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Xingchen Wan
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2020 – today
- 2024
- [j4]Diego Granziol, Nicholas P. Baskerville, Xingchen Wan, Samuel Albanie, Stephen Roberts:
Iterate Averaging in the Quest for Best Test Error. J. Mach. Learn. Res. 25: 20:1-20:55 (2024) - [j3]Han Zhou, Xingchen Wan, Ivan Vulic, Anna Korhonen:
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning. Trans. Assoc. Comput. Linguistics 12: 525-542 (2024) - [c16]Dongyu Gong
, Xingchen Wan, Dingmin Wang:
Working Memory Capacity of ChatGPT: An Empirical Study. AAAI 2024: 10048-10056 - [c15]Masaki Adachi
Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach. AISTATS 2024: 496-504 - [c14]Han Zhou, Xingchen Wan, Yinhong Liu, Nigel Collier, Ivan Vulic, Anna Korhonen:
Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments. EMNLP 2024: 1241-1252 - [c13]Han Zhou
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering. ICLR 2024 - [i26]Huidong Liang, Xingchen Wan, Xiaowen Dong:
Bayesian Optimization of Functions over Node Subsets in Graphs. CoRR abs/2405.15119 (2024) - [i25]Han Zhou, Xingchen Wan, Yinhong Liu, Nigel Collier, Ivan Vulic, Anna Korhonen:
Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments. CoRR abs/2406.11370 (2024) - [i24]Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Sercan Ö. Arik:
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization. CoRR abs/2406.15708 (2024) - [i23]Hanjun Dai, Bethany Wang, Xingchen Wan, Bo Dai, Sherry Yang, Azade Nova, Pengcheng Yin, Phitchaya Mangpo Phothilimthana, Charles Sutton, Dale Schuurmans:
UQE: A Query Engine for Unstructured Databases. CoRR abs/2407.09522 (2024) - [i22]Fei Wang, Xingchen Wan, Ruoxi Sun, Jiefeng Chen, Sercan Ö. Arik:
Astute RAG: Overcoming Imperfect Retrieval Augmentation and Knowledge Conflicts for Large Language Models. CoRR abs/2410.07176 (2024) - 2023
- [j2]Saad Hamid, Xingchen Wan, Martin Jørgensen, Binxin Ru, Michael A. Osborne:
Bayesian Quadrature for Neural Ensemble Search. Trans. Mach. Learn. Res. 2023 (2023) - [c12]Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Ö. Arik, Tomas Pfister:
Better Zero-Shot Reasoning with Self-Adaptive Prompting. ACL (Findings) 2023: 3493-3514 - [c11]Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Eisenschlos, Sercan Ö. Arik, Tomas Pfister:
Universal Self-Adaptive Prompting. EMNLP 2023: 7437-7462 - [c10]Han Zhou
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning. EMNLP (Findings) 2023: 13064-13077 - [c9]Xingchen Wan, Pierre Osselin, Henry Kenlay, Binxin Ru, Michael A. Osborne, Xiaowen Dong:
Bayesian Optimisation of Functions on Graphs. NeurIPS 2023 - [i21]Han Zhou
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning. CoRR abs/2301.12132 (2023) - [i20]Saad Hamid, Xingchen Wan, Martin Jørgensen, Binxin Ru, Michael A. Osborne:
Bayesian Quadrature for Neural Ensemble Search. CoRR abs/2303.08874 (2023) - [i19]Xinghui Li, Kai Han, Xingchen Wan, Victor Adrian Prisacariu:
SimSC: A Simple Framework for Semantic Correspondence with Temperature Learning. CoRR abs/2305.02385 (2023) - [i18]Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Ö. Arik, Tomas Pfister:
Better Zero-Shot Reasoning with Self-Adaptive Prompting. CoRR abs/2305.14106 (2023) - [i17]Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Martin Eisenschlos, Sercan Ö. Arik, Tomas Pfister:
Universal Self-adaptive Prompting. CoRR abs/2305.14926 (2023) - [i16]Xingchen Wan, Pierre Osselin, Henry Kenlay, Binxin Ru, Michael A. Osborne, Xiaowen Dong:
Bayesian Optimisation of Functions on Graphs. CoRR abs/2306.05304 (2023) - [i15]Masaki Adachi, Satoshi Hayakawa, Xingchen Wan, Martin Jørgensen, Harald Oberhauser, Michael A. Osborne:
Domain-Agnostic Batch Bayesian Optimization with Diverse Constraints via Bayesian Quadrature. CoRR abs/2306.05843 (2023) - [i14]Han Zhou, Xingchen Wan, Lev Proleev, Diana Mincu, Jilin Chen, Katherine A. Heller, Subhrajit Roy:
Batch Calibration: Rethinking Calibration for In-Context Learning and Prompt Engineering. CoRR abs/2309.17249 (2023) - [i13]Han Zhou, Xingchen Wan, Ivan Vulic, Anna Korhonen:
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning. CoRR abs/2310.12774 (2023) - 2022
- [j1]Hao Yu
Estimation of Reference Voltages for Time-Difference Electrical Impedance Tomography. IEEE Trans. Instrum. Meas. 71: 1-10 (2022) - [c8]Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael A. Osborne:
Bayesian Generational Population-Based Training. AutoML 2022: 14/1-27 - [c7]Antoine Grosnit, Cédric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou-Ammar:
BOiLS: Bayesian Optimisation for Logic Synthesis. DATE 2022: 1193-1196 - [c6]Xingchen Wan, Binxin Ru, Pedro M. Esperança, Zhenguo Li:
On Redundancy and Diversity in Cell-based Neural Architecture Search. ICLR 2022 - [c5]Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy:
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. NeurIPS 2022 - [c4]Xingchen Wan, Binxin Ru, Pedro M. Esperança, Fabio Maria Carlucci:
Approximate Neural Architecture Search via Operation Distribution Learning. WACV 2022: 3545-3554 - [i12]Xingchen Wan, Binxin Ru, Pedro M. Esperança, Zhenguo Li:
On Redundancy and Diversity in Cell-based Neural Architecture Search. CoRR abs/2203.08887 (2022) - [i11]Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael A. Osborne:
Bayesian Generational Population-Based Training. CoRR abs/2207.09405 (2022) - [i10]Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy:
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. CoRR abs/2210.10199 (2022) - 2021
- [c3]Bin Xin Ru, Xingchen Wan, Xiaowen Dong, Michael A. Osborne:
Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels. ICLR 2021 - [c2]Xingchen Wan, Vu Nguyen, Huong Ha, Bin Xin Ru, Cong Lu, Michael A. Osborne:
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces. ICML 2021: 10663-10674 - [c1]Xingchen Wan, Henry Kenlay, Robin Ru, Arno Blaas, Michael A. Osborne, Xiaowen Dong:
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation. NeurIPS 2021: 6983-6996 - [i9]Xingchen Wan
Think Global and Act Local: Bayesian Optimisation over High-Dimensional Categorical and Mixed Search Spaces. CoRR abs/2102.07188 (2021) - [i8]Xingchen Wan
Adversarial Attacks on Graph Classification via Bayesian Optimisation. CoRR abs/2111.02842 (2021) - [i7]Xingchen Wan
Approximate Neural Architecture Search via Operation Distribution Learning. CoRR abs/2111.04670 (2021) - [i6]Antoine Grosnit, Cédric Malherbe, Rasul Tutunov, Xingchen Wan
BOiLS: Bayesian Optimisation for Logic Synthesis. CoRR abs/2111.06178 (2021) - 2020
- [i5]Diego Granziol, Xingchen Wan, Stephen Roberts:
Iterate Averaging Helps: An Alternative Perspective in Deep Learning. CoRR abs/2003.01247 (2020) - [i4]Bin Xin Ru, Xingchen Wan
Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel. CoRR abs/2006.07556 (2020) - [i3]Xingchen Wan, Jie Yang, Slavi Marinov, Jan-Peter Calliess, Stefan Zohren, Xiaowen Dong:
Sentiment Diffusion in Financial News Networks and Associated Market Movements. CoRR abs/2011.06430 (2020) - [i2]Diego Granziol, Samuel Albanie, Xingchen Wan, Stephen J. Roberts:
Explaining the Adaptive Generalisation Gap. CoRR abs/2011.08181 (2020)
2010 – 2019
- 2019
- [i1]Diego Granziol, Xingchen Wan, Timur Garipov, Dmitry P. Vetrov, Stephen Roberts:
MLRG Deep Curvature. CoRR abs/1912.09656 (2019)
Coauthor Index
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