106 citations · 315 across the 15 of their papers we have counts for
17 papers
Efficient Resource-Shiftable RL Post-training with Generative Rewards
Yanfeng Zhao, Haoyang Li, Sheng Lin +4
Reinforcement learning (RL) post-training with generative reward models (GenRM) has become a central recipe for improving large language models (LLMs), and it is also increasingly…
HexAGenT: Efficient Agentic LLM Serving via Workflow- and Heterogeneity-Aware Scheduling
You Peng, Youhe Jiang, Wenshuang Li +5
Agentic LLM applications increasingly execute user requests as multi-step workflows involving planning, tool use, branching, refinement, and synthesis. In such settings, users expe…
An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec
Linping Xu, Jiawei Jiang, Dejun Zhang +7
Recently, neural networks have proven to be effective in performing speech coding task at low bitrates. However, under-utilization of intra-frame correlations and the error of quan…
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks
Qiang Huang, Jiawei Jiang, Xi Susie Rao +10
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed. Despite the success of these TGN…
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…
Efficient End-to-End AutoML via Scalable Search Space Decomposition
Yang Li, Yu Shen, Wentao Zhang +2
End-to-end AutoML has attracted intensive interests from both academia and industry which automatically searches for ML pipelines in a space induced by feature engineering, algorit…
Stochastic Gradient Descent without Full Data Shuffle
Lijie Xu, Shuang Qiu, Binhang Yuan +9
Stochastic gradient descent (SGD) is the cornerstone of modern machine learning (ML) systems. Despite its computational efficiency, SGD requires random data access that is inherent…
BRIGHT -- Graph Neural Networks in Real-Time Fraud Detection
Mingxuan Lu, Zhichao Han, Susie Xi Rao +6
Detecting fraudulent transactions is an essential component to control risk in e-commerce marketplaces. Apart from rule-based and machine learning filters that are already deployed…
Efficient Diversity-Driven Ensemble for Deep Neural Networks
Wentao Zhang, Jiawei Jiang, Yingxia Shao +1
The ensemble of deep neural networks has been shown, both theoretically and empirically, to improve generalization accuracy on the unseen test set. However, the high training cost…
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition
Yang Li, Yu Shen, Wentao Zhang +8
End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algori…
BAGUA: Scaling up Distributed Learning with System Relaxations
Shaoduo Gan, Xiangru Lian, Rui Wang +11
Recent years have witnessed a growing list of systems for distributed data-parallel training. Existing systems largely fit into two paradigms, i.e., parameter server and MPI-style…
OpenBox: A Generalized Black-box Optimization Service
Yang Li, Yu Shen, Wentao Zhang +9
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. However, it remains a challenge…
Towards Demystifying Serverless Machine Learning Training
Jiawei Jiang, Shaoduo Gan, Yue Liu +6
The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-intensive applications such as ETL, query processing, or machine learning (ML). Several sy…
Efficient Automatic CASH via Rising Bandits
Yang Li, Jiawei Jiang, Jinyang Gao +3
The Combined Algorithm Selection and Hyperparameter optimization (CASH) is one of the most fundamental problems in Automatic Machine Learning (AutoML). The existing Bayesian optimi…
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
Yang Li, Yu Shen, Jiawei Jiang +3
Hyperparameter optimization (HPO) is a fundamental problem in automatic machine learning (AutoML). However, due to the expensive evaluation cost of models (e.g., training deep lear…
An Experimental Evaluation of Large Scale GBDT Systems
Fangcheng Fu, Jiawei Jiang, Yingxia Shao +1
Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications. Further, driven by the…
Fast Hyperparameter Optimization of Deep Neural Networks via Ensembling Multiple Surrogates
Yang Li, Jiawei Jiang, Yingxia Shao +1
The performance of deep neural networks crucially depends on good hyperparameter configurations. Bayesian optimization is a powerful framework for optimizing the hyperparameters of…