Liu Yang

发表

Distance Metric Learning: A Comprehensive Survey

Rong Jin, Liu Yang, Liu Yang, 2006 .

A theory of transfer learning with applications to active learning

Jaime G. Carbonell, Liu Yang, Steve Hanneke, 2013, Machine Learning.

Unifying discriminative visual codebook generation with classifier training for object category recognition

Rong Jin, Liu Yang, Frédéric Jurie, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

An Overview of Distance Metric Learning

Liu Yang, 2007 .

Adaptive Proactive Learning with Cost-Reliability Tradeoff

J. Carbonell, Liu Yang, 2009 .

Embedding New Samples via Locality-Constrained Sparse Representation for Nonlinear Manifold Learning

Liu Yang, Yunyan Wei, Feng Pan, 2015, 2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC).

SURROGATE LOSSES IN PASSIVE AND ACTIVE LEARNING BY STEVE HANNEKE∗

Liu Yang, 2015 .

ACTIVE LEARNING WITH IDENTIFIABLE MIXTURE MODELS

Liu Yang, 2016 .

Active Perspectives on Computational Learning and Testing Thesis Proposal

Liu Yang, Liu Yang, 2011 .

Rare Gems: Finding Lottery Tickets at Initialization

Kartik K. Sreenivasan, Hongyi Wang, Dimitris Papailiopoulos, 2022, NeurIPS.

Common latent space identification for heterogeneous co-transfer clustering

Jian Yu, Liu Yang, Liping Jing, 2015, Neurocomputing.

Heterogeneous Co-transfer Spectral Clustering

Jian Yu, Liu Yang, Liping Jing, 2014, RSKT.

Discriminative Cluster Refinement: Improving Object Category Recognition Given Limited Training Data

Rong Jin, Liu Yang, Rahul Sukthankar, 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

A discriminative and sparse topic model for image classification and annotation

Jian Yu, Michael K. Ng, Liu Yang, 2016, Image Vis. Comput..

Multi-Label Classification by Semi-Supervised Singular Value Decomposition

Jian Yu, Michael K. Ng, Liu Yang, 2017, IEEE Transactions on Image Processing.

Semi-supervised low-rank mapping learning for multi-label classification

Jian Yu, Michael K. Ng, Liu Yang, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Locally Weighted Fusion of Structural and Attribute Information in Graph Clustering

Jian Yu, Liu Yang, Caiyan Jia, 2019, IEEE Transactions on Cybernetics.

Semi-supervised Learning with Weakly-Related Unlabeled Data: Towards Better Text Categorization

Rong Jin, Liu Yang, Rahul Sukthankar, 2008, NIPS.

Semi-supervised Multi-label Learning by Constrained Non-negative Matrix Factorization

Yi Liu, Rong Jin, Liu Yang, 2006, AAAI.

Uncertainty-Aware Aggregation for Federated Open Set Domain Adaptation

Liu Yang, Chenyang Shen, Qinghua Hu, 2022, IEEE Transactions on Neural Networks and Learning Systems.

Statistical Learning under Nonstationary Mixing Processes pdf

Liu Yang, Steve Hanneke, Tommi Jaakkola, 2015, AISTATS.

Surrogate Losses in Passive and Active Learning pdf

Liu Yang, Steve Hanneke, Steve Hanneke, 2012, Electronic Journal of Statistics.

Learning with Changing Features pdf

Amit Dhurandhar, Liu Yang, Steve Hanneke, 2017, ArXiv.

Minimax Analysis of Active Learning

Liu Yang, Steve Hanneke, Steve Hanneke, 2014, J. Mach. Learn. Res..

Batch Mode Active Learning for Semantic Segmentation Based on Multi-Clue Sample Selection

Liu Yang, Qinghua Hu, Zhibin Du, 2019, CIKM.

Learning Transferred Weights From Co-Occurrence Data for Heterogeneous Transfer Learning

Jian Yu, Michael K. Ng, Liu Yang, 2016, IEEE Transactions on Neural Networks and Learning Systems.

Fast and Robust Compression of Deep Convolutional Neural Networks

Liu Yang, Jia Wen, Chenyang Shen, 2020, ICANN.

Active Property Testing

Maria-Florina Balcan, Liu Yang, Avrim Blum, 2011, 2012 IEEE 53rd Annual Symposium on Foundations of Computer Science.

A Boosting Framework for Visuality-Preserving Distance Metric Learning and Its Application to Medical Image Retrieval

Rong Jin, Mahadev Satyanarayanan, Steven C. H. Hoi, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Robust and Non-Negative Collective Matrix Factorization for Text-to-Image Transfer Learning

Michael K. Ng, Liu Yang, Liping Jing, 2015, IEEE Transactions on Image Processing.

Learning with a Drifting Target Concept

Varun Kanade, Liu Yang, Steve Hanneke, 2015, ALT.

Buy-in-Bulk Active Learning

Jaime G. Carbonell, Liu Yang, J. Carbonell, 2013, NIPS.

An Efficient Algorithm for Local Distance Metric Learning

Yi Liu, Rong Jin, Liu Yang, 2006, AAAI.

Learning distance metrics for interactive search-assisted diagnosis of mammograms

Rong Jin, Mei Chen, Mahadev Satyanarayanan, 2007, SPIE Medical Imaging.

Bandit Learnability can be Undecidable

L. Rosasco, Steve Hanneke, Gergely Neu, 2023, COLT.

Qinghua Hu, Liu Yang, Qilong Wang, 2018, IJCAI.

More Attentional Local Descriptors for Few-Shot Learning

Liu Yang, Fei Gao, Hui Li, 2020, ICANN.

Active Learning with a Drifting Distribution

Liu Yang, Liu Yang, 2011, NIPS.

Active Testing

A. Blum, Maria-Florina Balcan, Liu Yang, 2011, ArXiv.

Rare Gems: Finding Lottery Tickets at Initialization

Kartik K. Sreenivasan, Hongyi Wang, Dimitris Papailiopoulos, 2022, NeurIPS.

Bayesian Active Distance Metric Learning pdf

Rong Jin, Liu Yang, Rahul Sukthankar, 2007, UAI.

Bounds on the minimax rate for estimating a prior over a VC class from independent learning tasks pdf

Jaime G. Carbonell, Liu Yang, Steve Hanneke, 2015, Theor. Comput. Sci..

Cost Complexity of Proactive Learning via a Reduction to Realizable Active Learning

Jaime G. Carbonell, Liu Yang, J. Carbonell, 2009 .