305 citations · 741 across the 44 of their papers we have counts for
44 papers
Observation of decay
M. Ablikim
Using a sample of events collected with the BESIII detector, the decay with subsequently decaying into $γπ^+…
A coupled-channel analysis of the lineshape with BESIII data
BESIII Collaboration, M. Ablikim, M. N. Achasov +611
We perform a study of the lineshape using the data samples of , and collected with the BESIII detector. The…
Investigation of the rule and test of CP violation through the measurement of decay asymmetry parameters in decays
BESIII Collaboration, M. Ablikim, M. N. Achasov +615
Using events collected with the BESIII detector, numerous and decay asymmetry parameters are simultaneously determined from the process…
Measurement of the cross sections from 2.000 to 3.080 GeV
BESIII Collaboration, M. Ablikim, M. N. Achasov +615
Based on collision data collected at center-of-mass energies from 2.000 to 3.080 GeV by the BESIII detector at the BEPCII collider, a partial wave analysis is performe…
FedSampling: A Better Sampling Strategy for Federated Learning
Tao Qi, Fangzhao Wu, Lingjuan Lyu +2
Federated learning (FL) is an important technique for learning models from decentralized data in a privacy-preserving way. Existing FL methods usually uniformly sample clients for…
FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning
Tao Qi, Fangzhao Wu, Chuhan Wu +5
Vertical federated learning (VFL) is a privacy-preserving machine learning paradigm that can learn models from features distributed on different platforms in a privacy-preserving w…
Robust Quantity-Aware Aggregation for Federated Learning
Jingwei Yi, Fangzhao Wu, Huishuai Zhang +4
Federated learning (FL) enables multiple clients to collaboratively train models without sharing their local data, and becomes an important privacy-preserving machine learning fram…
FedCL: Federated Contrastive Learning for Privacy-Preserving Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Contrastive learning is widely used for recommendation model learning, where selecting representative and informative negative samples is critical. Existing methods usually focus o…
FUM: Fine-grained and Fast User Modeling for News Recommendation
Tao Qi, Fangzhao Wu, Chuhan Wu +1
User modeling is important for news recommendation. Existing methods usually first encode user's clicked news into news embeddings independently and then aggregate them into user e…
News Recommendation with Candidate-aware User Modeling
Tao Qi, Fangzhao Wu, Chuhan Wu +1
News recommendation aims to match news with personalized user interest. Existing methods for news recommendation usually model user interest from historical clicked news without th…
ProFairRec: Provider Fairness-aware News Recommendation
Tao Qi, Fangzhao Wu, Chuhan Wu +5
News recommendation aims to help online news platform users find their preferred news articles. Existing news recommendation methods usually learn models from historical user behav…
Unified and Effective Ensemble Knowledge Distillation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Ensemble knowledge distillation can extract knowledge from multiple teacher models and encode it into a single student model. Many existing methods learn and distill the student mo…
FairRank: Fairness-aware Single-tower Ranking Framework for News Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Single-tower models are widely used in the ranking stage of news recommendation to accurately rank candidate news according to their fine-grained relatedness with user interest ind…
End-to-end Learnable Diversity-aware News Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Diversity is an important factor in providing high-quality personalized news recommendations. However, most existing news recommendation methods only aim to optimize recommendation…
Semi-FairVAE: Semi-supervised Fair Representation Learning with Adversarial Variational Autoencoder
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Adversarial learning is a widely used technique in fair representation learning to remove the biases on sensitive attributes from data representations. It usually requires to incor…
NoisyTune: A Little Noise Can Help You Finetune Pretrained Language Models Better
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Effectively finetuning pretrained language models (PLMs) is critical for their success in downstream tasks. However, PLMs may have risks in overfitting the pretraining tasks and da…
Are Big Recommendation Models Fair to Cold Users?
Chuhan Wu, Fangzhao Wu, Tao Qi +1
Big models are widely used by online recommender systems to boost recommendation performance. They are usually learned on historical user behavior data to infer user interest and p…
Quality-aware News Recommendation
Chuhan Wu, Fangzhao Wu, Tao Qi +1
News recommendation is a core technique used by many online news platforms. Recommending high-quality news to users is important for keeping good user experiences and news platform…
FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Federated learning (FL) is a feasible technique to learn personalized recommendation models from decentralized user data. Unfortunately, federated recommender systems are vulnerabl…
Game of Privacy: Towards Better Federated Platform Collaboration under Privacy Restriction
Chuhan Wu, Fangzhao Wu, Tao Qi +4
Vertical federated learning (VFL) aims to train models from cross-silo data with different feature spaces stored on different platforms. Existing VFL methods usually assume all dat…
Uni-FedRec: A Unified Privacy-Preserving News Recommendation Framework for Model Training and Online Serving
Tao Qi, Fangzhao Wu, Chuhan Wu +2
News recommendation is important for personalized online news services. Most existing news recommendation methods rely on centrally stored user behavior data to both train models o…
Fastformer: Additive Attention Can Be All You Need
Chuhan Wu, Fangzhao Wu, Tao Qi +2
Transformer is a powerful model for text understanding. However, it is inefficient due to its quadratic complexity to input sequence length. Although there are many methods on Tran…
UserBERT: Contrastive User Model Pre-training
Chuhan Wu, Fangzhao Wu, Yang Yu +3
User modeling is critical for personalized web applications. Existing user modeling methods usually train user models from user behaviors with task-specific labeled data. However,…
Smart Bird: Learnable Sparse Attention for Efficient and Effective Transformer
Chuhan Wu, Fangzhao Wu, Tao Qi +4
Transformer has achieved great success in NLP. However, the quadratic complexity of the self-attention mechanism in Transformer makes it inefficient in handling long sequences. Man…