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08/13/2023
SAILOR: Structural Augmentation Based Tail Node Representation Learning
Graph Neural Networks (GNNs) have achieved state-of-the-art performance ...
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05/23/2023
SyNDock: N Rigid Protein Docking via Learnable Group Synchronization
The regulation of various cellular processes heavily relies on the prote...
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04/22/2023
Towards Understanding Feature Learning in Out-of-Distribution Generalization
A common explanation for the failure of out-of-distribution (OOD) genera...
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04/09/2023
Reweighted Mixup for Subpopulation Shift
Subpopulation shift exists widely in many real-world applications, which...
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03/23/2023
Fairness-guided Few-shot Prompting for Large Language Models
Large language models have demonstrated surprising ability to perform in...
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02/15/2023
Activity Cliff Prediction: Dataset and Benchmark
Activity cliffs (ACs), which are generally defined as pairs of structura...
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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...
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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...
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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 ...
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07/28/2022
Diversity Boosted Learning for Domain Generalization with Large Number of Domains
Machine learning algorithms minimizing the average training loss usually...
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06/15/2022
Pareto Invariant Risk Minimization
Despite the success of invariant risk minimization (IRM) in tackling the...
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05/20/2022
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business ...
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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...
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03/31/2022
Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs
As a powerful tool for modeling complex relationships, hypergraphs are g...
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03/20/2022
Fine-Tuning Graph Neural Networks via Graph Topology induced Optimal Transport
Recently, the pretrain-finetuning paradigm has attracted tons of attenti...
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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...
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02/17/2022
Transformer for Graphs: An Overview from Architecture Perspective
Recently, Transformer model, which has achieved great success in many ar...
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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...
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01/25/2022
Masked Transformer for Neighhourhood-aware Click-Through Rate Prediction
Click-Through Rate (CTR) prediction, is an essential component of online...
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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...
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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...
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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...
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11/14/2021
p-Laplacian Based Graph Neural Networks
Graph neural networks (GNNs) have demonstrated superior performance for ...
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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...
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03/20/2021
Recognizing Predictive Substructures with Subgraph Information Bottleneck
The emergence of Graph Convolutional Network (GCN) has greatly boosted t...
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03/17/2021
Diversified Multiscale Graph Learning with Graph Self-Correction
Though the multiscale graph learning techniques have enabled advanced fe...
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11/04/2020
On Self-Distilling Graph Neural Network
Recently, the teacher-student knowledge distillation framework has demon...
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10/12/2020
Graph Information Bottleneck for Subgraph Recognition
Given the input graph and its label/property, several key problems of gr...
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06/24/2020
Continuous Submodular Function Maximization
Continuous submodular functions are a category of generally non-convex/n...
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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...
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10/04/2016
Model Selection for Gaussian Process Regression by Approximation Set Coding
Gaussian processes are powerful, yet analytically tractable models for s...
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11/26/2013
