Bo Xue

Orcid: 0000-0002-7295-4853

Affiliations:
  • City University of Hong Kong, Hong Kong
  • Nanjing University, National Key Laboratory for Novel Software Technology, Nanjing, China (former)


According to our database1, Bo Xue authored at least 22 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
Improved Approximate Regret for Decentralized Online Continuous Submodular Maximization via Reductions.
CoRR, February, 2026

Beyond the Lower Bound: Bridging Regret Minimization and Best Arm Identification in Lexicographic Bandits.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

Parametric Pareto Set Learning for Expensive Multi-Objective Optimization.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

Offline Multi-Objective Bandits: From Logged Data to Pareto-Optimal Policies.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025
Consistency Is Not Always Correct: Towards Understanding the Role of Exploration in Post-Training Reasoning.
CoRR, November, 2025

Optimal and Efficient Algorithms for Decentralized Online Convex Optimization.
J. Mach. Learn. Res., 2025

Lexicographic Lipschitz Bandits: New Algorithms and a Lower Bound.
J. Mach. Learn. Res., 2025

DREAM: Improving Video-Text Retrieval Through Relevance-Based Augmentation Using Large Foundation Models.
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies, 2025

Problem-dependent Regret for Lexicographic Multi-Armed Bandits with Adversarial Corruptions.
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 2025

Multi-Objective Neural Bandits with Random Scalarization.
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 2025

Multi-objective Linear Reinforcement Learning with Lexicographic Rewards.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

Safe Online Convex Optimization with Heavy-Tailed Observation Noises.
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

Multiple Trade-offs: An Improved Approach for Lexicographic Linear Bandits.
Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024
Multiobjective Lipschitz Bandits under Lexicographic Ordering.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Hierarchize Pareto Dominance in Multi-Objective Stochastic Linear Bandits.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Hypervolume Maximization: A Geometric View of Pareto Set Learning.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Balance Act: Mitigating Hubness in Cross-Modal Retrieval with Query and Gallery Banks.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

2021
Deep Unified Cross-Modality Hashing by Pairwise Data Alignment.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Projection-free Online Learning in Dynamic Environments.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Piecewise Hashing: A Deep Hashing Method for Large-Scale Fine-Grained Search.
Proceedings of the Pattern Recognition and Computer Vision - Third Chinese Conference, 2020

Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs.
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020


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