Results 61 to 70 of about 11,284 (160)
Algorithms for Approximate Minimization of the Difference Between Submodular Functions, with Applications [PDF]
, 2012 We extend the work of Narasimhan and Bilmes [30] for minimizing set functions representable as a difference between submodular functions. Similar to [30], our new algorithms are guaranteed to monotonically reduce the objective function at every step.
Bilmes, Jeff, Iyer, Rishabh
core +1 more source
Optimal Selling Mechanisms With Endogenous Seller Outside Offers
International Economic Review, EarlyView.ABSTRACT We examine a two‐stage selling mechanism design problem, where the buyer makes her report and the seller endogenously decides his effort (hidden investment) to generate a possibly better outside offer. The optimal mechanism shows that the seller's effort depends on the reported value of the buyer; a higher value lowers the seller's incentive ...
Xiaogang Che +3 more
wiley +1 more source
Same/Other/All K‐Fold Cross‐Validation for Estimating Similarity of Patterns in Data Subsets
Statistical Analysis and Data Mining: An ASA Data Science Journal, Volume 19, Issue 1, February 2026.ABSTRACT In many real‐world applications of machine learning, we are interested to know if it is possible to train on the data that we have gathered so far, and obtain accurate predictions on a new test data subset that is qualitatively different in some respect (time period, geographic region, etc.).
Toby Dylan Hocking +5 more
wiley +1 more source
On Some Algorithmic and Structural Results on Flames
Journal of Graph Theory, Volume 110, Issue 4, Page 392-397, December 2025.ABSTRACT A directed graph F with a root node r is called a flame if for every vertex v other than r the local edge‐connectivity value λ F ( r , v ) from r to v is equal to ϱ F ( v ), the in‐degree of v. It is a classic, simple and beautiful result of Lovász [4] that every digraph D with a root node r has a spanning subgraph F that is a flame and the λ (
Dávid Szeszlér
wiley +1 more source
Structured sparsity-inducing norms through submodular functions [PDF]
, 2010 Sparse methods for supervised learning aim at finding good linear predictors from as few variables as possible, i.e., with small cardinality of their supports.
Bach, Francis
core +8 more sources
Control Node Placement and Structural Controllability of Water Quality Dynamics in Drinking Networks
Water Resources Research, Volume 61, Issue 12, December 2025.Abstract Chlorine, the most widely used disinfectant, needs to be adequately distributed in water distribution networks (WDNs) to maintain consistent residual levels and ensure safe water. This is performed through control node injections at the treatment plant and via booster stations distributed across the WDNs.
Salma M. Elsherif, Ahmad F. Taha
wiley +1 more source
Data and Competition: A Simple Framework
The RAND Journal of Economics, Volume 56, Issue 4, Page 494-510, Winter 2025.ABSTRACT Does enhanced access to data foster or hinder competition among firms? Using a competition‐in‐utility framework that encompasses many situations where firms use data, we model data as a revenue‐shifter and identify two opposite effects: a mark‐up effect according to which data induces firms to compete harder, and a surplus‐extraction effect ...
Alexandre de Cornière, Greg Taylor
wiley +1 more source
Monotonic Decompositions of Submodular Set Functions
26 ...
Bérczi, Kristóf +4 more
openaire +2 more sources
Multivariate Incomplete Information in the Mixture Model of Contests
The RAND Journal of Economics, Volume 56, Issue 4, Page 607-624, Winter 2025.ABSTRACT A general mixture model of contests is introduced, combining stochastic performance and multivariate incomplete information. Performance is determined by a mixture distribution with endogenous weights on a good and bad distribution, respectively.
René Kirkegaard
wiley +1 more source
Shaping Level Sets with Submodular Functions
, 2010 We consider a class of sparsity-inducing regularization terms based on submodular functions. While previous work has focused on non-decreasing functions, we explore symmetric submodular functions and their \lova extensions. We show that the Lovasz extension may be seen as the convex envelope of a function that depends on level sets (i.e., the set of ...
openaire +3 more sources