activity
20182021
most citedWarm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings

29 citations · 69 across the 9 of their papers we have counts for

collaborators

14 papers

cs.CL2021

Layer-wise Model Pruning based on Mutual Information

Chun Fan, Jiwei Li, Xiang Ao +3

The proposed pruning strategy offers merits over weight-based pruning techniques: (1) it avoids irregular memory access since representations and matrices can be squeezed into thei…

cs.LG202112 cited

Follow the Prophet: Accurate Online Conversion Rate Prediction in the Face of Delayed Feedback

Haoming Li, Feiyang Pan, Xiang Ao +6

The delayed feedback problem is one of the imperative challenges in online advertising, which is caused by the highly diversified feedback delay of a conversion varying from a few…

cs.LG2021

GuideBoot: Guided Bootstrap for Deep Contextual Bandits

Feiyang Pan, Haoming Li, Xiang Ao +4

The exploration/exploitation (E&E) dilemma lies at the core of interactive systems such as online advertising, for which contextual bandit algorithms have been proposed. Bayesian a…

cs.CL202115 cited

Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification

Binzong Geng, Min Yang, Fajie Yuan +3

Lifelong learning capabilities are crucial for sentiment classifiers to process continuous streams of opinioned information on the Web. However, performing lifelong learning is non…

cs.CL20205 cited

Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information

Yuyang Nie, Yuanhe Tian, Yan Song +2

Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the…

cs.CL20205 cited

Improving Robustness and Generality of NLP Models Using Disentangled Representations

Jiawei Wu, Xiaoya Li, Xiang Ao +3

Supervised neural networks, which first map an input to a single representation , and then map to the output label , have achieved remarkable success in a wide range…