122 citations · 553 across the 26 of their papers we have counts for
5 papers · 1 filter
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness
Tong Wang, Maytal Saar-Tsechansky
We propose a model-agnostic approach for mitigating the prediction bias of a black-box decision-maker, and in particular, a human decision-maker. Our method detects in the feature…
An Empirical Study on Neural Keyphrase Generation
Rui Meng, Xingdi Yuan, Tong Wang +3
Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. Mode…
Same-Day Delivery with Fairness
Xinwei Chen, Tong Wang, Barrett W. Thomas +1
The demand for same-day delivery (SDD) has increased rapidly in the last few years and has particularly boomed during the COVID-19 pandemic. The fast growth is not without its chal…
Exploring and Predicting Transferability across NLP Tasks
Tu Vu, Tong Wang, Tsendsuren Munkhdalai +5
Recent advances in NLP demonstrate the effectiveness of training large-scale language models and transferring them to downstream tasks. Can fine-tuning these models on tasks other…
Interpretable Companions for Black-Box Models
Danqing Pan, Tong Wang, Satoshi Hara
We present an interpretable companion model for any pre-trained black-box classifiers. The idea is that for any input, a user can decide to either receive a prediction from the bla…