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Yatao A. Bian
Yatao A. Bian
Tencent AI Lab
Verified email at inf.ethz.ch - Homepage
Title
Cited by
Cited by
Year
Self-Supervised Graph Transformer on Large-Scale Molecular Data
Y Rong*, Y Bian*, T Xu, W Xie, Y Wei, W Huang, J Huang
Advances in Neural Information Processing Systems 33, 2020
2202020
Guarantees for Greedy Maximization of Non-submodular Functions with Applications
AA Bian, JM Buhmann, A Krause, S Tschiatschek
ICML 2017, 2017
2082017
Guaranteed non-convex optimization: Submodular maximization over continuous domains
AA Bian, B Mirzasoleiman, JM Buhmann, A Krause
AISTATS 2017, 2017
1242017
CoLa: Communication-Efficient Decentralized Linear Learning
L He*, A Bian*, M Jaggi
NeurIPS 2018, 2018
108*2018
Continuous DR-submodular Maximization: Structure and Algorithms
A Bian, K Levy, A Krause, JM Buhmann
NIPS 2017, 486-496, 2017
642017
Graph Information Bottleneck for Subgraph Recognition
J Yu, T Xu, Y Rong, Y Bian, J Huang, R He
ICLR 2021, 2020
442020
Cross-dependent graph neural networks for molecular property prediction
H Ma, Y Bian, Y Rong, W Huang, T Xu, W Xie, G Ye, J Huang
Bioinformatics 38 (7), 2003-2009, 2022
35*2022
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference
YA Bian, JM Buhmann, A Krause
ICML, 644-653, 2019
31*2019
Independent SE (3)-Equivariant Models for End-to-End Rigid Protein Docking
OE Ganea, X Huang, C Bunne, Y Bian, R Barzilay, T Jaakkola, A Krause
ICLR 2022 Spotlight, 2021
302021
A Distributed Second-Order Algorithm You Can Trust
C DŁnner, A Lucchi, M Gargiani, A Bian, T Hofmann, M Jaggi
ICML 2018, 2018
292018
On Self-Distilling Graph Neural Network
Y Chen, Y Bian, X Xiao, Y Rong, T Xu, J Huang
IJCAI 2021, 2020
152020
Continuous submodular function maximization
Y Bian, JM Buhmann, A Krause
arXiv preprint arXiv:2006.13474, 2020
152020
DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-aided Drug Discovery--A Focus on Affinity Prediction Problems with Noise Annotations
Y Ji, L Zhang, J Wu, B Wu, L Li, LK Huang, T Xu, Y Rong, J Ren, D Xue, ...
DataPerf Workshop at ICML 2022, 2022
13*2022
Recognizing Predictive Substructures with Subgraph Information Bottleneck
J Yu, T Xu, Y Rong, Y Bian, J Huang, R He
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
132021
Parallel Coordinate Descent Newton Method for Efficient -Regularized Loss Minimization
YA Bian, X Li, Y Liu, MH Yang
IEEE transactions on neural networks and learning systems 30 (11), 3233-3245, 2019
12*2019
Model selection for gaussian process regression
NS Gorbach, AA Bian, B Fischer, S Bauer, JM Buhmann
German Conference on Pattern Recognition, 306-318, 2017
122017
Invariance Principle Meets Out-of-Distribution Generalization on Graphs
Y Chen, Y Zhang, Y Bian, H Yang, K Ma, B Xie, T Liu, B Han, J Cheng
ICML Workshop on Spurious Correlations, Invariance and Stability, 2022
102022
Not all low-pass filters are robust in graph convolutional networks
H Chang, Y Rong, T Xu, Y Bian, S Zhou, X Wang, J Huang, W Zhu
Advances in Neural Information Processing Systems 34, 25058-25071, 2021
102021
Model selection for Gaussian process regression by approximation set coding
B Fischer, N Gorbach, S Bauer, Y Bian, JM Buhmann
arXiv preprint arXiv:1610.00907, 2016
92016
Greedy maxcut algorithms and their information content
Y Bian, A Gronskiy, JM Buhmann
2015 IEEE Information Theory Workshop (ITW), 1-5, 2015
82015
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Articles 1–20