b'Yatao Bian'

research

∙ 08/13/2023

SAILOR: Structural Augmentation Based Tail Node Representation Learning

Graph Neural Networks (GNNs) have achieved state-of-the-art performance ...

0 Jie Liao, et al. ∙

research

∙ 05/23/2023

SyNDock: N Rigid Protein Docking via Learnable Group Synchronization

The regulation of various cellular processes heavily relies on the prote...

0 Yuanfeng Ji, et al. ∙

research

∙ 04/22/2023

Towards Understanding Feature Learning in Out-of-Distribution Generalization

A common explanation for the failure of out-of-distribution (OOD) genera...

0 Yongqiang Chen, et al. ∙

research

∙ 04/09/2023

Reweighted Mixup for Subpopulation Shift

Subpopulation shift exists widely in many real-world applications, which...

0 Zongbo Han, et al. ∙

research

∙ 03/23/2023

Fairness-guided Few-shot Prompting for Large Language Models

Large language models have demonstrated surprising ability to perform in...

3 Huan Ma, et al. ∙

research

∙ 02/15/2023

Activity Cliff Prediction: Dataset and Benchmark

Activity cliffs (ACs), which are generally defined as pairs of structura...

0 Ziqiao Zhang, et al. ∙

research

∙ 10/08/2022

Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method

Few-shot object detection (FSOD) is to detect objects with a few example...

0 Lu Zhang, et al. ∙

research

∙ 09/19/2022

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup

Subpopulation shift wildly exists in many real-world machine learning ap...

0 Zongbo Han, et al. ∙

research

∙ 09/16/2022

ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery

The last decade has witnessed a prosperous development of computational ...

0 Lanqing Li, et al. ∙

research

∙ 07/28/2022

Diversity Boosted Learning for Domain Generalization with Large Number of Domains

Machine learning algorithms minimizing the average training loss usually...

0 Xi Leng, et al. ∙

research

∙ 06/15/2022

Pareto Invariant Risk Minimization

Despite the success of invariant risk minimization (IRM) in tackling the...

5 Yongqiang Chen, et al. ∙

research

∙ 05/20/2022

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

Deep graph learning has achieved remarkable progresses in both business ...

0 Bingzhe Wu, et al. ∙

research

∙ 04/16/2022

DRFLM: Distributionally Robust Federated Learning with Inter-client Noise via Local Mixup

Recently, federated learning has emerged as a promising approach for tra...

0 Bingzhe Wu, et al. ∙

research

∙ 03/31/2022

Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs

As a powerful tool for modeling complex relationships, hypergraphs are g...

0 Jiying Zhang, et al. ∙

research

∙ 03/20/2022

Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport

Recently, the pretrain-finetuning paradigm has attracted tons of attenti...

0 Jiying Zhang, et al. ∙

research

∙ 03/03/2022

Learning Set Functions Under the Optimal Subset Oracle via Equivariant Variational Inference

Learning set functions becomes increasingly more important in many appli...

0 Zijing Ou, et al. ∙

research

∙ 02/17/2022

Transformer for Graphs: An Overview from Architecture Perspective

Recently, Transformer model, which has achieved great success in many ar...

0 Erxue Min, et al. ∙

research

∙ 02/15/2022

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

Deep graph learning (DGL) has achieved remarkable progress in both busin...

0 Bingzhe Wu, et al. ∙

research

∙ 01/25/2022

Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction

Click-Through Rate (CTR) prediction, is an essential component of online...

0 Erxue Min, et al. ∙

research

∙ 01/24/2022

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

AI-aided drug discovery (AIDD) is gaining increasing popularity due to i...

1 Yuanfeng Ji, et al. ∙

research

∙ 12/14/2021

Generalization Bounds for Stochastic Gradient Langevin Dynamics: A Unified View via Information Leakage Analysis

Recently, generalization bounds of the non-convex empirical risk minimiz...

0 Bingzhe Wu, et al. ∙

research

∙ 11/15/2021

Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking

Protein complex formation is a central problem in biology, being involve...

21 Octavian-Eugen Ganea, et al. ∙

research

∙ 11/14/2021

p-Laplacian Based Graph Neural Networks

Graph neural networks (GNNs) have demonstrated superior performance for ...

0 Guoji Fu, et al. ∙

research

∙ 06/05/2021

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

Valuation problems, such as attribution-based feature interpretation, da...

0 Yatao Bian, et al. ∙

research

∙ 03/20/2021

Recognizing Predictive Substructures with Subgraph Information Bottleneck

The emergence of Graph Convolutional Network (GCN) has greatly boosted t...

0 Junchi Yu, et al. ∙

research

∙ 03/17/2021

Diversified Multiscale Graph Learning with Graph Self-Correction

Though the multiscale graph learning techniques have enabled advanced fe...

0 Yuzhao Chen, et al. ∙

research

∙ 11/04/2020

On Self-Distilling Graph Neural Network

Recently, the teacher-student knowledge distillation framework has demon...

0 Yuzhao Chen, et al. ∙

research

∙ 10/12/2020

Graph Information Bottleneck for Subgraph Recognition

Given the input graph and its label/property, several key problems of gr...

0 Junchi Yu, et al. ∙

research

∙ 06/24/2020

Continuous Submodular Function Maximization

Continuous submodular functions are a category of generally non-convex/n...

4 Yatao Bian, et al. ∙

research

∙ 06/01/2020

From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models

Submodular functions have been studied extensively in machine learning a...

18 Aytunc Sahin, et al. ∙

research

∙ 10/04/2016

Model Selection for Gaussian Process Regression by Approximation Set Coding

Gaussian processes are powerful, yet analytically tractable models for s...

0 Benjamin Fischer, et al. ∙

research

∙ 11/26/2013

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

In this paper, we present an approach to reconstruct 3-D human motion fr...

0 Jian Song, et al. ∙

Yatao Bian

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