8 citations · 9 across the 3 of their papers we have counts for
3 papers
stat.ML2023
Training Discrete Energy-Based Models with Energy Discrepancy
Tobias Schröder, Zijing Ou, Yingzhen Li +1
Training energy-based models (EBMs) on discrete spaces is challenging because sampling over such spaces can be difficult. We propose to train discrete EBMs with energy discrepancy…
cs.LG2023★ 1 cited
Scalable Infomin Learning
Yanzhi Chen, Weihao Sun, Yingzhen Li +1
The task of infomin learning aims to learn a representation with high utility while being uninformative about a specified target, with the latter achieved by minimising the mutual…
cs.LG2022★ 8 cited
Repairing Neural Networks by Leaving the Right Past Behind
Ryutaro Tanno, Melanie F. Pradier, Aditya Nori +1
Prediction failures of machine learning models often arise from deficiencies in training data, such as incorrect labels, outliers, and selection biases. However, such data points t…