activity
20092025
most citedLearning Robust Recommenders through Cross-Model Agreement

63 citations · 219 across the 20 of their papers we have counts for

collaborators

20 papers

cs.AI2025

Training Data Attribution for Image Generation using Ontology-Aligned Knowledge Graphs

Theodoros Aivalis, Iraklis A. Klampanos, Antonis Troumpoukis +1

As generative models become powerful, concerns around transparency, accountability, and copyright violations have intensified. Understanding how specific training data contributes…

cs.AI2025

Enhancing Interpretability in Generative AI Through Search-Based Data Influence Analysis

Theodoros Aivalis, Iraklis A. Klampanos, Antonis Troumpoukis +1

Generative AI models offer powerful capabilities but often lack transparency, making it difficult to interpret their output. This is critical in cases involving artistic or copyrig…

cs.LG2023

Label Denoising through Cross-Model Agreement

Yu Wang, Xin Xin, Zaiqiao Meng +2

Learning from corrupted labels is very common in real-world machine-learning applications. Memorizing such noisy labels could affect the learning of the model, leading to sub-optim…

cs.IR2023

Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems

Zhaochun Ren, Na Huang, Yidan Wang +7

Learning reinforcement learning (RL)-based recommenders from historical user-item interaction sequences is vital to generate high-reward recommendations and improve long-term cumul…

cs.IR2023★ 26 cited

Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment

Xin Xin, Xiangyuan Liu, Hanbing Wang +8

Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target…

cs.CV2022

Cross-modal Semantic Enhanced Interaction for Image-Sentence Retrieval

Xuri Ge, Fuhai Chen, Songpei Xu +2

Image-sentence retrieval has attracted extensive research attention in multimedia and computer vision due to its promising application. The key issue lies in jointly learning the v…