Yatao Bian

Orcid: 0000-0002-2368-4084

Affiliations:
  • Tencent AI Lab, China
  • ETH Zürich, Department of Computer Science, Switzerland


According to our database1, Yatao Bian authored at least 86 papers between 2012 and 2024.

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Timeline

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Bibliography

2024
Recognizing Predictive Substructures With Subgraph Information Bottleneck.
IEEE Trans. Pattern Anal. Mach. Intell., March, 2024

Can Pretrained Models Really Learn Better Molecular Representations for AI-Aided Drug Discovery?
J. Chem. Inf. Model., 2024

HIGHT: Hierarchical Graph Tokenization for Graph-Language Alignment.
CoRR, 2024

One Fits All: Learning Fair Graph Neural Networks for Various Sensitive Attributes.
CoRR, 2024

How Interpretable Are Interpretable Graph Neural Networks?
CoRR, 2024

Graph Unitary Message Passing.
CoRR, 2024

Step-On-Feet Tuning: Scaling Self-Alignment of LLMs via Bootstrapping.
CoRR, 2024

Enhancing Neural Subset Selection: Integrating Background Information into Set Representations.
CoRR, 2024

Integration of cognitive tasks into artificial general intelligence test for large models.
CoRR, 2024

Rethinking and Simplifying Bootstrapped Graph Latents.
Proceedings of the 17th ACM International Conference on Web Search and Data Mining, 2024

2023
Positional Information Matters for Invariant In-Context Learning: A Case Study of Simple Function Classes.
CoRR, 2023

X-Mark: Towards Lossless Watermarking Through Lexical Redundancy.
CoRR, 2023

ETDock: A Novel Equivariant Transformer for Protein-Ligand Docking.
CoRR, 2023

Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators.
CoRR, 2023

SyNDock: N Rigid Protein Docking via Learnable Group Synchronization.
CoRR, 2023

Towards Understanding Feature Learning in Out-of-Distribution Generalization.
CoRR, 2023

Reweighted Mixup for Subpopulation Shift.
CoRR, 2023

Activity Cliff Prediction: Dataset and Benchmark.
CoRR, 2023

Curriculum Graph Poisoning.
Proceedings of the ACM Web Conference 2023, 2023

Learning Invariant Molecular Representation in Latent Discrete Space.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Simplifying and Empowering Transformers for Large-Graph Representations.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fairness-guided Few-shot Prompting for Large Language Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Understanding and Improving Feature Learning for Out-of-Distribution Generalization.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

BEEF: Bi-Compatible Class-Incremental Learning via Energy-Based Expansion and Fusion.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in Out-of-Distribution Generalization.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

RECAL: Sample-Relation Guided Confidence Calibration over Tabular Data.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2023, 2023

Beyond Factuality: A Comprehensive Evaluation of Large Language Models as Knowledge Generators.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

SAILOR: Structural Augmentation Based Tail Node Representation Learning.
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023

DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery - a Focus on Affinity Prediction Problems with Noise Annotations.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup.
CoRR, 2022

ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery.
CoRR, 2022

Can Pre-trained Models Really Learn Better Molecular Representations for AI-aided Drug Discovery?
CoRR, 2022

Diversity Boosted Learning for Domain Generalization with Large Number of Domains.
CoRR, 2022

Pareto Invariant Risk Minimization.
CoRR, 2022

A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection.
CoRR, 2022

DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup.
CoRR, 2022

Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs.
CoRR, 2022

Learning Set Functions Under the Optimal Subset Oracle via Equivariant Variational Inference.
CoRR, 2022

Transformer for Graphs: An Overview from Architecture Perspective.
CoRR, 2022

Recent Advances in Reliable Deep Graph Learning: Adversarial Attack, Inherent Noise, and Distribution Shift.
CoRR, 2022

Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction.
CoRR, 2022

DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery - A Focus on Affinity Prediction Problems with Noise Annotations.
CoRR, 2022

Cross-dependent graph neural networks for molecular property prediction.
Bioinform., 2022

Divide-and-Conquer: Post-User Interaction Network for Fake News Detection on Social Media.
Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

Diversified Multiscale Graph Learning with Graph Self-Correction.
Proceedings of the Topological, 2022

Neighbour Interaction based Click-Through Rate Prediction via Graph-masked Transformer.
Proceedings of the SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11, 2022

Learning Neural Set Functions Under the Optimal Subset Oracle.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method.
Proceedings of the MM '22: The 30th ACM International Conference on Multimedia, Lisboa, Portugal, October 10, 2022

Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Fine-Tuning Graph Neural Networks via Graph Topology Induced Optimal Transport.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

p-Laplacian Based Graph Neural Networks.
Proceedings of the International Conference on Machine Learning, 2022

Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning.
Proceedings of the Tenth International Conference on Learning Representations, 2022

MoDNA: motif-oriented pre-training for DNA language model.
Proceedings of the BCB '22: 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, Northbrook, Illinois, USA, August 7, 2022

2021
Generalization Bounds for Stochastic Gradient Langevin Dynamics: A Unified View via Information Leakage Analysis.
CoRR, 2021

Energy-Based Learning for Cooperative Games, with Applications to Feature/Data/Model Valuations.
CoRR, 2021

Diversified Multiscale Graph Learning with Graph Self-Correction.
CoRR, 2021

Not All Low-Pass Filters are Robust in Graph Convolutional Networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On Self-Distilling Graph Neural Network.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Graph Information Bottleneck for Subgraph Recognition.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
GROVER: Self-supervised Message Passing Transformer on Large-scale Molecular Data.
CoRR, 2020

Continuous Submodular Function Maximization.
CoRR, 2020

Dual Message Passing Neural Network for Molecular Property Prediction.
CoRR, 2020

Self-Supervised Graph Transformer on Large-Scale Molecular Data.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Provable Non-Convex Optimization and Algorithm Validation via Submodularity.
PhD thesis, 2019

Parallel Coordinate Descent Newton Method for Efficient L<sub>1</sub> -Regularized Loss Minimization.
IEEE Trans. Neural Networks Learn. Syst., 2019

Provable Non-Convex Optimization and Algorithm Validation via Submodularity.
CoRR, 2019

Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference.
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
COLA: Communication-Efficient Decentralized Linear Learning.
CoRR, 2018

Optimal DR-Submodular Maximization and Applications to Provable Mean Field Inference.
CoRR, 2018

COLA: Decentralized Linear Learning.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Distributed Second-Order Algorithm You Can Trust.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Guarantees for Greedy Maximization of Non-submodular Functions with Applications.
Proceedings of the 34th International Conference on Machine Learning, 2017

Model Selection for Gaussian Process Regression.
Proceedings of the Pattern Recognition - 39th German Conference, 2017

Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Information-theoretic analysis of MaxCut algorithms.
Proceedings of the 2016 Information Theory and Applications Workshop, 2016

2015
Greedy MaxCut algorithms and their information content.
Proceedings of the 2015 IEEE Information Theory Workshop, 2015

2013
Digitize Your Body and Action in 3-D at Over 10 FPS: Real Time Dense Voxel Reconstruction and Marker-less Motion Tracking via GPU Acceleration.
CoRR, 2013

Parallel Coordinate Descent Newton for Large-scale L1-Regularized Minimization.
CoRR, 2013

Bundle CDN: A Highly Parallelized Approach for Large-Scale ℓ1-Regularized Logistic Regression.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

2012
Parallelized Annealed Particle Filter for real-time marker-less motion tracking via heterogeneous computing.
Proceedings of the 21st International Conference on Pattern Recognition, 2012


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