5 citations · 13 across the 5 of their papers we have counts for
9 papers
Toward Annotator Group Bias in Crowdsourcing
Haochen Liu, Joseph Thekinen, Sinem Mollaoglu +5
Crowdsourcing has emerged as a popular approach for collecting annotated data to train supervised machine learning models. However, annotator bias can lead to defective annotations…
AutoLoss: Automated Loss Function Search in Recommendations
Xiangyu Zhao, Haochen Liu, Wenqi Fan +3
Designing an effective loss function plays a crucial role in training deep recommender systems. Most existing works often leverage a predefined and fixed loss function that could l…
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial Learning
Haochen Liu, Wentao Wang, Yiqi Wang +3
Dialogue systems play an increasingly important role in various aspects of our daily life. It is evident from recent research that dialogue systems trained on human conversation da…
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly Detection
Zhiwei Wang, Zhengzhang Chen, Jingchao Ni +3
Discrete event sequences are ubiquitous, such as an ordered event series of process interactions in Information and Communication Technology systems. Recent years have witnessed in…
Memory-efficient Embedding for Recommendations
Xiangyu Zhao, Haochen Liu, Hui Liu +6
Practical large-scale recommender systems usually contain thousands of feature fields from users, items, contextual information, and their interactions. Most of them empirically al…
Learning Multi-level Dependencies for Robust Word Recognition
Zhiwei Wang, Hui Liu, Jiliang Tang +3
Robust language processing systems are becoming increasingly important given the recent awareness of dangerous situations where brittle machine learning models can be easily broken…