Search Results for author: Chenghao Liu

Found 46 papers, 22 papers with code

Calibration of Time-Series Forecasting Transformers: Detecting and Adapting Context-Driven Distribution Shift

no code implementations23 Oct 2023 Mouxiang Chen, Lefei Shen, Han Fu, Zhuo Li, Jianling Sun, Chenghao Liu

In this paper, we introduce a universal calibration methodology for the detection and adaptation of CDS with a trained Transformer model.

Time Series Time Series Forecasting

ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt

no code implementations23 Oct 2023 Mouxiang Chen, Zemin Liu, Chenghao Liu, Jundong Li, Qiheng Mao, Jianling Sun

Based on this framework, we propose a prompt-based transferability test to find the most relevant pretext task in order to reduce the semantic gap.

Multi-Task Learning

Identifiability Matters: Revealing the Hidden Recoverable Condition in Unbiased Learning to Rank

no code implementations27 Sep 2023 Mouxiang Chen, Chenghao Liu, Zemin Liu, Zhuo Li, Jianling Sun

In this work, we aim to answer if or when the true relevance can be recovered from click data, which is a foundation issue for ULTR field.


FedET: A Communication-Efficient Federated Class-Incremental Learning Framework Based on Enhanced Transformer

no code implementations27 Jun 2023 Chenghao Liu, Xiaoyang Qu, Jianzong Wang, Jing Xiao

To address local forgetting caused by new classes of new tasks and global forgetting brought by non-i. i. d (non-independent and identically distributed) class imbalance across different local clients, we proposed an Enhancer distillation method to modify the imbalance between old and new knowledge and repair the non-i. i. d.

Class Incremental Learning Federated Learning +1

PyRCA: A Library for Metric-based Root Cause Analysis

1 code implementation20 Jun 2023 Chenghao Liu, Wenzhuo Yang, Himanshu Mittal, Manpreet Singh, Doyen Sahoo, Steven C. H. Hoi

We introduce PyRCA, an open-source Python machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps).

Causal Discovery graph construction

OTW: Optimal Transport Warping for Time Series

no code implementations1 Jun 2023 Fabian Latorre, Chenghao Liu, Doyen Sahoo, Steven C. H. Hoi

Dynamic Time Warping (DTW) has become the pragmatic choice for measuring distance between time series.

Clustering Dynamic Time Warping +1

AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges

no code implementations10 Apr 2023 Qian Cheng, Doyen Sahoo, Amrita Saha, Wenzhuo Yang, Chenghao Liu, Gerald Woo, Manpreet Singh, Silvio Saverese, Steven C. H. Hoi

There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency.

Unsupervised Brain Tumor Segmentation with Image-based Prompts

no code implementations4 Apr 2023 Xinru Zhang, Ni Ou, Chenghao Liu, Zhizheng Zhuo, Yaou Liu, Chuyang Ye

Specifically, instead of directly training a model for brain tumor segmentation with a large amount of annotated data, we seek to train a model that can answer the question: is a voxel in the input image associated with tumor-like hyper-/hypo-intensity?

Brain Tumor Segmentation Lesion Segmentation +2

HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer

1 code implementation22 Feb 2023 Qiheng Mao, Zemin Liu, Chenghao Liu, Jianling Sun

To bridge this gap, in this paper we investigate the representation learning on HINs with Graph Transformer, and propose a novel model named HINormer, which capitalizes on a larger-range aggregation mechanism for node representation learning.

Representation Learning

LogAI: A Library for Log Analytics and Intelligence

1 code implementation31 Jan 2023 Qian Cheng, Amrita Saha, Wenzhuo Yang, Chenghao Liu, Doyen Sahoo, Steven Hoi

In order to enable users to perform multiple types of AI-based log analysis tasks in a uniform manner, we introduce LogAI (https://github. com/salesforce/logai), a one-stop open source library for log analytics and intelligence.

Anomaly Detection Log Parsing +2

Continual Learning, Fast and Slow

1 code implementation6 Sep 2022 Quang Pham, Chenghao Liu, Steven C. H. Hoi

Motivated by this theory, we propose \emph{DualNets} (for Dual Networks), a general continual learning framework comprising a fast learning system for supervised learning of pattern-separated representation from specific tasks and a slow learning system for representation learning of task-agnostic general representation via Self-Supervised Learning (SSL).

Continual Learning Hippocampus +2

Learning Deep Time-index Models for Time Series Forecasting

1 code implementation13 Jul 2022 Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historical-value models.

Inductive Bias Meta-Learning +2

Scalar is Not Enough: Vectorization-based Unbiased Learning to Rank

1 code implementation3 Jun 2022 Mouxiang Chen, Chenghao Liu, Zemin Liu, Jianling Sun

Most of the current ULTR methods are based on the examination hypothesis (EH), which assumes that the click probability can be factorized into two scalar functions, one related to ranking features and the other related to bias factors.


Continual Normalization: Rethinking Batch Normalization for Online Continual Learning

1 code implementation ICLR 2022 Quang Pham, Chenghao Liu, Steven Hoi

Existing continual learning methods use Batch Normalization (BN) to facilitate training and improve generalization across tasks.

Continual Learning

Learning Fast and Slow for Online Time Series Forecasting

1 code implementation23 Feb 2022 Quang Pham, Chenghao Liu, Doyen Sahoo, Steven C. H. Hoi

The fast adaptation capability of deep neural networks in non-stationary environments is critical for online time series forecasting.

Time Series Time Series Forecasting

CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

1 code implementation ICLR 2022 Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Motivated by the recent success of representation learning in computer vision and natural language processing, we argue that a more promising paradigm for time series forecasting, is to first learn disentangled feature representations, followed by a simple regression fine-tuning step -- we justify such a paradigm from a causal perspective.

Contrastive Learning Representation Learning +2

Node-wise Localization of Graph Neural Networks

1 code implementation27 Oct 2021 Zemin Liu, Yuan Fang, Chenghao Liu, Steven C. H. Hoi

Ideally, how a node receives its neighborhood information should be a function of its local context, to diverge from the global GNN model shared by all nodes.

Representation Learning

DualNet: Continual Learning, Fast and Slow

1 code implementation NeurIPS 2021 Quang Pham, Chenghao Liu, Steven Hoi

According to Complementary Learning Systems (CLS) theory~\citep{mcclelland1995there} in neuroscience, humans do effective \emph{continual learning} through two complementary systems: a fast learning system centered on the hippocampus for rapid learning of the specifics and individual experiences, and a slow learning system located in the neocortex for the gradual acquisition of structured knowledge about the environment.

Continual Learning Hippocampus +2

Modeling Dynamic Attributes for Next Basket Recommendation

no code implementations23 Sep 2021 Yongjun Chen, Jia Li, Chenghao Liu, Chenxi Li, Markus Anderle, Julian McAuley, Caiming Xiong

However, properly integrating them into user interest models is challenging since attribute dynamics can be diverse such as time-interval aware, periodic patterns (etc.

Next-basket recommendation

CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation

1 code implementation16 Aug 2021 Xinru Zhang, Chenghao Liu, Ni Ou, Xiangzhu Zeng, Xiaoliang Xiong, Yizhou Yu, Zhiwen Liu, Chuyang Ye

Data augmentation is a widely used strategy that improves the training of CNNs, and the design of the augmentation method for brain lesion segmentation is still an open problem.

Data Augmentation Lesion Segmentation +1

Contextual Transformation Networks for Online Continual Learning

no code implementations ICLR 2021 Quang Pham, Chenghao Liu, Doyen Sahoo, Steven Hoi

Continual learning methods with fixed architectures rely on a single network to learn models that can perform well on all tasks.

Continual Learning Transfer Learning

Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning Beyond Global Prior

no code implementations1 Jan 2021 Chenghao Liu, Tao Lu, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven Hoi

Meta-learning methods learn the meta-knowledge among various training tasks and aim to promote the learning of new tasks under the task similarity assumption.


Online Continual Learning Under Domain Shift

no code implementations1 Jan 2021 Quang Pham, Chenghao Liu, Steven Hoi

CIER employs an adversarial training to correct the shift in $P(X, Y)$ by matching $P(X|Y)$, which results in an invariant representation that can generalize to unseen domains during inference.

Continual Learning Test

Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling

no code implementations22 Oct 2020 Jianwen Yin, Chenghao Liu, Weiqing Wang, Jianling Sun, Steven C. H. Hoi

Sequential user behavior modeling plays a crucial role in online user-oriented services, such as product purchasing, news feed consumption, and online advertising.

Transfer Learning

Decentralized Knowledge Graph Representation Learning

no code implementations16 Oct 2020 Lingbing Guo, Weiqing Wang, Zequn Sun, Chenghao Liu, Wei Hu

Knowledge graph (KG) representation learning methods have achieved competitive performance in many KG-oriented tasks, among which the best ones are usually based on graph neural networks (GNNs), a powerful family of networks that learns the representation of an entity by aggregating the features of its neighbors and itself.

Entity Alignment Graph Representation Learning

Bilevel Continual Learning

1 code implementation30 Jul 2020 Quang Pham, Doyen Sahoo, Chenghao Liu, Steven C. H. Hoi

Continual learning aims to learn continuously from a stream of tasks and data in an online-learning fashion, being capable of exploiting what was learned previously to improve current and future tasks while still being able to perform well on the previous tasks.

Bilevel Optimization Continual Learning +2

Adaptive Task Sampling for Meta-Learning

no code implementations ECCV 2020 Chenghao Liu, Zhihao Wang, Doyen Sahoo, Yuan Fang, Kun Zhang, Steven C. H. Hoi

Meta-learning methods have been extensively studied and applied in computer vision, especially for few-shot classification tasks.

Classification General Classification +1

Graph Prototypical Networks for Few-shot Learning on Attributed Networks

1 code implementation23 Jun 2020 Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, Huan Liu

By constructing a pool of semi-supervised node classification tasks to mimic the real test environment, GPN is able to perform \textit{meta-learning} on an attributed network and derive a highly generalizable model for handling the target classification task.

Classification Drug Discovery +5

UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues

1 code implementation EMNLP 2020 Hung Le, Doyen Sahoo, Chenghao Liu, Nancy F. Chen, Steven C. H. Hoi

Building an end-to-end conversational agent for multi-domain task-oriented dialogues has been an open challenge for two main reasons.

Dialogue State Tracking

MCEN: Bridging Cross-Modal Gap between Cooking Recipes and Dish Images with Latent Variable Model

no code implementations CVPR 2020 Han Fu, Rui Wu, Chenghao Liu, Jianling Sun

Nowadays, driven by the increasing concern on diet and health, food computing has attracted enormous attention from both industry and research community.

Cross-Modal Retrieval Retrieval

A^2-GCN: An Attribute-aware Attentive GCN Model for Recommendation

no code implementations20 Mar 2020 Fan Liu, Zhiyong Cheng, Lei Zhu, Chenghao Liu, Liqiang Nie

Considering the fact that for different users, the attributes of an item have different influence on their preference for this item, we design a novel attention mechanism to filter the message passed from an item to a target user by considering the attribute information.

Recommendation Systems

Cross-Modal Food Retrieval: Learning a Joint Embedding of Food Images and Recipes with Semantic Consistency and Attention Mechanism

no code implementations9 Mar 2020 Hao Wang, Doyen Sahoo, Chenghao Liu, Ke Shu, Palakorn Achananuparp, Ee-Peng Lim, Steven C. H. Hoi

Food retrieval is an important task to perform analysis of food-related information, where we are interested in retrieving relevant information about the queried food item such as ingredients, cooking instructions, etc.

Cross-Modal Retrieval Retrieval

Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Leanring Beyond Global Prior

no code implementations25 Sep 2019 Chenghao Liu, Tao Lu, Doyen Sahoo, Yuan Fang, Steven C.H. Hoi.

Meta-learning methods learn the meta-knowledge among various training tasks and aim to promote the learning of new tasks under the task similarity assumption.


Feature Interaction-aware Graph Neural Networks

no code implementations19 Aug 2019 Kaize Ding, Yichuan Li, Jundong Li, Chenghao Liu, Huan Liu

Inspired by the immense success of deep learning, graph neural networks (GNNs) are widely used to learn powerful node representations and have demonstrated promising performance on different graph learning tasks.

Graph Learning Representation Learning

Compositional Coding for Collaborative Filtering

1 code implementation9 May 2019 Chenghao Liu, Tao Lu, Xin Wang, Zhiyong Cheng, Jianling Sun, Steven C. H. Hoi

However, CF with binary codes naturally suffers from low accuracy due to limited representation capability in each bit, which impedes it from modeling complex structure of the data.

Collaborative Filtering Recommendation Systems

Learning Cross-Modal Embeddings with Adversarial Networks for Cooking Recipes and Food Images

2 code implementations CVPR 2019 Hao Wang, Doyen Sahoo, Chenghao Liu, Ee-Peng Lim, Steven C. H. Hoi

Food computing is playing an increasingly important role in human daily life, and has found tremendous applications in guiding human behavior towards smart food consumption and healthy lifestyle.

Cross-Modal Retrieval Nutrition +2

Meta-Learning with Domain Adaptation for Few-Shot Learning under Domain Shift

no code implementations ICLR 2019 Doyen Sahoo, Hung Le, Chenghao Liu, Steven C. H. Hoi

Most existing work assumes that both training and test tasks are drawn from the same distribution, and a large amount of labeled data is available in the training tasks.

Domain Adaptation Few-Shot Learning +1

SL$^2$MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization

no code implementations20 Oct 2018 Yong Liu, Min Wu, Chenghao Liu, Xiao-Li Li, Jie Zheng

Moreover, we also incorporate biological knowledge about genes from protein-protein interaction (PPI) data and Gene Ontology (GO).

Malicious URL Detection using Machine Learning: A Survey

1 code implementation25 Jan 2017 Doyen Sahoo, Chenghao Liu, Steven C. H. Hoi

This article aims to provide a comprehensive survey and a structural understanding of Malicious URL Detection techniques using machine learning.

BIG-bench Machine Learning

SOL: A Library for Scalable Online Learning Algorithms

1 code implementation28 Oct 2016 Yue Wu, Steven C. H. Hoi, Chenghao Liu, Jing Lu, Doyen Sahoo, Nenghai Yu

SOL is an open-source library for scalable online learning algorithms, and is particularly suitable for learning with high-dimensional data.

BIG-bench Machine Learning General Classification +1

Online Bayesian Collaborative Topic Regression

no code implementations28 May 2016 Chenghao Liu, Tao Jin, Steven C. H. Hoi, Peilin Zhao, Jianling Sun

In this paper, we propose a novel scheme of Online Bayesian Collaborative Topic Regression (OBCTR) which is efficient and scalable for learning from data streams.

Recommendation Systems regression

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