Publications

Xingchen Wan, Han Zhou, Ruoxi Sun, Hootan Nakhost, Ke Jiang, Sercan Ö. Arık (2025). From Few to Many: Self-Improving Many-Shot Reasoners Through Iterative Optimization and Generation. International Conference on Learning Representations (ICLR).

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Han Zhou, Xingchen Wan, Ruoxi Sun, Hamid Palangi, Shariq Iqbal, Ivan Vulić, Anna Korhonen, Sercan Ö. Arık (2025). Multi-Agent Design: Optimizing Agents with Better Prompts and Topologies. arXiv preprint arXiv:2502.02533.

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Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Sercan Ö. Arık (2024). Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization. Advances in Neural Information Processing Systems (NeurIPS).

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Hanjun Dai, Bethany Yixin Wang, Xingchen Wan, Bo Dai, Sherry Yang, Azade Nova, Pengcheng Yin, Phitchaya Mangpo Phothilimthana, Charles Sutton, Dale Schuurmans (2024). UQE: A Query Engine for Unstructured Databases. Advances in Neural Information Processing Systems (NeurIPS).

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Huidong Liang, Xingchen Wan, Xiaowen Dong (2024). Bayesian Optimization of Functions over Node Subsets in Graphs. Advances in Neural Information Processing Systems (NeurIPS).

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Han Zhou, Xingchen Wan, Yinhong Liu, Nigel Collier, Ivan Vulić, Anna Korhonen (2024). Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments. Empirical Methods in Natural Language Processing (EMNLP).

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Fei Wang, Xingchen Wan, Ruoxi Sun, Jiefeng Chen, Sercan Ö. Arık (2024). Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models. arXiv preprint arXiv:2410.07176.

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Han Zhou, Xingchen Wan, Lev Proleev, Diana Mincu, Jilin Chen, Katherine Heller, Subhrajit Roy (2024). Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering. International Conference on Learning Representations (ICLR).

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Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Xingchen Wan, Vu Nguyen, Harald Oberhauser, Michael Osborne (2024). Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach. International Conference on Artificial Intelligence and Statistics (AISTATS).

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Dongyu Gong, Xingchen Wan, Dingmin Wang (2024). Working Memory Capacity of ChatGPT: An Empirical Study. AAAI Conference on Artificial Intelligence (AAAI).

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Han Zhou, Xingchen Wan, Ivan Vulić, Anna Korhonen (2024). AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning. Transactions of the Association for Computational Linguistics (TACL).

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Diego Granziol, Nick P. Baskerville, Xingchen Wan, Samuel Albanie, Stephen Roberts (2024). Iterate Averaging in the Quest for Best Test Error. Journal of Machine Learning Research (JMLR).

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Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Martin Eisenschlos, Sercan Ö. Arık, Tomas Pfister (2023). Universal Self-Adaptive Prompting. Empirical Methods in Natural Language Processing (EMNLP).

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Han Zhou, Xingchen Wan, Ivan Vulić, Anna Korhonen (2023). Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning. Findings of the Association for Computational Linguistics: EMNLP 2023.

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Xingchen Wan, Pierre Osselin, Henry Kenlay, Binxin Ru, Michael Osborne, Xiaowen Dong (2023). Bayesian Optimisation of Functions on Graphs. Advances in Neural Information Processing Systems (NeurIPS).

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Saad Hamid, Xingchen Wan, Martin Jørgensen, Binxin Ru, Michael Osborne (2023). Bayesian Quadrature for Neural Ensemble Search. Transactions on Machine Learning Research (TMLR).

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Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Ö. Arık, Tomas Pfister (2023). Better Zero-Shot Reasoning with Self-Adaptive Prompting. Findings of the Association for Computational Linguistics: ACL 2023.

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Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael Osborne, Eytan Bakshy (2022). Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. Advances in Neural Information Processing Systems (NeurIPS).

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Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael Osborne (2022). Bayesian Generational Population-Based Training. International Conference on Automated Machine Learning (AutoML-Conf).

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Xingchen Wan, Binxin Ru, Pedro M. Esperança, Zhenguo Li (2022). On Redundancy and Diversity in Cell-based Neural Architecture Search. International Conference on Learning Representations (ICLR).

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Antoine Grosnit, Cedric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou Ammar (2022). BOiLS: Bayesian Optimisation for Logic Synthesis. Design, Automation and Test in Europe (DATE).

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Xingchen Wan, Binxin Ru, Pedro M. Esperança, Fabio M. Carlucci (2022). Approximate Neural Architecture Search via Operation Distribution Learning. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

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Xingchen Wan, Henry Kenlay, Binxin Ru, Arno Blaas, Michael Osborne, Xiaowen Dong (2021). Adversarial Attacks on Graph Classifiers via Bayesian Optimisation. Advances in Neural Information Processing Systems (NeurIPS).

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Xingchen Wan, Vu Nguyen, Huong Ha, Binxin Ru, Cong Lu, Michael Osborne (2021). Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces. International Conference on Machine Learning (ICML).

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Binxin Ru, Xingchen Wan, Xiaowen Dong, Michael Osborne (2021). Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels. International Conference on Learning Representations (ICLR).

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Xingchen Wan, Jie Yang, Slavi Marinov, Jan-Peter Calliess, Stefan Zohren, Xiaowen Dong (2021). Sentiment Correlation in Financial News Networks and Associated Market Movements. Scientific Reports 11.

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