74 citations · 96 across the 10 of their papers we have counts for
5 papers · 1 filter
To Aggregate or Not? Learning with Separate Noisy Labels
Jiaheng Wei, Zhaowei Zhu, Tianyi Luo +3
The rawly collected training data often comes with separate noisy labels collected from multiple imperfect annotators (e.g., via crowdsourcing). A typical way of using these separa…
Compressed Predictive Information Coding
Rui Meng, Tianyi Luo, Kristofer Bouchard
Unsupervised learning plays an important role in many fields, such as artificial intelligence, machine learning, and neuroscience. Compared to static data, methods for extracting l…
The Rich Get Richer: Disparate Impact of Semi-Supervised Learning
Zhaowei Zhu, Tianyi Luo, Yang Liu
Semi-supervised learning (SSL) has demonstrated its potential to improve the model accuracy for a variety of learning tasks when the high-quality supervised data is severely limite…
Machine Truth Serum
Tianyi Luo, Yang Liu
Wisdom of the crowd revealed a striking fact that the majority answer from a crowd is often more accurate than any individual expert. We observed the same story in machine learning…
Learning from LDA using Deep Neural Networks
Dongxu Zhang, Tianyi Luo, Dong Wang +1
Latent Dirichlet Allocation (LDA) is a three-level hierarchical Bayesian model for topic inference. In spite of its great success, inferring the latent topic distribution with LDA…