Conditional minimax estimates

Regularized Minimax Conditional Entropy for Crowdsourcing

There is a rapidly increasing interest in crowdsourcing for data labeling. By crowdsourcing, a large number of labels can be often quickly gathered at low cost. However, the labels provided by the crowdsourcing workers are usually not of high quality. In this paper, we propose a minimax conditional entropy principle to infer ground truth from noisy crowdsourced labels. Under this principle, we ...

Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy

We propose a method to aggregate noisy ordinal labels collected from a crowd of workers or annotators. Eliciting ordinal labels is important in tasks such as judging web search quality and rating products. Our method is motivated by the observation that workers usually have difficulty distinguishing between two adjacent ordinal classes whereas distinguishing between two classes which are far aw...

A Minimax Surrogate Loss Approach to Conditional Difference Estimation

We present a new machine learning approach to estimate personalized treatment effects in the classical potential outcomes framework with binary outcomes. To overcome the problem that both treatment and control outcomes for the same unit are required for supervised learning, we propose surrogate loss functions that incorporate both treatment and control data. The new surrogates yield tighter bou...

Bayes and Minimax Estimates for Quadratic Loss Functions

Conditional survival estimates for cancer patients