16 citations · 55 across the 8 of their papers we have counts for
8 papers
PAC-Bayesian Generalization Bounds for Knowledge Graph Representation Learning
Jaejun Lee, Minsung Hwang, Joyce Jiyoung Whang
While a number of knowledge graph representation learning (KGRL) methods have been proposed over the past decade, very few theoretical analyses have been conducted on them. In this…
InGram: Inductive Knowledge Graph Embedding via Relation Graphs
Jaejun Lee, Chanyoung Chung, Joyce Jiyoung Whang
Inductive knowledge graph completion has been considered as the task of predicting missing triplets between new entities that are not observed during training. While most inductive…
Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers
Chanyoung Chung, Jaejun Lee, Joyce Jiyoung Whang
A hyper-relational knowledge graph has been recently studied where a triplet is associated with a set of qualifiers; a qualifier is composed of a relation and an entity, providing…
Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise
Giwon Hong, Jeonghwan Kim, Junmo Kang +2
Most existing retrieval-augmented language models (LMs) assume a naive dichotomy within a retrieved document set: query-relevance and irrelevance. Our work investigates a more chal…
Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Prediction
Chanyoung Chung, Joyce Jiyoung Whang
Knowledge graphs represent known facts using triplets. While existing knowledge graph embedding methods only consider the connections between entities, we propose considering the r…
Improving Test Distance for Failure Clustering with Hypergraph Modelling
Gabin An, Juyeon Yoon, Joyce Jiyoung Whang +1
Automated debugging techniques, such as Fault Localisation (FL) or Automated Program Repair (APR), are typically designed under the Single Fault Assumption (SFA). However, in pract…
Non-Exhaustive, Overlapping Co-Clustering: An Extended Analysis
Joyce Jiyoung Whang, Inderjit S. Dhillon
The goal of co-clustering is to simultaneously identify a clustering of rows as well as columns of a two dimensional data matrix. A number of co-clustering techniques have been pro…
Fast Multiplier Methods to Optimize Non-exhaustive, Overlapping Clustering
Yangyang Hou, Joyce Jiyoung Whang, David F. Gleich +1
Clustering is one of the most fundamental and important tasks in data mining. Traditional clustering algorithms, such as K-means, assign every data point to exactly one cluster. Ho…