A survey on the densest subgraph problem and its variants
The Densest Subgraph Problem requires us to find, in a given graph, a subset of vertices
whose induced subgraph maximizes a measure of density. The problem has received a …
whose induced subgraph maximizes a measure of density. The problem has received a …
Modeling the AC power flow equations with optimally compact neural networks: Application to unit commitment
Nonlinear power flow constraints render a variety of power system optimization problems
computationally intractable. Emerging research shows, however, that the nonlinear AC …
computationally intractable. Emerging research shows, however, that the nonlinear AC …
Loss functions for discrete contextual pricing with observational data
We study a pricing setting where each customer is offered a contextualized price based on
customer and/or product features. Often only historical sales data are available, so we …
customer and/or product features. Often only historical sales data are available, so we …
Tackling provably hard representative selection via graph neural networks
Representative Selection (RS) is the problem of finding a small subset of exemplars from a
dataset that is representative of the dataset. In this paper, we study RS for attributed graphs …
dataset that is representative of the dataset. In this paper, we study RS for attributed graphs …
Convex Surrogate Loss Functions for Contextual Pricing with Transaction Data
M Biggs - arXiv preprint arXiv:2202.10944, 2022 - arxiv.org
We study an off-policy contextual pricing problem where the seller has access to samples of
prices that customers were previously offered, whether they purchased at that price, and …
prices that customers were previously offered, whether they purchased at that price, and …