Sandeep Kumar

~Sandeep_Kumar8

论文总数

6.0

年均投稿

4.7

平均评分

接收情况1/12

会议分布

ICLR

NeurIPS

ICML

发表论文 (12 篇)

20257 篇

4.8

GraphFLEx: Structure Learning $\underline{\text{F}}$ramework for $\underline{\text{L}}$arge $\underline{\text{Ex}}$panding $\underline{\text{Graph}}$s

ICML 2025Rejected

6.0

AH-UGC: $\underline{\text{A}}$daptive and $\underline{\text{H}}$eterogeneous-$\underline{\text{U}}$niversal $\underline{\text{G}}$raph $\underline{\text{C}}$oarsening

NeurIPS 2025Rejected

4.0

Task and Model Agnostic Differentially Private Graph Neural Networks via Coarsening

ICLR 2025Rejected

5.8

SENSE: $\underline{\text{SEN}}$sing Similarity $\underline{\text{SE}}$eing Structure

NeurIPS 2025Rejected

5.8

GraphFLEx: Structure Learning $\underline{\text{F}}$ramework for $\underline{\text{L}}$arge $\underline{\text{Ex}}$panding $\underline{\text{Graph}}$s

NeurIPS 2025Rejected

3.0

Coarsening to Conceal: Enabling Privacy-Preserving Federated Learning for Graph Data

ICLR 2025Rejected

4.8

An Optimization-Based Framework for Adversarial Defence of Graph Neural Networks Via Adaptive Lipschitz Regularization

ICLR 2024Rejected

4.8

Structured Graph Reduction for Efficient GNN

ICLR 2024Rejected

3.4

Attributed Graph Clustering via Coarsening with Modularity

合作者 (18)

Sandeep Kumar

发表论文 (12 篇)

20257 篇

GraphFLEx: Structure Learning $\underline{\text{F}}$ramework for $\underline{\text{L}}$arge $\underline{\text{Ex}}$panding $\underline{\text{Graph}}$s

AH-UGC: $\underline{\text{A}}$daptive and $\underline{\text{H}}$eterogeneous-$\underline{\text{U}}$niversal $\underline{\text{G}}$raph $\underline{\text{C}}$oarsening

Task and Model Agnostic Differentially Private Graph Neural Networks via Coarsening

SENSE: $\underline{\text{SEN}}$sing Similarity $\underline{\text{SE}}$eing Structure

GraphFLEx: Structure Learning $\underline{\text{F}}$ramework for $\underline{\text{L}}$arge $\underline{\text{Ex}}$panding $\underline{\text{Graph}}$s

Coarsening to Conceal: Enabling Privacy-Preserving Federated Learning for Graph Data

Feature Driven Graph Coarsening for Scaling Graph Representation Learning

20245 篇

UGC: UNIVERSAL GRAPH COARSENING

UGC: Universal Graph Coarsening

An Optimization-Based Framework for Adversarial Defence of Graph Neural Networks Via Adaptive Lipschitz Regularization

Structured Graph Reduction for Efficient GNN

Attributed Graph Clustering via Coarsening with Modularity