DeepAI AI Chat
Log In Sign Up

Ballpark Learning: Estimating Labels from Rough Group Comparisons

06/30/2016
by   Tom Hope, et al.
Hebrew University of Jerusalem
0

We are interested in estimating individual labels given only coarse, aggregated signal over the data points. In our setting, we receive sets ("bags") of unlabeled instances with constraints on label proportions. We relax the unrealistic assumption of known label proportions, made in previous work; instead, we assume only to have upper and lower bounds, and constraints on bag differences. We motivate the problem, propose an intuitive formulation and algorithm, and apply our methods to real-world scenarios. Across several domains, we show how using only proportion constraints and no labeled examples, we can achieve surprisingly high accuracy. In particular, we demonstrate how to predict income level using rough stereotypes and how to perform sentiment analysis using very little information. We also apply our method to guide exploratory analysis, recovering geographical differences in twitter dialect.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/13/2017

Ballpark Crowdsourcing: The Wisdom of Rough Group Comparisons

Crowdsourcing has become a popular method for collecting labeled trainin...
02/24/2014

On Learning from Label Proportions

Learning from Label Proportions (LLP) is a learning setting, where the t...
05/16/2023

Learning from Aggregated Data: Curated Bags versus Random Bags

Protecting user privacy is a major concern for many machine learning sys...
01/23/2021

Granular conditional entropy-based attribute reduction for partially labeled data with proxy labels

Attribute reduction is one of the most important research topics in the ...
04/20/2022

Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability

In this paper, we study learning in probabilistic domains where the lear...