18 citations · 63 across the 24 of their papers we have counts for
9 papers · 1 filter
Intent-aware Multi-source Contrastive Alignment for Tag-enhanced Recommendation
Haolun Wu, Yingxue Zhang, Chen Ma +4
To offer accurate and diverse recommendation services, recent methods use auxiliary information to foster the learning process of user and item representations. Many SOTA methods f…
Evaluation of Categorical Generative Models -- Bridging the Gap Between Real and Synthetic Data
Florence Regol, Anja Kroon, Mark Coates
The machine learning community has mainly relied on real data to benchmark algorithms as it provides compelling evidence of model applicability. Evaluation on synthetic datasets ca…
DyG2Vec: Efficient Representation Learning for Dynamic Graphs
Mohammad Ali Alomrani, Mahdi Biparva, Yingxue Zhang +1
Temporal graph neural networks have shown promising results in learning inductive representations by automatically extracting temporal patterns. However, previous works often rely…
Contrastive Learning for Time Series on Dynamic Graphs
Yitian Zhang, Florence Regol, Antonios Valkanas +1
There have been several recent efforts towards developing representations for multivariate time-series in an unsupervised learning framework. Such representations can prove benefic…
Bidirectional Learning for Offline Infinite-width Model-based Optimization
Can Chen, Yingxue Zhang, Jie Fu +2
In offline model-based optimization, we strive to maximize a black-box objective function by only leveraging a static dataset of designs and their scores. This problem setting aris…
Node Copying: A Random Graph Model for Effective Graph Sampling
Florence Regol, Soumyasundar Pal, Jianing Sun +3
There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative…
Adapting Triplet Importance of Implicit Feedback for Personalized Recommendation
Haolun Wu, Chen Ma, Yingxue Zhang +3
Implicit feedback is frequently used for developing personalized recommendation services due to its ubiquity and accessibility in real-world systems. In order to effectively utiliz…
Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks
Soumyasundar Pal, Antonios Valkanas, Florence Regol +1
Multiple Instance Learning (MIL) is a weakly supervised learning problem where the aim is to assign labels to sets or bags of instances, as opposed to traditional supervised learni…
Motion Inbetweening via Deep -Interpolator
Boris N. Oreshkin, Antonios Valkanas, Félix G. Harvey +3
We show that the task of synthesizing human motion conditioned on a set of key frames can be solved more accurately and effectively if a deep learning based interpolator operates i…