Institution profile

Catholic University of Korea

Academic institutionasia · kr
Official website
Research library12linked papers
Opportunities0open roles
Selected work

Representative Papers

Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving

Feb 09, 2026

This work proposes Federated Inference (FI) as a complementary paradigm to federated learning, enabling secure collaboration among private models during inference without sharing data or model parameters. The study introduces the first unified abstraction framework for FI, articulating two core objectives: preserving privacy during inference and achieving performance gains through collaboration. Building upon secure multi-party computation, the authors design a privacy-preserving collaborative inference architecture that integrates ensemble learning and incentive mechanisms. Systematic modeling and empirical analysis are conducted under non-IID data distributions and stringent privacy constraints. Experimental results reveal critical trade-offs among privacy, collaboration efficacy, and incentive alignment, underscoring the necessity of designing FI systems independently from conventional training-centric paradigms.

0 citationsRead paper

Context-aware Graph Causality Inference for Few-Shot Molecular Property Prediction

Jan 16, 2026

This work addresses the challenge of leveraging functional group–based causal priors and identifying critical substructures in few-shot molecular property prediction. To this end, we propose CaMol, a novel framework that introduces causal inference into this task for the first time. By constructing a contextual graph that integrates functional groups, molecular structures, and target properties, and combining it with a learnable atomic masking strategy and a chemistry-informed backdoor adjustment mechanism, CaMol effectively disentangles causal effects from confounding factors to identify substructures directly causally linked to the target property. Extensive experiments demonstrate that CaMol significantly improves both prediction accuracy and sample efficiency across multiple datasets. Moreover, the identified causal substructures show strong alignment with known functional groups, highlighting the model’s high performance and interpretability.

0 citationsRead paper

Pre-training Graph Neural Networks on 2D and 3D Molecular Structures by using Multi-View Conditional Information Bottleneck

Nov 23, 2025

Existing molecular graph pretraining struggles to simultaneously ensure cross-view (2D/3D) semantic consistency and alignment of functionally critical substructures. To address this, we propose the Multi-View Conditional Information Bottleneck (MVCIB) framework, which achieves fine-grained cross-view alignment via context-guided representation learning and a functional-group-driven substructure anchoring mechanism. MVCIB is the first method to attain geometric discriminability at the 3D Weisfeiler–Lehman hierarchy level. It integrates ego-network modeling with cross-view attention to jointly optimize 2D topological and 3D geometric representations. Evaluated on four molecular property prediction benchmarks, MVCIB significantly outperforms state-of-the-art methods—particularly excelling at distinguishing stereoisomers with identical 2D graphs but distinct 3D conformations. The framework delivers both superior predictive performance and enhanced interpretability through principled substructure grounding.

0 citationsRead paper
Recent publications

Latest Papers

Federated Inference: Toward Privacy-Preserving Collaborative and Incentivized Model Serving

Feb 09, 2026

This work proposes Federated Inference (FI) as a complementary paradigm to federated learning, enabling secure collaboration among private models during inference without sharing data or model parameters. The study introduces the first unified abstraction framework for FI, articulating two core objectives: preserving privacy during inference and achieving performance gains through collaboration. Building upon secure multi-party computation, the authors design a privacy-preserving collaborative inference architecture that integrates ensemble learning and incentive mechanisms. Systematic modeling and empirical analysis are conducted under non-IID data distributions and stringent privacy constraints. Experimental results reveal critical trade-offs among privacy, collaboration efficacy, and incentive alignment, underscoring the necessity of designing FI systems independently from conventional training-centric paradigms.

0 citationsRead paper

Context-aware Graph Causality Inference for Few-Shot Molecular Property Prediction

Jan 16, 2026

This work addresses the challenge of leveraging functional group–based causal priors and identifying critical substructures in few-shot molecular property prediction. To this end, we propose CaMol, a novel framework that introduces causal inference into this task for the first time. By constructing a contextual graph that integrates functional groups, molecular structures, and target properties, and combining it with a learnable atomic masking strategy and a chemistry-informed backdoor adjustment mechanism, CaMol effectively disentangles causal effects from confounding factors to identify substructures directly causally linked to the target property. Extensive experiments demonstrate that CaMol significantly improves both prediction accuracy and sample efficiency across multiple datasets. Moreover, the identified causal substructures show strong alignment with known functional groups, highlighting the model’s high performance and interpretability.

0 citationsRead paper

Pre-training Graph Neural Networks on 2D and 3D Molecular Structures by using Multi-View Conditional Information Bottleneck

Nov 23, 2025

Existing molecular graph pretraining struggles to simultaneously ensure cross-view (2D/3D) semantic consistency and alignment of functionally critical substructures. To address this, we propose the Multi-View Conditional Information Bottleneck (MVCIB) framework, which achieves fine-grained cross-view alignment via context-guided representation learning and a functional-group-driven substructure anchoring mechanism. MVCIB is the first method to attain geometric discriminability at the 3D Weisfeiler–Lehman hierarchy level. It integrates ego-network modeling with cross-view attention to jointly optimize 2D topological and 3D geometric representations. Evaluated on four molecular property prediction benchmarks, MVCIB significantly outperforms state-of-the-art methods—particularly excelling at distinguishing stereoisomers with identical 2D graphs but distinct 3D conformations. The framework delivers both superior predictive performance and enhanced interpretability through principled substructure grounding.

0 citationsRead paper