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06/25/2023
FedSampling: A Better Sampling Strategy for Federated Learning
Federated learning (FL) is an important technique for learning models fr...
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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 ...
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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...
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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...
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02/28/2023
Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems
Existing research efforts for multi-interest candidate matching in recom...
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10/20/2022
Federated Unlearning for On-Device Recommendation
The increasing data privacy concerns in recommendation systems have made...
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10/17/2022
Effective and Efficient Query-aware Snippet Extraction for Web Search
Query-aware webpage snippet extraction is widely used in search engines ...
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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...
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07/19/2022
FedX: Unsupervised Federated Learning with Cross Knowledge Distillation
This paper presents FedX, an unsupervised federated learning framework. ...
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06/26/2022
Two-Stage Neural Contextual Bandits for Personalised News Recommendation
We consider the problem of personalised news recommendation where each u...
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06/07/2022
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
Vertical federated learning (VFL) is a privacy-preserving machine learni...
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05/22/2022
Robust Quantity-Aware Aggregation for Federated Learning
Federated learning (FL) enables multiple clients to collaboratively trai...
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04/21/2022
FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation
Contrastive learning is widely used for recommendation model learning, w...
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04/18/2022
PrivateRec: Differentially Private Training and Serving for Federated News Recommendation
Privacy protection is an essential issue in personalized news recommenda...
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04/10/2022
FUM: Fine-grained and Fast User Modeling for News Recommendation
User modeling is important for news recommendation. Existing methods usu...
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04/10/2022
News Recommendation with Candidate-aware User Modeling
News recommendation aims to match news with personalized user interest. ...
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04/10/2022
ProFairRec: Provider Fairness-aware News Recommendation
News recommendation aims to help online news platform users find their p...
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04/01/2022
Unified and Effective Ensemble Knowledge Distillation
Ensemble knowledge distillation can extract knowledge from multiple teac...
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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...
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04/01/2022
End-to-end Learnable Diversity-aware News Recommendation
Diversity is an important factor in providing high-quality personalized ...
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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...
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02/28/2022
Are Big Recommendation Models Fair to Cold Users?
Big models are widely used by online recommender systems to boost recomm...
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02/28/2022
Quality-aware News Recommendation
News recommendation is a core technique used by many online news platfor...
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02/24/2022
NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better
Effectively finetuning pretrained language models (PLMs) is critical for...
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02/16/2022
No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices
Federated learning (FL) is an important paradigm for training global mod...
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02/14/2022
UA-FedRec: Untargeted Attack on Federated News Recommendation
News recommendation is critical for personalized news distribution. Fede...
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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...
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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...
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12/05/2021
Protecting Intellectual Property of Language Generation APIs with Lexical Watermark
Nowadays, due to the breakthrough in natural language generation (NLG), ...
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12/02/2021
Tiny-NewsRec: Efficient and Effective PLM-based News Recommendation
Personalized news recommendation has been widely adopted to improve user...
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09/12/2021
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation
News recommendation is critical for personalized news access. Most exist...
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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. ...
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09/03/2021
UserBERT: Contrastive User Model Pre-training
User modeling is critical for personalized web applications. Existing us...
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08/30/2021
FedKD: Communication Efficient Federated Learning via Knowledge Distillation
Federated learning is widely used to learn intelligent models from decen...
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08/20/2021
Smart Bird: Learnable Sparse Attention for Efficient and Effective Transformer
Transformer has achieved great success in NLP. However, the quadratic co...
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08/20/2021
Fastformer: Additive Attention Can Be All You Need
Transformer is a powerful model for text understanding. However, it is i...
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08/20/2021
Is News Recommendation a Sequential Recommendation Task?
News recommendation is often modeled as a sequential recommendation task...
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06/16/2021
Personalized News Recommendation: A Survey
Personalized news recommendation is an important technique to help users...
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06/11/2021
DebiasGAN: Eliminating Position Bias in News Recommendation with Adversarial Learning
News recommendation is important for improving news reading experience o...
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06/08/2021
HieRec: Hierarchical User Interest Modeling for Personalized News Recommendation
User interest modeling is critical for personalized news recommendation....
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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 ...
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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...
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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...
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05/27/2021
Rethinking InfoNCE: How Many Negative Samples Do You Need?
InfoNCE loss is a widely used loss function for contrastive model traini...
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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...
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04/20/2021
Personalized News Recommendation with Knowledge-aware Interactive Matching
The most important task in personalized news recommendation is accurate ...
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04/15/2021
Empowering News Recommendation with Pre-trained Language Models
Personalized news recommendation is an essential technique for online ne...
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04/15/2021
MM-Rec: Multimodal News Recommendation
Accurate news representation is critical for news recommendation. Most o...
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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...
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04/15/2021
