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Found 12 papers, 0 papers with code
Learning from Viral Content
We study learning on social media with an equilibrium model of users interacting with shared news stories.
KL-divergence Based Deep Learning for Discrete Time Model
Neural Network (Deep Learning) is a modern model in Artificial Intelligence and it has been exploited in Survival Analysis.
Observability, Dominance, and Induction in Learning Models
Learning models do not in general imply that weakly dominated strategies are irrelevant or justify the related concept of "forward induction," because rational agents may use dominated strategies as experiments to learn how opponents play, and may not have enough data to rule out a strategy that opponents never use.
Private Private Information
A private private information structure delivers information about an unknown state while preserving privacy: An agent's signal contains information about the state but remains independent of others' sensitive or private information.
Screening $p$-Hackers: Dissemination Noise as Bait
Uninformed $p$-hackers, who are fully ignorant of the true mechanism and engage in data mining, often fall for baits.
Evolutionarily Stable (Mis)specifications: Theory and Applications
Toward explaining the persistence of biased inferences, we propose a framework to evaluate competing (mis)specifications in strategic settings.
Aggregative Efficiency of Bayesian Learning in Networks
In a class of networks where agents move in generations and observe the previous generation, we quantify the information loss with an aggregative efficiency index.
An Experiment on Network Density and Sequential Learning
A network determines the observable predecessors, and we compare subjects' accuracy on sparse and dense networks.
Dynamic Information Design with Diminishing Sensitivity Over News
Diminishing sensitivity induces a preference over news skewness: gradual bad news, one-shot good news is worse than one-shot resolution, which is in turn worse than gradual good news, one-shot bad news.
Covariance-Insured Screening
Modern bio-technologies have produced a vast amount of high-throughput data with the number of predictors far greater than the sample size.
Mislearning from Censored Data: The Gambler's Fallacy and Other Correlational Mistakes in Optimal-Stopping Problems
When agents wrongly expect systematic reversals (the "gambler's fallacy"), they understate the likelihood of consecutive below-average draws, converge to over-pessimistic beliefs about the distribution's mean, and stop too early.
Classification with Ultrahigh-Dimensional Features
Although much progress has been made in classification with high-dimensional features \citep{Fan_Fan:2008, JGuo:2010, CaiSun:2014, PRXu:2014}, classification with ultrahigh-dimensional features, wherein the features much outnumber the sample size, defies most existing work.
