Jialin Yu

Orcid: 0000-0003-1381-2203

Affiliations:
  • University of Oxford, Department of Engineering Science, Oxford, UK
  • University College London, UK (2023 - 2025)
  • Durham University, Department of Computer Science, Durham, UK (PhD 2023)


According to our database1, Jialin Yu authored at least 40 papers between 2020 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
ELAN4D: Embodiment-Centric 4D Supervision for Vision-Language-Action Models via Plug-and-Play Adaptation.
CoRR, May, 2026

The Path Matters: Learning a Token-Commitment Policy for Diffusion Language Models.
CoRR, May, 2026

Retrieval-Augmented Linguistic Calibration.
CoRR, May, 2026

Checkup2Action: A Multimodal Clinical Check-up Report Dataset for Patient-Oriented Action Card Generation.
CoRR, May, 2026

Measuring Black-Box Confidence via Reasoning Trajectories: Geometry, Coverage, and Verbalization.
CoRR, May, 2026

Human-Centric Topic Modeling with Goal-Prompted Contrastive Learning and Optimal Transport.
CoRR, April, 2026

Deep Autocorrelation Modeling for Time-Series Forecasting: Progress and Prospects.
CoRR, March, 2026

Benchmarking at the Edge of Comprehension.
CoRR, February, 2026

MechPert: Mechanistic Consensus as an Inductive Bias for Unseen Perturbation Prediction.
CoRR, February, 2026

A Fragile Guardrail: Diffusion LLM's Safety Blessing and Its Failure Mode.
CoRR, February, 2026

Permission Manifests for Web Agents.
CoRR, January, 2026

Towards Multi-Label Text Interpretation with Chain-of-Thought Prompting and Contextualized Knowledge.
Proceedings of the ACM Web Conference 2026, 2026

2025
ToolTweak: An Attack on Tool Selection in LLM-based Agents.
CoRR, October, 2025

TraceDet: Hallucination Detection from the Decoding Trace of Diffusion Large Language Models.
CoRR, October, 2025

BiasBusters: Uncovering and Mitigating Tool Selection Bias in Large Language Models.
CoRR, October, 2025

Can Large Language Models Express Uncertainty Like Human?
CoRR, September, 2025

Causal Analysis of Intellectual Complexity in Software Engineering.
Proceedings of the International Joint Conference on Neural Networks, 2025

2024
Fine-Tuning Pre-trained Language Models for Robust Causal Representation Learning.
CoRR, 2024

Structured Learning of Compositional Sequential Interventions.
Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

2023
Refined feature fusion for in-field high-density and multi-scale rice panicle counting in UAV images.
Comput. Electron. Agric., August, 2023

Language as a latent sequence: Deep latent variable models for semi-supervised paraphrase generation.
AI Open, January, 2023

Deep Latent Variable Models for Semi-supervised Paraphrase Generation.
CoRR, 2023

Improving Health Mention Classification Through Emphasising Literal Meanings: A Study Towards Diversity and Generalisation for Public Health Surveillance.
Proceedings of the ACM Web Conference 2023, 2023

Intervention Generalization: A View from Factor Graph Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adaptive Distance Message Passing From the Multi-Relational Edge View.
Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

Religion and Spirituality on Social Media in the Aftermath of the Global Pandemic.
Proceedings of the IEEE International Conference on Big Data, 2023

Incorporating Emotions into Health Mention Classification Task on Social Media.
Proceedings of the IEEE International Conference on Big Data, 2023

2022
Religion and Spirituality on Social Media in the Aftermath of the Global Pandemic.
CoRR, 2022

Multi-task Learning for Personal Health Mention Detection on Social Media.
CoRR, 2022

Efficient Uncertainty Quantification for Multilabel Text Classification.
Proceedings of the International Joint Conference on Neural Networks, 2022

INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations.
Proceedings of the International Joint Conference on Neural Networks, 2022

Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification.
Proceedings of the International Joint Conference on Neural Networks, 2022

Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention.
Proceedings of the IEEE International Conference on Big Data, 2022

2021
Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums.
Proceedings of the Intelligent Tutoring Systems - 17th International Conference, 2021

A Brief Survey of Deep Learning Approaches for Learning Analytics on MOOCs.
Proceedings of the Intelligent Tutoring Systems - 17th International Conference, 2021

A Generative Bayesian Graph Attention Network for Semi-Supervised Classification on Scarce Data.
Proceedings of the International Joint Conference on Neural Networks, 2021

Detecting Fine-Grained Emotions on Social Media during major Disease Outbreaks: Health and Well-being before and during the COVID-19 Pandemic.
Proceedings of the AMIA 2021, American Medical Informatics Association Annual Symposium, San Diego, CA, USA, October 30, 2021, 2021

2020
Sentence Contextual Encoder with BERT and BiLSTM for Automatic Classification with Imbalanced Medication Tweets.
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task, 2020

Temporal Sentiment Analysis of Learners: Public Versus Private Social Media Communication Channels in a Women-in-Tech Conversion Course.
Proceedings of the 15th International Conference on Computer Science & Education, 2020

Digital Inclusion in Nothern England: Training Women from Underrepresented Communities in Tech: A Data Analytics Case Study.
Proceedings of the 15th International Conference on Computer Science & Education, 2020


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