More Like this

1. Can you trust what you see online?

   https://doi.org/10.33424/FUTURUM381  

   Wang, Gang; Mink, Jaron (April 2023, Futurum Careers)

   https://futurumcareers.com/can-you-trust-what-you-see-online 

   more » « less
2. What Can You Draw?

   https://doi.org/10.1080/00029890.2022.2128607  

   Frick, Florian; Peng, Fei (January 2023, The American Mathematical Monthly)
3. What makes you unique?

   https://doi.org/10.1214/22-EJS2097  

   Seiler, Benjamin B; Mase, Masayoshi; Owen, Art B (January 2023, Electronic Journal of Statistics)
4. Changing What You See by Changing What You Know: The Role of Attention

   https://doi.org/10.3389/fpsyg.2017.00553  

   Lupyan, Gary (May 2017, Frontiers in Psychology)

   null (Ed.)
5. You Get What You Give: Reciprocally Fair Federated Learning

   Murhekar, A; Song, J; Shahkar, P; Ray_Chaudhury, B; Mehta, R (May 2025, Proceedings of Machine Learning Research (PMLR))

   Federated learning (FL) is a popular collaborative learning paradigm, whereby agents with individual datasets can jointly train an ML model. While higher data sharing improves model accuracy and leads to higher payoffs, it also raises costs associated with data acquisition or loss of privacy, causing agents to be strategic about their data contribution. This leads to undesirable behavior at a Nash equilibrium (NE) such as free-riding, resulting in sub-optimal fairness, data sharing, and welfare. To address this, we design MSHAP, a budget-balanced payment mechanism for FL, that admits Nash equilibria under mild conditions, and achieves reciprocal fairness: where each agent's payoff equals her contribution to the collaboration, as measured by the Shapley share. In addition to fairness, we show that the NE under MSHAP has desirable guarantees in terms of accuracy, welfare, and total data collected. We validate our theoretical results through experiments, demonstrating that MSHAP outperforms baselines in terms of fairness and efficiency. 

   more » « less