4 citations · 11 across the 14 of their papers we have counts for
14 papers
DebiasRAG: A Tuning-Free Path to Fair Generation in Large Language Models through Retrieval-Augmented Generation
Rui Chu, Bingyin Zhao, Thanh Quoc Hung Le +6
Large language models (LLMs) have achieved unprecedented success due to their exceptional generative capabilities. However, because they depend on knowledge encapsulated from train…
GUITAR: Gradient Pruning toward Fast Neural Ranking
Weijie Zhao, Shulong Tan, Ping Li
With the continuous popularity of deep learning and representation learning, fast vector search becomes a vital task in various ranking/retrieval based applications, say recommenda…
Pb-Hash: Partitioned b-bit Hashing
Ping Li, Weijie Zhao
Many hashing algorithms including minwise hashing (MinHash), one permutation hashing (OPH), and consistent weighted sampling (CWS) generate integers of bits. With hashes fo…
Blockwise Feature Interaction in Recommendation Systems
Weijie Zhao, Ping Li
Feature interactions can play a crucial role in recommendation systems as they capture complex relationships between user preferences and item characteristics. Existing methods suc…
Practice with Graph-based ANN Algorithms on Sparse Data: Chi-square Two-tower model, HNSW, Sign Cauchy Projections
Ping Li, Weijie Zhao, Chao Wang +3
Sparse data are common. The traditional ``handcrafted'' features are often sparse. Embedding vectors from trained models can also be very sparse, for example, embeddings trained vi…
Building K-Anonymous User Cohorts with\\ Consecutive Consistent Weighted Sampling (CCWS)
Xinyi Zheng, Weijie Zhao, Xiaoyun Li +1
To retrieve personalized campaigns and creatives while protecting user privacy, digital advertising is shifting from member-based identity to cohort-based identity. Under such iden…
Constrained Approximate Similarity Search on Proximity Graph
Weijie Zhao, Shulong Tan, Ping Li
Search engines and recommendation systems are built to efficiently display relevant information from those massive amounts of candidates. Typically a three-stage mechanism is emplo…
Asymmetric Hashing for Fast Ranking via Neural Network Measures
Khoa Doan, Shulong Tan, Weijie Zhao +1
Fast item ranking is an important task in recommender systems. In previous works, graph-based Approximate Nearest Neighbor (ANN) approaches have demonstrated good performance on it…
FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems
Weijie Zhao, Xuewu Jiao, Xinsheng Luo +3
Deep learning has been widely deployed for online ads systems to predict Click-Through Rate (CTR). Machine learning researchers and practitioners frequently retrain CTR models to t…
Package for Fast ABC-Boost
Ping Li, Weijie Zhao
This report presents the open-source package which implements the series of our boosting works in the past years. In particular, the package includes mainly three lines of techniqu…
pGMM Kernel Regression and Comparisons with Boosted Trees
Ping Li, Weijie Zhao
In this work, we demonstrate the advantage of the pGMM (``powered generalized min-max'') kernel in the context of (ridge) regression. In recent prior studies, the pGMM kernel has b…
Fast ABC-Boost: A Unified Framework for Selecting the Base Class in Multi-Class Classification
Ping Li, Weijie Zhao
The work in ICML'09 showed that the derivatives of the classical multi-class logistic regression loss function could be re-written in terms of a pre-chosen "base class" and applied…
Integrity Authentication in Tree Models
Weijie Zhao, Yingjie Lao, Ping Li
Tree models are very widely used in practice of machine learning and data mining. In this paper, we study the problem of model integrity authentication in tree models. In general,…
Proximity Graph Maintenance for Fast Online Nearest Neighbor Search
Zhaozhuo Xu, Weijie Zhao, Shulong Tan +2
Approximate Nearest Neighbor (ANN) search is a fundamental technique for (e.g.,) the deployment of recommender systems. Recent studies bring proximity graph-based methods into prac…