22 citations · 76 across the 14 of their papers we have counts for
15 papers
Judging the Justification: Decision Certificate Supervision for Generative Reward Models
Guiquan Sun, Bing Bai, Martin Renqiang Min +1
Generative reward models (GenRMs) generate natural language rationales to justify preference judgments, yet a correct verdict does not guarantee a sound justification. Existing app…
Uncertainty-Aware Learning Against Label Noise on Imbalanced Datasets
Yingsong Huang, Bing Bai, Shengwei Zhao +2
Learning against label noise is a vital topic to guarantee a reliable performance for deep neural networks. Recent research usually refers to dynamic noise modeling with model outp…
Contrastive Multi-view Hyperbolic Hierarchical Clustering
Fangfei Lin, Bing Bai, Kun Bai +3
Hierarchical clustering recursively partitions data at an increasingly finer granularity. In real-world applications, multi-view data have become increasingly important. This raise…
Domain Agnostic Learning for Unbiased Authentication
Jian Liang, Yuren Cao, Shuang Li +4
Authentication is the task of confirming the matching relationship between a data instance and a given identity. Typical examples of authentication problems include face recognitio…
Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark
Guanhua Zhang, Bing Bai, Jian Liang +3
Recent studies show that crowd-sourced Natural Language Inference (NLI) datasets may suffer from significant biases like annotation artifacts. Models utilizing these superficial cl…
Hybrid Differentially Private Federated Learning on Vertically Partitioned Data
Chang Wang, Jian Liang, Mingkai Huang +3
We present HDP-VFL, the first hybrid differentially private (DP) framework for vertical federated learning (VFL) to demonstrate that it is possible to jointly learn a generalized l…
A Federated Multi-View Deep Learning Framework for Privacy-Preserving Recommendations
Mingkai Huang, Hao Li, Bing Bai +3
Privacy-preserving recommendations are recently gaining momentum, since the decentralized user data is increasingly harder to collect, by recommendation service providers, due to t…
Why Attentions May Not Be Interpretable?
Bing Bai, Jian Liang, Guanhua Zhang +3
Attention-based methods have played important roles in model interpretations, where the calculated attention weights are expected to highlight the critical parts of inputs~(e.g., k…
Adversarial Infidelity Learning for Model Interpretation
Jian Liang, Bing Bai, Yuren Cao +2
Model interpretation is essential in data mining and knowledge discovery. It can help understand the intrinsic model working mechanism and check if the model has undesired characte…
General-Purpose User Embeddings based on Mobile App Usage
Junqi Zhang, Bing Bai, Ye Lin +3
In this paper, we report our recent practice at Tencent for user modeling based on mobile app usage. User behaviors on mobile app usage, including retention, installation, and unin…
CSRN: Collaborative Sequential Recommendation Networks for News Retrieval
Bing Bai, Guanhua Zhang, Ye Lin +3
Nowadays, news apps have taken over the popularity of paper-based media, providing a great opportunity for personalization. Recurrent Neural Network (RNN)-based sequential recommen…
Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting
Guanhua Zhang, Bing Bai, Junqi Zhang +3
With the recent proliferation of the use of text classifications, researchers have found that there are certain unintended biases in text classification datasets. For example, text…
Generating Followup Questions for Interpretable Multi-hop Question Answering
Christopher Malon, Bing Bai
We propose a framework for answering open domain multi-hop questions in which partial information is read and used to generate followup questions, to finally be answered by a pretr…
Mitigating Annotation Artifacts in Natural Language Inference Datasets to Improve Cross-dataset Generalization Ability
Guanhua Zhang, Bing Bai, Junqi Zhang +3
Natural language inference (NLI) aims at predicting the relationship between a given pair of premise and hypothesis. However, several works have found that there widely exists a bi…
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets
Guanhua Zhang, Bing Bai, Jian Liang +5
Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process. However,…