applications, such as streaming PCA and semidefinite programming, involve a large-scale positive-semidefinite (psd) matrix that is presented as a sequence of linear updates. Because of storage limitations, it may only be possible to retain a sketch of the psd matrix. This paper develops a new algorithm for fixed-rank psd approximation from… (More)

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)

- Paea LePendu, Yi Liu, Srinivasan Iyer, Madeleine R. Udell, Nigam H. Shah
- AMIA Joint Summits on Translational Science…
- 2012

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)

- Madeleine Udell
- 2014

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 convexified 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)

- Edward H. Lee, Madeleine Udell, S. Simon Wong
- 2015 IEEE International Conference on Acoustics…
- 2015

We present matrix factorization as an enabling technique for analog-to-digital matrix multiplication (AD-MM). We show that factorization in the analog domain increases the total precision of AD-MM in precision-limited analog multiplication, reduces the number of analog-to-digital (A/D) conversions needed for overcomplete matrices, and avoids unneeded… (More)

- Alp Yurtsever, Madeleine Udell, Joel A. Tropp, Volkan Cevher
- AISTATS
- 2017

This paper concerns a fundamental class of convex matrix optimization problems. It presents the first algorithm that uses optimal storage and provably computes a lowrank approximation of a solution. In particular, when all solutions have low rank, the algorithm converges to a solution. This algorithm, SketchyCGM, modifies a standard convex optimization… (More)