62 citations · 138 across the 9 of their papers we have counts for
9 papers
LOGIN: A Large Language Model Consulted Graph Neural Network Training Framework
Yiran Qiao, Xiang Ao, Yang Liu +3
Recent prevailing works on graph machine learning typically follow a similar methodology that involves designing advanced variants of graph neural networks (GNNs) to maintain the s…
Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning
Shuo Yu, Hongyan Xue, Xiang Ao +4
In the field of quantitative trading, it is common practice to transform raw historical stock data into indicative signals for the market trend. Such signals are called alpha facto…
User-Centric Conversational Recommendation with Multi-Aspect User Modeling
Shuokai Li, Ruobing Xie, Yongchun Zhu +3
Conversational recommender systems (CRS) aim to provide highquality recommendations in conversations. However, most conventional CRS models mainly focus on the dialogue understandi…
Domain Adaptation with Category Attention Network for Deep Sentiment Analysis
Dongbo Xi, Fuzhen Zhuang, Ganbin Zhou +3
Domain adaptation tasks such as cross-domain sentiment classification aim to utilize existing labeled data in the source domain and unlabeled or few labeled data in the target doma…
ConRPG: Paraphrase Generation using Contexts as Regularizer
Yuxian Meng, Xiang Ao, Qing He +5
A long-standing issue with paraphrase generation is how to obtain reliable supervision signals. In this paper, we propose an unsupervised paradigm for paraphrase generation based o…
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…
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…
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information
Zijun Sun, Xiaoya Li, Xiaofei Sun +6
Recent pretraining models in Chinese neglect two important aspects specific to the Chinese language: glyph and pinyin, which carry significant syntax and semantic information for l…
Field-aware Calibration: A Simple and Empirically Strong Method for Reliable Probabilistic Predictions
Feiyang Pan, Xiang Ao, Pingzhong Tang +4
It is often observed that the probabilistic predictions given by a machine learning model can disagree with averaged actual outcomes on specific subsets of data, which is also know…