7 papers
T-ARC: Topology-Aware Randomized Clustering via Distributionally Robust Stochastic Block Models
Serena Grazia De Benedictis, Andersen Ang, Nicoletta Del Buono +2
In this work, we introduce a new clustering method, namely T-ARC (Topology-Aware Randomized Clustering), that corrects the geometric bias of K-means by embedding topological inform…
Binno: A 1st-order method for Bi-level Nonconvex Nonsmooth Optimization for Matrix Factorizations
Laura Selicato, Flavia Esposito, Andersen Ang
Nonconvex and nonsmooth bi-level optimization poses critical theoretical challenges, while arising in several applications. In this work, we develop a method for nonconvex, nonsmoo…
Optimal Network Pricing for Oblivious Users under Projected Decision-Dependent Distributions
Yixuan Li, Andersen Ang, Sebastian Stein
Efficient large-scale network allocation requires data-driven pricing mechanisms that internalize stochastic, nonlinear user behavior. We move beyond the classic fully strategic ag…
Optimization on the Oblique Manifold for Sparse Simplex Constraints via Multiplicative Updates
Flavia Esposito, Andersen Ang
Low-rank optimization problems with sparse simplex constraints involve variables that must satisfy nonnegativity, sparsity, and sum-to-1 conditions, making their optimization parti…
Sparse Hyperparametric Itakura-Saito Nonnegative Matrix Factorization via Bi-Level Optimization
Laura Selicato, Flavia Esposito, Andersen Ang +2
The selection of penalty hyperparameters is a critical aspect in Nonnegative Matrix Factorization (NMF), since these values control the trade-off between reconstruction accuracy an…
Sum-of-norms regularized Nonnegative Matrix Factorization
Andersen Ang, Waqas Bin Hamed, Hans De Sterck
When applying nonnegative matrix factorization (NMF), the rank parameter is generally unknown. This rank, called the nonnegative rank, is usually estimated heuristically since comp…
Chordal-NMF with Riemannian Multiplicative Update
Flavia Esposito, Andersen Ang
Nonnegative Matrix Factorization (NMF) is the problem of approximating a given nonnegative matrix M through the product of two nonnegative low-rank matrices W and H. Traditionally…