activity
20172024
most citedOne for Many: Transfer Learning for Building HVAC Control

57 citations · 122 across the 11 of their papers we have counts for

collaborators

11 papers

cs.LG2024

DACR: Distribution-Augmented Contrastive Reconstruction for Time-Series Anomaly Detection

Lixu Wang, Shichao Xu, Xinyu Du +1

Anomaly detection in time-series data is crucial for identifying faults, failures, threats, and outliers across a range of applications. Recently, deep learning techniques have bee…

cs.LG2022★ 3 cited

Federated Class-Incremental Learning

Jiahua Dong, Lixu Wang, Zhen Fang +4

Federated learning (FL) has attracted growing attention via data-private collaborative training on decentralized clients. However, most existing methods unrealistically assume obje…

eess.SY2021

Co-designing Intelligent Control of Building HVACs and Microgrids

Rumia Masburah, Sayan Sinha, Rajib Lochan Jana +2

Building loads consume roughly 40% of the energy produced in developed countries, a significant part of which is invested towards building temperature-control infrastructure. There…

eess.SY2021★ 1 cited

Learning-based Framework for Sensor Fault-Tolerant Building HVAC Control with Model-assisted Learning

Shichao Xu, Yangyang Fu, Yixuan Wang +2

As people spend up to 87% of their time indoors, intelligent Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings are essential for maintaining occupant comfort a…

cs.LG2021★ 6 cited

Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Authorization

Lixu Wang, Shichao Xu, Ruiqi Xu +2

As Artificial Intelligence as a Service gains popularity, protecting well-trained models as intellectual property is becoming increasingly important. There are two common types of…

cs.LG2021

Weak Adaptation Learning -- Addressing Cross-domain Data Insufficiency with Weak Annotator

Shichao Xu, Lixu Wang, Yixuan Wang +1

Data quantity and quality are crucial factors for data-driven learning methods. In some target problem domains, there are not many data samples available, which could significantly…

cs.LG2020★ 5 cited

Addressing Class Imbalance in Federated Learning

Lixu Wang, Shichao Xu, Xiao Wang +1

Federated learning (FL) is a promising approach for training decentralized data located on local client devices while improving efficiency and privacy. However, the distribution an…

eess.SY2020★ 57 cited

One for Many: Transfer Learning for Building HVAC Control

Shichao Xu, Yixuan Wang, Yanzhi Wang +2

The design of building heating, ventilation, and air conditioning (HVAC) system is critically important, as it accounts for around half of building energy consumption and directly…

cs.LG2019★ 35 cited

Eavesdrop the Composition Proportion of Training Labels in Federated Learning

Lixu Wang, Shichao Xu, Xiao Wang +1

Federated learning (FL) has recently emerged as a new form of collaborative machine learning, where a common model can be learned while keeping all the training data on local devic…

cs.CV2019

MaskPlus: Improving Mask Generation for Instance Segmentation

Shichao Xu, Shuyue Lan, Qi Zhu

Instance segmentation is a promising yet challenging topic in computer vision. Recent approaches such as Mask R-CNN typically divide this problem into two parts -- a detection comp…

cs.AI2017★ 15 cited

Deep Reinforcement Learning: Framework, Applications, and Embedded Implementations

Hongjia Li, Tianshu Wei, Ao Ren +2

The recent breakthroughs of deep reinforcement learning (DRL) technique in Alpha Go and playing Atari have set a good example in handling large state and actions spaces of complica…