402 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Alibaba Group (China)CN60 papers
- Zhejiang UniversityCN46 papers
- Bellevue Hospital CenterUS30 papers
- Peking UniversityCN22 papers
- Nanyang Technological UniversitySG21 papers
- Tsinghua UniversityCN15 papers
- Chinese Academy of SciencesCN14 papers
- Renmin University of ChinaCN13 papers
- Shanghai Jiao Tong UniversityCN11 papers
- Tianjin UniversityCN11 papers
- Alibaba Group (Cayman Islands)KY10 papers
- East China Normal UniversityCN10 papers
69 papers · 1 filter
CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition
Junjie Meng, Ranxu Zhang, Zi-an Zhang +6
Forecasting in large-scale e-commerce marketplaces is increasingly required to support planning: merchants need to evaluate sales outcomes under future action sequences such as bud…
Safe-SAIL: Towards a Fine-grained Safety Landscape of Large Language Models via Sparse Autoencoder Interpretation Framework
Jiaqi Weng, Han Zheng, Hanyu Zhang +6
Sparse autoencoders (SAEs) enable interpretability research by decomposing entangled model activations into monosemantic features. However, under what circumstances SAEs derive mos…
Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting
Yingtao Luo, Shikai Fang, Binqing Wu +2
Weather forecasting is essential but remains computationally intensive and physically incomplete in traditional numerical weather prediction (NWP) methods. Deep learning (DL) model…
Deeply Learned Robust Matrix Completion for Large-scale Low-rank Data Recovery
HanQin Cai, Chandra Kundu, Jialin Liu +1
Robust matrix completion (RMC) is a widely used machine learning tool that simultaneously tackles two critical issues in low-rank data analysis: missing data entries and extreme ou…
Explainable Fuzzy Neural Network with Multi-Fidelity Reinforcement Learning for Micro-Architecture Design Space Exploration
Hanwei Fan, Ya Wang, Sicheng Li +2
With the continuous advancement of processors, modern micro-architecture designs have become increasingly complex. The vast design space presents significant challenges for human d…
SAttention: Improving Long Sequence Attention with Smoothed Skeleton Sketching
Xue Wang, Tian Zhou, Jianqing Zhu +6
Attention based models have achieved many remarkable breakthroughs in numerous applications. However, the quadratic complexity of Attention makes the vanilla Attention based models…
DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection
Yiyuan Yang, Chaoli Zhang, Tian Zhou +2
Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundame…
OneShotSTL: One-Shot Seasonal-Trend Decomposition For Online Time Series Anomaly Detection And Forecasting
Xiao He, Ye Li, Jian Tan +2
Seasonal-trend decomposition is one of the most fundamental concepts in time series analysis that supports various downstream tasks, including time series anomaly detection and for…
TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis
Chaoli Zhang, Tian Zhou, Qingsong Wen +1
Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. Although some algorithms including both traditional and…
CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning
Chenyu Sun, Hangwei Qian, Chunyan Miao
In reinforcement learning (RL), it is challenging to learn directly from high-dimensional observations, where data augmentation has recently been shown to remedy this via encoding…
Learning to be a Statistician: Learned Estimator for Number of Distinct Values
Renzhi Wu, Bolin Ding, Xu Chu +4
Estimating the number of distinct values (NDV) in a column is useful for many tasks in database systems, such as columnstore compression and data profiling. In this work, we focus…
KNAS: Green Neural Architecture Search
Jingjing Xu, Liang Zhao, Junyang Lin +3
Many existing neural architecture search (NAS) solutions rely on downstream training for architecture evaluation, which takes enormous computations. Considering that these computat…
Density-Based Clustering with Kernel Diffusion
Chao Zheng, Yingjie Chen, Chong Chen +2
Finding a suitable density function is essential for density-based clustering algorithms such as DBSCAN and DPC. A naive density corresponding to the indicator function of a unit $…
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection
HanQin Cai, Jialin Liu, Wotao Yin
Robust principal component analysis (RPCA) is a critical tool in modern machine learning, which detects outliers in the task of low-rank matrix reconstruction. In this paper, we pr…
Stable Prediction on Graphs with Agnostic Distribution Shift
Shengyu Zhang, Kun Kuang, Jiezhong Qiu +5
Graph is a flexible and effective tool to represent complex structures in practice and graph neural networks (GNNs) have been shown to be effective on various graph tasks with rand…
From Known to Unknown: Knowledge-guided Transformer for Time-Series Sales Forecasting in Alibaba
Xinyuan Qi, Kai Hou, Tong Liu +3
Time series forecasting (TSF) is fundamentally required in many real-world applications, such as electricity consumption planning and sales forecasting. In e-commerce, accurate tim…
DQN Control Solution for KDD Cup 2021 City Brain Challenge
Yitian Chen, Kunlong Chen, Kunjin Chen +1
We took part in the city brain challenge competition and achieved the 8th place. In this competition, the players are provided with a real-world city-scale road network and its tra…
Decentralized Composite Optimization with Compression
Yao Li, Xiaorui Liu, Jiliang Tang +2
Decentralized optimization and communication compression have exhibited their great potential in accelerating distributed machine learning by mitigating the communication bottlenec…
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…
Automated Graph Learning via Population Based Self-Tuning GCN
Ronghang Zhu, Zhiqiang Tao, Yaliang Li +1
Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of t…
Revisiting Knowledge Distillation: An Inheritance and Exploration Framework
Zhen Huang, Xu Shen, Jun Xing +6
Knowledge Distillation (KD) is a popular technique to transfer knowledge from a teacher model or ensemble to a student model. Its success is generally attributed to the privileged…
GraphMI: Extracting Private Graph Data from Graph Neural Networks
Zaixi Zhang, Qi Liu, Zhenya Huang +4
As machine learning becomes more widely used for critical applications, the need to study its implications in privacy turns to be urgent. Given access to the target model and auxil…
Learning to Rehearse in Long Sequence Memorization
Zhu Zhang, Chang Zhou, Jianxin Ma +4
Existing reasoning tasks often have an important assumption that the input contents can be always accessed while reasoning, requiring unlimited storage resources and suffering from…
Accelerating Gossip SGD with Periodic Global Averaging
Yiming Chen, Kun Yuan, Yingya Zhang +3
Communication overhead hinders the scalability of large-scale distributed training. Gossip SGD, where each node averages only with its neighbors, is more communication-efficient th…