Výsledky vyhledávání - "Edward McFowland"

Pattern detection in the activation space for identifying synthesized content

Autor: Celia Cintas, Skyler Speakman, Girmaw Abebe Tadesse, Victor Akinwande, Edward McFowland, Komminist Weldemariam

Publikováno v: Pattern Recognition Letters. 153:207-213

Generative Adversarial Networks (GANs) have recently achieved unprecedented success in photo-realistic image synthesis from low-dimensional random noise. The ability to synthesize high-quality content at a large scale brings potential risks as the ge

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc14782a4c858a2ba3bcfa7520f209d6
https://doi.org/10.1016/j.patrec.2021.12.007

Zobrazit plný text záznamu

So, Who Likes You? Evidence from a Randomized Field Experiment

Autor: Ravi Bapna, Edward McFowland, Probal Mojumder, Jui Ramaprasad, Akhmed Umyarov

Publikováno v: Management Science.

With one-third of marriages in the United States beginning online, online dating platforms have become important curators of the modern social fabric. Prior work on online dating has elicited two critical frictions in the heterosexual dating market.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c32ad81bae34815bc931535102f264cf
https://doi.org/10.1287/mnsc.2022.4576

Zobrazit plný text záznamu

A Prescriptive Analytics Framework for Optimal Policy Deployment Using Heterogeneous Treatment Effects

Autor: Tianshu Sun, Edward McFowland III

Publikováno v: MIS Quarterly. 45:1807-1832

We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility-maximiz

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f658b26a4c93ed2808001b08c06cffa4
https://doi.org/10.25300/misq/2021/15684

Zobrazit plný text záznamu

Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations

Autor: Cosma Rohilla Shalizi, Edward McFowland

Publikováno v: Journal of the American Statistical Association. 118:707-718

Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, i.e., with a node's network partners being informative about the node's attributes and th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0286aaa3ce7d61a5cb36337e8dc08b65
https://doi.org/10.1080/01621459.2021.1953506

Zobrazit plný text záznamu

Commentary on 'Causal Decision Making and Causal Effect Estimation Are Not the Same… and Why It Matters'

Autor: Edward McFowland III

Publikováno v: INFORMS Journal on Data Science. 1:21-22

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::76f98f77e89bdb5f070242f42cb91375
https://doi.org/10.1287/ijds.2021.0010

Zobrazit plný text záznamu

Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error

Autor: William Ogallo, Komminist Weldemariam, Skyler Speakman, Celia Cintas, Victor Akinwande, Edward McFowland, Srihari Sridharan

Publikováno v: IJCAI
Scopus-Elsevier

Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f0c6fd5c2899a8f80883d9c1027c5435
https://doi.org/10.24963/ijcai.2020/122

Zobrazit plný text záznamu

Penalized Fast Subset Scanning

Autor: Sriram Somanchi, Edward McFowland, Skyler Speakman, Daniel B. Neill

Publikováno v: Journal of Computational and Graphical Statistics. 25:382-404

We present the penalized fast subset scan (PFSS), a new and general framework for scalable and accurate pattern detection. PFSS enables exact and efficient identification of the most anomalous subsets of the data, as measured by a likelihood ratio sc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c47779aa318e29af64e625252cccf8db
https://doi.org/10.1080/10618600.2015.1029578

Zobrazit plný text záznamu

Automated Local Regression Discontinuity Design Discovery

Autor: William Herlands, Edward McFowland, Daniel B. Neill, Andrew Gordon Wilson

Publikováno v: KDD

Inferring causal relationships in observational data is crucial for understanding scientific and social processes. We develop the first statistical machine learning approach for automatically discovering regression discontinuity designs (RDDs), a qua

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3cfe2d9dce2ae74ae01fd60b9a34d2cd
https://doi.org/10.1145/3219819.3219982

Zobrazit plný text záznamu

Scalable Detection of Anomalous Patterns With Connectivity Constraints

Autor: Edward McFowland, Skyler Speakman, Daniel B. Neill

Publikováno v: Journal of Computational and Graphical Statistics. 24:1014-1033

We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can e

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::74d4163c67dbe72efae39ff53807cc8d
https://doi.org/10.1080/10618600.2014.960926

Zobrazit plný text záznamu

Vyhledávací nástroje:

Upřesnit hledání