Publications
Publications
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(60)
- Faculty Publications (10)
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- 2023
- Working Paper
Black-box Training Data Identification in GANs via Detector Networks
By: Lukman Olagoke, Salil Vadhan and Seth Neel
Since their inception Generative Adversarial Networks (GANs) have been popular generative models across images, audio, video, and tabular data. In this paper we study whether given access to a trained GAN, as well as fresh samples from the underlying distribution, if... View Details
- July 2023 (Revised July 2023)
- Background Note
Generative AI Value Chain
By: Andy Wu and Matt Higgins
Generative AI refers to a type of artificial intelligence (AI) that can create new content (e.g., text, image, or audio) in response to a prompt from a user. ChatGPT, Bard, and Claude are examples of text generating AIs, and DALL-E, Midjourney, and Stable Diffusion are... View Details
- 2023
- Article
Estimating Causal Peer Influence in Homophilous Social Networks by Inferring Latent Locations.
By: Edward McFowland III and Cosma Rohilla Shalizi
Social influence cannot be identified from purely observational data on social networks, because such influence is generically confounded with latent homophily, that is, with a node’s network partners being informative about the node’s attributes and therefore its... View Details
- March 2021
- Article
The Impact of the General Data Protection Regulation on Internet Interconnection
By: Ran Zhuo, Bradley Huffaker, KC Claffy and Shane Greenstein
The Internet comprises thousands of independently operated networks, where bilaterally negotiated interconnection agreements determine the flow of data between networks. The European Union’s General Data Protection Regulation (GDPR) imposes strict restrictions on... View Details
- Article
Incorporating Interpretable Output Constraints in Bayesian Neural Networks
By: Wanqian Yang, Lars Lorch, Moritz Graule, Himabindu Lakkaraju and Finale Doshi-Velez
Domains where supervised models are deployed often come with task-specific constraints, such as prior expert knowledge on the ground-truth function, or desiderata like safety and fairness. We introduce a novel probabilistic framework for reasoning with such constraints... View Details
- 2019
- Working Paper
The Impact of the General Data Protection Regulation on Internet Interconnection
By: Ran Zhuo, Bradley Huffaker, KC Claffy and Shane Greenstein
The Internet comprises thousands of independently operated networks, where bilaterally negotiated interconnection agreements determine the flow of data between networks. The European Union’s General Data Protection Regulation (GDPR) imposes strict restrictions on... View Details
- 2019
- Article
Structural Balance Emerges and Explains Performance in Risky Decision-Making
By: Omid Askarisichani, Jacqueline N. Lane, Francesco Bullo, Noah E. Friedkin, Ambuj K. Singh and Brian Uzzi
Polarization affects many forms of social organization. A key issue focuses on which affective relationships are prone to change and how their change relates to performance. In this study,
we analyze a financial institutional over a two-year period that employed 66... View Details
- March 2018
- Case
TrustSphere: Building a Market for Relationship Analytics
By: Boris Groysberg and Katherine Connolly Baden
Manish Goel was the CEO of TrustSphere, a seven-year-old company in the data analytics industry that focused squarely on relationship analytics, a space in which TrustSphere was pioneering a unique technology and solutions in the areas of sales, risk, and people... View Details
- February 2016
- Article
After The Break-Up: The Relational and Reputational Consequences of Withdrawals from Venture Capital Syndicates
By: Pavel Zhelyazkov and Ranjay Gulati
Traditional research has long treated reputation as an egocentric attribute, typically described as an intangible asset directly shaped by the focal actor's track record. We argue, however, that reputation is dyadic: that an actor can have different reputations with... View Details
- Research Summary
Overview
By: Eva Ascarza
Professor Ascarza’s research primarily focuses on providing researchers and marketers a better understanding of how to manage customer retention so as to reduce churn and increase firm’s profitability. She addresses these issues by building empirical models of customer... View Details