b'Fangzhao Wu'

research

∙ 06/25/2023

FedSampling: A Better Sampling Strategy for Federated Learning

Federated learning (FL) is an important technique for learning models fr...

0 Tao Qi, et al. ∙

research

∙ 05/17/2023

Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark

Large language models (LLMs) have demonstrated powerful capabilities in ...

0 Wenjun Peng, et al. ∙

research

∙ 03/15/2023

DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision

Algorithmic fairness has become an important machine learning problem, e...

0 Sungwon Han, et al. ∙

research

∙ 03/01/2023

Backdoor for Debias: Mitigating Model Bias with Backdoor Attack-based Artificial Bias

With the swift advancement of deep learning, state-of-the-art algorithms...

0 Shangxi Wu, et al. ∙

research

∙ 02/28/2023

Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems

Existing research efforts for multi-interest candidate matching in recom...

0 Yueqi Xie, et al. ∙

research

∙ 10/20/2022

Federated Unlearning for On-Device Recommendation

The increasing data privacy concerns in recommendation systems have made...

0 Wei Yuan, et al. ∙

research

∙ 10/17/2022

Effective and Efficient Query-aware Snippet Extraction for Web Search

Query-aware webpage snippet extraction is widely used in search engines ...

0 Jingwei Yi, et al. ∙

research

∙ 09/19/2022

CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks

Previous works have validated that text generation APIs can be stolen th...

0 Xuanli He, et al. ∙

research

∙ 07/19/2022

FedX: Unsupervised Federated Learning with Cross Knowledge Distillation

This paper presents FedX, an unsupervised federated learning framework. ...

0 Sungwon Han, et al. ∙

research

∙ 06/26/2022

Two-Stage Neural Contextual Bandits for Personalised News Recommendation

We consider the problem of personalised news recommendation where each u...

1 Mengyan Zhang, et al. ∙

research

∙ 06/07/2022

FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning

Vertical federated learning (VFL) is a privacy-preserving machine learni...

0 Tao Qi, et al. ∙

research

∙ 05/22/2022

Robust Quantity-Aware Aggregation for Federated Learning

Federated learning (FL) enables multiple clients to collaboratively trai...

0 Jingwei Yi, et al. ∙

research

∙ 04/21/2022

FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation

Contrastive learning is widely used for recommendation model learning, w...

0 Chuhan Wu, et al. ∙

research

∙ 04/18/2022

PrivateRec: Differentially Private Training and Serving for Federated News Recommendation

Privacy protection is an essential issue in personalized news recommenda...

0 Ruixuan Liu, et al. ∙

research

∙ 04/10/2022

FUM: Fine-grained and Fast User Modeling for News Recommendation

User modeling is important for news recommendation. Existing methods usu...

0 Tao Qi, et al. ∙

research

∙ 04/10/2022

News Recommendation with Candidate-aware User Modeling

News recommendation aims to match news with personalized user interest. ...

0 Tao Qi, et al. ∙

research

∙ 04/10/2022

ProFairRec: Provider Fairness-aware News Recommendation

News recommendation aims to help online news platform users find their p...

0 Tao Qi, et al. ∙

research

∙ 04/01/2022

Unified and Effective Ensemble Knowledge Distillation

Ensemble knowledge distillation can extract knowledge from multiple teac...

0 Chuhan Wu, et al. ∙

research

∙ 04/01/2022

FairRank: Fairness-aware Single-tower Ranking Framework for News Recommendation

Single-tower models are widely used in the ranking stage of news recomme...

0 Chuhan Wu, et al. ∙

research

∙ 04/01/2022

End-to-end Learnable Diversity-aware News Recommendation

Diversity is an important factor in providing high-quality personalized ...

0 Chuhan Wu, et al. ∙

research

∙ 04/01/2022

Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder

Adversarial learning is a widely used technique in fair representation l...

0 Chuhan Wu, et al. ∙

research

∙ 02/28/2022

Are Big Recommendation Models Fair to Cold Users?

Big models are widely used by online recommender systems to boost recomm...

0 Chuhan Wu, et al. ∙

research

∙ 02/28/2022

Quality-aware News Recommendation

News recommendation is a core technique used by many online news platfor...

0 Chuhan Wu, et al. ∙

research

∙ 02/24/2022

NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better

Effectively finetuning pretrained language models (PLMs) is critical for...

0 Chuhan Wu, et al. ∙

research

∙ 02/16/2022

No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices

Federated learning (FL) is an important paradigm for training global mod...

0 Ruixuan Liu, et al. ∙

research

∙ 02/14/2022

UA-FedRec: Untargeted Attack on Federated News Recommendation

News recommendation is critical for personalized news distribution. Fede...

0 Jingwei Yi, et al. ∙

research

∙ 02/10/2022

Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction

Vertical federated learning (VFL) aims to train models from cross-silo d...

0 Chuhan Wu, et al. ∙

research

∙ 02/10/2022

FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling

Federated learning (FL) is a feasible technique to learn personalized re...

0 Chuhan Wu, et al. ∙

research

∙ 12/05/2021

Protecting Intellectual Property of Language Generation APIs with Lexical Watermark

Nowadays, due to the breakthrough in natural language generation (NLG), ...

0 Xuanli He, et al. ∙

research

∙ 12/02/2021

Tiny-NewsRec: Efficient and Effective PLM-based News Recommendation

Personalized news recommendation has been widely adopted to improve user...

0 Yang Yu, et al. ∙

research

∙ 09/12/2021

Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation

News recommendation is critical for personalized news access. Most exist...

0 Jingwei Yi, et al. ∙

research

∙ 09/11/2021

Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving

News recommendation is important for personalized online news services. ...

0 Tao Qi, et al. ∙

research

∙ 09/03/2021

UserBERT: Contrastive User Model Pre-training

User modeling is critical for personalized web applications. Existing us...

0 Chuhan Wu, et al. ∙

research

∙ 08/30/2021

FedKD: Communication Efficient Federated Learning via Knowledge Distillation

Federated learning is widely used to learn intelligent models from decen...

0 Chuhan Wu, et al. ∙

research

∙ 08/20/2021

Smart Bird: Learnable Sparse Attention for Efficient and Effective Transformer

Transformer has achieved great success in NLP. However, the quadratic co...

0 Chuhan Wu, et al. ∙

research

∙ 08/20/2021

Fastformer: Additive Attention Can Be All You Need

Transformer is a powerful model for text understanding. However, it is i...

0 Chuhan Wu, et al. ∙

research

∙ 08/20/2021

Is News Recommendation a Sequential Recommendation Task?

News recommendation is often modeled as a sequential recommendation task...

0 Chuhan Wu, et al. ∙

research

∙ 06/16/2021

Personalized News Recommendation: A Survey

Personalized news recommendation is an important technique to help users...

0 Chuhan Wu, et al. ∙

research

∙ 06/11/2021

DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning

News recommendation is important for improving news reading experience o...

0 Chuhan Wu, et al. ∙

research

∙ 06/08/2021

HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation

User interest modeling is critical for personalized news recommendation....

0 Tao Qi, et al. ∙

research

∙ 06/02/2021

PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity

Personalized news recommendation methods are widely used in online news ...

0 Tao Qi, et al. ∙

research

∙ 06/02/2021

Hi-Transformer: Hierarchical Interactive Transformer for Efficient and Effective Long Document Modeling

Transformer is important for text modeling. However, it has difficulty i...

0 Chuhan Wu, et al. ∙

research

∙ 06/02/2021

One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers

Pre-trained language models (PLMs) achieve great success in NLP. However...

0 Chuhan Wu, et al. ∙

research

∙ 05/27/2021

Rethinking InfoNCE: How Many Negative Samples Do You Need?

InfoNCE loss is a widely used loss function for contrastive model traini...

0 Chuhan Wu, et al. ∙

research

∙ 05/23/2021

Killing Two Birds with One Stone: Stealing Model and Inferring Attribute from BERT-based APIs

The advances in pre-trained models (e.g., BERT, XLNET and etc) have larg...

0 Lingjuan Lyu, et al. ∙

research

∙ 04/20/2021

Personalized News Recommendation with Knowledge-aware Interactive Matching

The most important task in personalized news recommendation is accurate ...

0 Tao Qi, et al. ∙

research

∙ 04/15/2021

Empowering News Recommendation with Pre-trained Language Models

Personalized news recommendation is an essential technique for online ne...

0 Chuhan Wu, et al. ∙

research

∙ 04/15/2021

MM-Rec: Multimodal News Recommendation

Accurate news representation is critical for news recommendation. Most o...

0 Chuhan Wu, et al. ∙

research

∙ 04/15/2021

Two Birds with One Stone: Unified Model Learning for Both Recall and Ranking in News Recommendation

Recall and ranking are two critical steps in personalized news recommend...

0 Chuhan Wu, et al. ∙

research

∙ 04/15/2021

DebiasedRec: Bias-aware User Modeling and Click Prediction for Personalized News Recommendation

News recommendation is critical for personalized news access. Existing n...

0 Jingwei Yi, et al. ∙

Fangzhao Wu

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