Junkang Wu

Orcid: 0000-0001-6663-926X

According to our database1, Junkang Wu authored at least 23 papers between 2021 and 2025.

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

Timeline

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Links

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Bibliography

2025
On Negative-aware Preference Optimization for Recommendation.
CoRR, August, 2025

AdaViP: Aligning Multi-modal LLMs via Adaptive Vision-enhanced Preference Optimization.
CoRR, April, 2025

Aligning Multimodal LLM with Human Preference: A Survey.
CoRR, March, 2025

RePO: ReLU-based Preference Optimization.
CoRR, March, 2025

Larger or Smaller Reward Margins to Select Preferences for Alignment?
CoRR, March, 2025

MM-RLHF: The Next Step Forward in Multimodal LLM Alignment.
CoRR, February, 2025

DAMA: Data- and Model-aware Alignment of Multi-modal LLMs.
CoRR, February, 2025

Towards Robust Alignment of Language Models: Distributionally Robustifying Direct Preference Optimization.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Robust Preference Optimization via Dynamic Target Margins.
Proceedings of the Findings of the Association for Computational Linguistics, 2025

2024
Large Language Models Empower Personalized Valuation in Auction.
CoRR, 2024

α-DPO: Adaptive Reward Margin is What Direct Preference Optimization Needs.
CoRR, 2024

Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation.
Proceedings of the ACM on Web Conference 2024, 2024

β-DPO: Direct Preference Optimization with Dynamic β.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

BSL: Understanding and Improving Softmax Loss for Recommendation.
Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

Masked Graph Modeling with Multi- View Contrast.
Proceedings of the 40th IEEE International Conference on Data Engineering, 2024

Direct Multi-Turn Preference Optimization for Language Agents.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023
Adap-tau: Adaptively Modulating Embedding Magnitude for Recommendation.
CoRR, 2023

FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion.
CoRR, 2023

On the Theories Behind Hard Negative Sampling for Recommendation.
Proceedings of the ACM Web Conference 2023, 2023

Adap-τ : Adaptively Modulating Embedding Magnitude for Recommendation.
Proceedings of the ACM Web Conference 2023, 2023

Understanding Contrastive Learning via Distributionally Robust Optimization.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Discriminative-Invariant Representation Learning for Unbiased Recommendation.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

2021
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network.
Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021


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