57 citations · 168 across the 16 of their papers we have counts for
8 papers · 1 filter
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning
Tianmeng Yang, Min Zhou, Yujing Wang +4
Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many res…
Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent
Xiaonan Nie, Yi Liu, Fangcheng Fu +5
Recent years have witnessed the unprecedented achievements of large-scale pre-trained models, especially the Transformer models. Many products and services in Tencent Inc., such as…
Transfer Learning for Bayesian Optimization: A Survey
Tianyi Bai, Yang Li, Yu Shen +3
A wide spectrum of design and decision problems, including parameter tuning, A/B testing and drug design, intrinsically are instances of black-box optimization. Bayesian optimizati…
DivBO: Diversity-aware CASH for Ensemble Learning
Yu Shen, Yupeng Lu, Yang Li +3
The Combined Algorithm Selection and Hyperparameters optimization (CASH) problem is one of the fundamental problems in Automated Machine Learning (AutoML). Motivated by the success…
Rover: An online Spark SQL tuning service via generalized transfer learning
Yu Shen, Xinyuyang Ren, Yupeng Lu +6
Distributed data analytic engines like Spark are common choices to process massive data in industry. However, the performance of Spark SQL highly depends on the choice of configura…
Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates
Fangcheng Fu, Xupeng Miao, Jiawei Jiang +2
Vertical federated learning (VFL) is an emerging paradigm that allows different parties (e.g., organizations or enterprises) to collaboratively build machine learning models with p…