Search Results for author: Liang Sun

Found 59 papers, 20 papers with code

CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect

no code implementations1 Jul 2024 Jiehui Zhou, Linxiao Yang, Xingyu Liu, Xinyue Gu, Liang Sun, Wei Chen

In this paper, we propose CURLS, a novel rule learning method leveraging HTE, which can effectively describe subgroups with significant treatment effects.

Causal Inference Management

SLIM: a Scalable Light-weight Root Cause Analysis for Imbalanced Data in Microservice

no code implementations31 May 2024 Rui Ren, Jingbang Yang, Linxiao Yang, Xinyue Gu, Liang Sun

Compared with existing fault localization algorithms, our algorithm can adapt to the imbalanced fault scenario of change service, and provide interpretable fault causes which are easy to understand and verify.

Fault localization

Attention as Robust Representation for Time Series Forecasting

no code implementations8 Feb 2024 Peisong Niu, Tian Zhou, Xue Wang, Liang Sun, Rong Jin

Time series forecasting is essential for many practical applications, with the adoption of transformer-based models on the rise due to their impressive performance in NLP and CV.

Multivariate Time Series Forecasting Time Series

FusionSF: Fuse Heterogeneous Modalities in a Vector Quantized Framework for Robust Solar Power Forecasting

no code implementations8 Feb 2024 Ziqing Ma, Wenwei Wang, Tian Zhou, Chao Chen, Bingqing Peng, Liang Sun, Rong Jin

Current research predominantly relies on historical solar power data or numerical weather prediction in a single-modality format, ignoring the complementary information provided in different modalities.

DSAF: A Dual-Stage Adaptive Framework for Numerical Weather Prediction Downscaling

1 code implementation19 Dec 2023 Pengwei Liu, Wenwei Wang, Bingqing Peng, Binqing Wu, Liang Sun

While widely recognized as one of the most substantial weather forecasting methodologies, Numerical Weather Prediction (NWP) usually suffers from relatively coarse resolution and inevitable bias due to tempo-spatial discretization, physical parametrization process, and computation limitation.

Multi-Task Learning Weather Forecasting

SVQ: Sparse Vector Quantization for Spatiotemporal Forecasting

1 code implementation6 Dec 2023 Chao Chen, Tian Zhou, Yanjun Zhao, Hui Liu, Liang Sun, Rong Jin

Moreover, we approximate the sparse regression process using a blend of a two-layer MLP and an extensive codebook.

Computational Efficiency Quantization +6

One Fits All: Universal Time Series Analysis by Pretrained LM and Specially Designed Adaptors

1 code implementation24 Nov 2023 Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin

Despite the impressive achievements of pre-trained models in the fields of natural language processing (NLP) and computer vision (CV), progress in the domain of time series analysis has been limited.

Anomaly Detection Few-Shot Learning +2

WeatherGNN: Exploiting Meteo- and Spatial-Dependencies for Local Numerical Weather Prediction Bias-Correction

no code implementations9 Oct 2023 Binqing Wu, Weiqi Chen, Wengwei Wang, Bingqing Peng, Liang Sun, Ling Chen

Due to insufficient local area information, numerical weather prediction (NWP) may yield biases for specific areas.

OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling

1 code implementation NeurIPS 2023 Yi-Fan Zhang, Qingsong Wen, Xue Wang, Weiqi Chen, Liang Sun, Zhang Zhang, Liang Wang, Rong Jin, Tieniu Tan

Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data.

Time Series Time Series Forecasting

BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition

1 code implementation28 Aug 2023 Shikai Fang, Qingsong Wen, Yingtao Luo, Shandian Zhe, Liang Sun

More importantly, almost all methods assume the observations are sampled at regular time stamps, and fail to handle complex irregular sampled time series arising from different applications.

Computational Efficiency Gaussian Processes +3

Benchmarks and Custom Package for Electrical Load Forecasting

1 code implementation14 Jul 2023 Zhixian Wang, Qingsong Wen, Chaoli Zhang, Liang Sun, Leandro Von Krannichfeldt, Yi Wang

Based on this, we conducted extensive experiments on load data at different levels, providing a reference for researchers to compare different load forecasting models.

Feature Engineering Load Forecasting +2

DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection

2 code implementations17 Jun 2023 Yiyuan Yang, Chaoli Zhang, Tian Zhou, Qingsong Wen, Liang Sun

On the other hand, contrastive learning aims to find a representation that can clearly distinguish any instance from the others, which can bring a more natural and promising representation for time series anomaly detection.

Anomaly Detection Contrastive Learning +3

SaDI: A Self-adaptive Decomposed Interpretable Framework for Electric Load Forecasting under Extreme Events

no code implementations14 Jun 2023 Hengbo Liu, Ziqing Ma, Linxiao Yang, Tian Zhou, Rui Xia, Yi Wang, Qingsong Wen, Liang Sun

In this paper, we propose a novel forecasting framework, named Self-adaptive Decomposed Interpretable framework~(SaDI), which ensembles long-term trend, short-term trend, and period modelings to capture temporal characteristics in different components.

Load Forecasting Management

DiffLoad: Uncertainty Quantification in Load Forecasting with Diffusion Model

no code implementations31 May 2023 Zhixian Wang, Qingsong Wen, Chaoli Zhang, Liang Sun, Yi Wang

The uncertainties in load forecasting can be divided into two types: epistemic uncertainty and aleatoric uncertainty.

Decision Making energy management +3

AHPA: Adaptive Horizontal Pod Autoscaling Systems on Alibaba Cloud Container Service for Kubernetes

no code implementations7 Mar 2023 Zhiqiang Zhou, Chaoli Zhang, Lingna Ma, Jing Gu, Huajie Qian, Qingsong Wen, Liang Sun, Peng Li, Zhimin Tang

This paper discusses horizontal POD resources management in Alibaba Cloud Container Services with a newly deployed AI algorithm framework named AHPA -- the adaptive horizontal pod auto-scaling system.


Robust Dominant Periodicity Detection for Time Series with Missing Data

no code implementations6 Mar 2023 Qingsong Wen, Linxiao Yang, Liang Sun

In this paper, we propose a robust and effective periodicity detection algorithm for time series with block missing data.

Time Series Time Series Analysis

One Fits All:Power General Time Series Analysis by Pretrained LM

3 code implementations23 Feb 2023 Tian Zhou, Peisong Niu, Xue Wang, Liang Sun, Rong Jin

The main challenge that blocks the development of pre-trained model for time series analysis is the lack of a large amount of data for training.

Anomaly Detection Few-Shot Learning +2

Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment

1 code implementation24 Oct 2022 Chenxiao Yang, Qitian Wu, Qingsong Wen, Zhiqiang Zhou, Liang Sun, Junchi Yan

The goal of sequential event prediction is to estimate the next event based on a sequence of historical events, with applications to sequential recommendation, user behavior analysis and clinical treatment.

Sequential Recommendation Variational Inference

TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis

1 code implementation18 Oct 2022 Chaoli Zhang, Tian Zhou, Qingsong Wen, Liang Sun

Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data.

Anomaly Detection Data Augmentation +2

TreeDRNet:A Robust Deep Model for Long Term Time Series Forecasting

no code implementations24 Jun 2022 Tian Zhou, Jianqing Zhu, Xue Wang, Ziqing Ma, Qingsong Wen, Liang Sun, Rong Jin

Various deep learning models, especially some latest Transformer-based approaches, have greatly improved the state-of-art performance for long-term time series forecasting. However, those transformer-based models suffer a severe deterioration performance with prolonged input length, which prohibits them from using extended historical info. Moreover, these methods tend to handle complex examples in long-term forecasting with increased model complexity, which often leads to a significant increase in computation and less robustness in performance(e. g., overfitting).

Computational Efficiency feature selection +2

Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach

no code implementations NeurIPS 2021 Fan Yang, Kai He, Linxiao Yang, Hongxia Du, Jingbang Yang, Bo Yang, Liang Sun

The learning problem is framed as a subset selection task in which a subset of all possible rules needs to be selected to form an accurate and interpretable rule set.

FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting

3 code implementations18 May 2022 Tian Zhou, Ziqing Ma, Xue Wang, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin

Recent studies have shown that deep learning models such as RNNs and Transformers have brought significant performance gains for long-term forecasting of time series because they effectively utilize historical information.

Dimensionality Reduction Time Series +1

Transformers in Time Series: A Survey

10 code implementations15 Feb 2022 Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, Liang Sun

From the perspective of network structure, we summarize the adaptations and modifications that have been made to Transformers in order to accommodate the challenges in time series analysis.

Anomaly Detection Time Series +1

FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

3 code implementations30 Jan 2022 Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin

Although Transformer-based methods have significantly improved state-of-the-art results for long-term series forecasting, they are not only computationally expensive but more importantly, are unable to capture the global view of time series (e. g. overall trend).

Time Series Time Series Analysis

CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms

no code implementations5 Nov 2021 Yingying Zhang, Zhengxiong Guan, Huajie Qian, Leili Xu, Hengbo Liu, Qingsong Wen, Liang Sun, Junwei Jiang, Lunting Fan, Min Ke

As business of Alibaba expands across the world among various industries, higher standards are imposed on the service quality and reliability of big data cloud computing platforms which constitute the infrastructure of Alibaba Cloud.

Anomaly Detection Cloud Computing +1

Uncertainty-Aware Task Allocation for Distributed Autonomous Robots

no code implementations21 Jul 2021 Liang Sun, Leonardo Escamilla

This paper addresses task-allocation problems with uncertainty in situational awareness for distributed autonomous robots (DARs).

Two-Stage Framework for Seasonal Time Series Forecasting

no code implementations3 Mar 2021 Qingyang Xu, Qingsong Wen, Liang Sun

By incorporating the learned long-range structure, the second stage can enhance the prediction accuracy in the forecast horizon.

Time Series Time Series Forecasting +1

Text-Embedded Bilinear Model for Fine-Grained Visual Recognition

no code implementations12 Oct 2020 Liang Sun, Xiang Guan, Yang Yang, Lei Zhang

Specially, we first conduct a text-embedded network to embed text feature into the discriminative image feature learning to get a embedded feature.

Fine-Grained Image Recognition Fine-Grained Visual Recognition +1

An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI

no code implementations10 Aug 2020 Xin Zhang, Liangxiu Han, Wenyong Zhu, Liang Sun, Daoqiang Zhang

Different from the existing approaches, the novelty of our approach is three-fold: 1) A Residual Self-Attention Deep Neural Network has been proposed to capture local, global and spatial information of MR images to improve diagnostic performance; 2) An explanation method using Gradient-based Localization Class Activation mapping (Grad-CAM) has been introduced to improve the explainable of the proposed method; 3) This work has provided a full end-to-end learning solution for automated disease diagnosis.


RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicity Detection

1 code implementation21 Feb 2020 Qingsong Wen, Kai He, Liang Sun, Yingying Zhang, Min Ke, Huan Xu

Periodicity detection is a crucial step in time series tasks, including monitoring and forecasting of metrics in many areas, such as IoT applications and self-driving database management system.

Anomaly Detection Clustering +3

Exploring Overall Contextual Information for Image Captioning in Human-Like Cognitive Style

no code implementations ICCV 2019 Hongwei Ge, Zehang Yan, Kai Zhang, Mingde Zhao, Liang Sun

In the training process, the forward and backward LSTMs encode the succeeding and preceding words into their respective hidden states by simultaneously constructing the whole sentence in a complementary manner.

Decoder Image Captioning +1

Robust Gaussian Process Regression for Real-Time High Precision GPS Signal Enhancement

no code implementations3 Jun 2019 Ming Lin, Xiaomin Song, Qi Qian, Hao Li, Liang Sun, Shenghuo Zhu, Rong Jin

We validate the superiority of the proposed method in our real-time high precision positioning system against several popular state-of-the-art robust regression methods.


Multimodal Semantic Attention Network for Video Captioning

no code implementations8 May 2019 Liang Sun, Bing Li, Chunfeng Yuan, Zheng-Jun Zha, Weiming Hu

Inspired by the fact that different modalities in videos carry complementary information, we propose a Multimodal Semantic Attention Network(MSAN), which is a new encoder-decoder framework incorporating multimodal semantic attributes for video captioning.

Attribute Decoder +3

RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series

1 code implementation5 Dec 2018 Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Huan Xu, Shenghuo Zhu

Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component.

Anomaly Detection Time Series +1

Cross-Technology Communications for Heterogeneous IoT Devices Through Artificial Doppler Shifts

no code implementations27 Nov 2018 Wei Wang, Shiyue He, Liang Sun, Tao Jiang, Qian Zhang

To this end, we propose DopplerFi, a communication framework that enables a two-way communication channel between BLE and Wi-Fi by injecting artificial Doppler shifts, which can be decoded by sensing the patterns in the Gaussian frequency shift keying (GFSK) demodulator and Channel State Information (CSI).

Networking and Internet Architecture

A Many-Objective Evolutionary Algorithm With Two Interacting Processes: Cascade Clustering and Reference Point Incremental Learning

no code implementations3 Mar 2018 Hongwei Ge, Mingde Zhao, Liang Sun, Zhen Wang, Guozhen Tan, Qiang Zhang, C. L. Philip Chen

This paper proposes a many-objective optimization algorithm with two interacting processes: cascade clustering and reference point incremental learning (CLIA).

Clustering Diversity +1

Fill it up: Exploiting partial dependency annotations in a minimum spanning tree parser

no code implementations26 Nov 2016 Liang Sun, Jason Mielens, Jason Baldridge

Unsupervised models of dependency parsing typically require large amounts of clean, unlabeled data plus gold-standard part-of-speech tags.

Dependency Parsing

Expectation-maximization for logistic regression

no code implementations31 May 2013 James G. Scott, Liang Sun

We present a family of expectation-maximization (EM) algorithms for binary and negative-binomial logistic regression, drawing a sharp connection with the variational-Bayes algorithm of Jaakkola and Jordan (2000).


Projection onto A Nonnegative Max-Heap

no code implementations NeurIPS 2011 Jun Liu, Liang Sun, Jieping Ye

In this paper, we show that such Euclidean projection problem admits an analytical solution and we develop a top-down algorithm where the key operation is to find the so-called \emph{maximal root-tree} of the subtree rooted at each node.


Efficient Recovery of Jointly Sparse Vectors

no code implementations NeurIPS 2009 Liang Sun, Jun Liu, Jianhui Chen, Jieping Ye

MMV is an extension of the single measurement vector (SMV) model employed in standard compressive sensing (CS).

Compressive Sensing

Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data

no code implementations NeurIPS 2009 Shuai Huang, Jing Li, Liang Sun, Jun Liu, Teresa Wu, Kewei Chen, Adam Fleisher, Eric Reiman, Jieping Ye

Recent advances in neuroimaging techniques provide great potentials for effective diagnosis of Alzheimer’s disease (AD), the most common form of dementia.

Multi-label Multiple Kernel Learning

no code implementations NeurIPS 2008 Shuiwang Ji, Liang Sun, Rong Jin, Jieping Ye

We present a multi-label multiple kernel learning (MKL) formulation, in which the data are embedded into a low-dimensional space directed by the instance-label correlations encoded into a hypergraph.

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