8 papers
Posterior Calibrated Training on Sentence Classification Tasks
Taehee Jung, Dongyeop Kang, Hua Cheng +2
Most classification models work by first predicting a posterior probability distribution over all classes and then selecting that class with the largest estimated probability. In m…
On Racial Disparities in Recent Fatal Police Shootings
Lucas Mentch
Fatal police shootings in the United States continue to be a polarizing social and political issue. Clear disagreement between racial proportions of victims and nationwide racial d…
Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success
Lucas Mentch, Siyu Zhou
Random forests remain among the most popular off-the-shelf supervised machine learning tools with a well-established track record of predictive accuracy in both regression and clas…
Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization
Taehee Jung, Dongyeop Kang, Lucas Mentch +1
Despite the recent developments on neural summarization systems, the underlying logic behind the improvements from the systems and its corpus-dependency remains largely unexplored.…
Locally Optimized Random Forests
Tim Coleman, Kimberly Kaufeld, Mary Frances Dorn +1
Standard supervised learning procedures are validated against a test set that is assumed to have come from the same distribution as the training data. However, in many problems, th…
Scalable and Efficient Hypothesis Testing with Random Forests
Tim Coleman, Wei Peng, Lucas Mentch
Throughout the last decade, random forests have established themselves as among the most accurate and popular supervised learning methods. While their black-box nature has made the…