12 citations · 21 across the 11 of their papers we have counts for
5 papers · 1 filter
Near-Optimal Experimental Design Under the Budget Constraint in Online Platforms
Yongkang Guo, Yuan Yuan, Jinshan Zhang +3
A/B testing, or controlled experiments, is the gold standard approach to causally compare the performance of algorithms on online platforms. However, conventional Bernoulli randomi…
Principled Evaluation with Human Labels: One Rater at a Time and Rater Equivalence
Paul Resnick, Yuqing Kong, Grant Schoenebeck +1
In many classification tasks, there is no definitive ground truth, only human judgments that may disagree. We address two challenges that arise in such settings: (1) how to use hum…
L_DMI: An Information-theoretic Noise-robust Loss Function
Yilun Xu, Peng Cao, Yuqing Kong +1
Accurately annotating large scale dataset is notoriously expensive both in time and in money. Although acquiring low-quality-annotated dataset can be much cheaper, it often badly d…
Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds
Peng Cao, Yilun Xu, Yuqing Kong +1
Eliciting labels from crowds is a potential way to obtain large labeled data. Despite a variety of methods developed for learning from crowds, a key challenge remains unsolved: \em…
Water from Two Rocks: Maximizing the Mutual Information
Yuqing Kong, Grant Schoenebeck
We build a natural connection between the learning problem, co-training, and forecast elicitation without verification (related to peer-prediction) and address them simultaneously…