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Found 6 papers, 3 papers with code
Optimal Decision Making Under Strategic Behavior
Using this characterization, we first show that, in general, we cannot expect to find optimal decision policies in polynomial time and there are cases in which deterministic policies are suboptimal.
Consequential Ranking Algorithms and Long-term Welfare
Can we design ranking models that understand the consequences of their proposed rankings and, more importantly, are able to avoid the undesirable ones?
Optimizing Human Learning
Can we find the optimal reviewing schedule to maximize the benefits of spaced repetition?
Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation
Online social networking sites are experimenting with the following crowd-powered procedure to reduce the spread of fake news and misinformation: whenever a user is exposed to a story through her feed, she can flag the story as misinformation and, if the story receives enough flags, it is sent to a trusted third party for fact checking.
Design and Analysis of the NIPS 2016 Review Process
Neural Information Processing Systems (NIPS) is a top-tier annual conference in machine learning.
Distilling Information Reliability and Source Trustworthiness from Digital Traces
Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness.
