12 citations · 26 across the 9 of their papers we have counts for
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cs.LG2020
Learnability with Indirect Supervision Signals
Kaifu Wang, Qiang Ning, Dan Roth
Learning from indirect supervision signals is important in real-world AI applications when, often, gold labels are missing or too costly. In this paper, we develop a unified theore…
cs.LG2020
Foreseeing the Benefits of Incidental Supervision
Hangfeng He, Mingyuan Zhang, Qiang Ning +1
Real-world applications often require improved models by leveraging a range of cheap incidental supervision signals. These could include partial labels, noisy labels, knowledge-bas…
cs.LG2019
Partial Or Complete, That's The Question
Qiang Ning, Hangfeng He, Chuchu Fan +1
For many structured learning tasks, the data annotation process is complex and costly. Existing annotation schemes usually aim at acquiring completely annotated structures, under t…