Xingchen Wan

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 43

Top Topics

Bayesian Decision

Neural Architecture

Working Memory Capacity

Top Venues

Publications

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 (2024)
Han Zhou, Xingchen Wan, Ivan Vulic, Anna Korhonen
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning. Trans. Assoc. Comput. Linguistics 12 (2024)
Masaki Adachi, Satoshi Hayakawa, Martin Jørgensen, Xingchen Wan, Vu Nguyen, Harald Oberhauser, Michael A. Osborne
Adaptive Batch Sizes for Active Learning: A Probabilistic Numerics Approach. AISTATS (2024)
Huidong Liang, Xingchen Wan, Xiaowen Dong
Bayesian Optimization of Functions over Node Subsets in Graphs. CoRR (2024)
Han Zhou, Xingchen Wan, Yinhong Liu, Nigel Collier, Ivan Vulic, Anna Korhonen
Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments. CoRR (2024)
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. ICLR (2024)
Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Sercan Ö. Arik
Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimization. CoRR (2024)
Dongyu Gong, Xingchen Wan, Dingmin Wang
Working Memory Capacity of ChatGPT: An Empirical Study. AAAI (2024)
Xingchen Wan, Pierre Osselin, Henry Kenlay, Binxin Ru, Michael A. Osborne, Xiaowen Dong
Bayesian Optimisation of Functions on Graphs. CoRR (2023)
Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Ö. Arik, Tomas Pfister
Better Zero-Shot Reasoning with Self-Adaptive Prompting. ACL (Findings) (2023)
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 (2023)
Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Martin Eisenschlos, Sercan Ö. Arik, Tomas Pfister
Universal Self-adaptive Prompting. CoRR (2023)
Han Zhou, Xingchen Wan, Ivan Vulic, Anna Korhonen
AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning. CoRR (2023)
Xingchen Wan, Ruoxi Sun, Hanjun Dai, Sercan Ö. Arik, Tomas Pfister
Better Zero-Shot Reasoning with Self-Adaptive Prompting. CoRR (2023)
Han Zhou, Xingchen Wan, Ivan Vulic, Anna Korhonen
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning. CoRR (2023)
Han Zhou, Xingchen Wan, Ivan Vulic, Anna Korhonen
Survival of the Most Influential Prompts: Efficient Black-Box Prompt Search via Clustering and Pruning. EMNLP (Findings) (2023)
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 (2023)
Xinghui Li, Kai Han, Xingchen Wan, Victor Adrian Prisacariu
SimSC: A Simple Framework for Semantic Correspondence with Temperature Learning. CoRR (2023)
Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai, Julian Eisenschlos, Sercan Ö. Arik, Tomas Pfister
Universal Self-Adaptive Prompting. EMNLP (2023)
Xingchen Wan, Pierre Osselin, Henry Kenlay, Binxin Ru, Michael A. Osborne, Xiaowen Dong
Bayesian Optimisation of Functions on Graphs. NeurIPS (2023)
Saad Hamid, Xingchen Wan, Martin Jørgensen, Binxin Ru, Michael A. Osborne
Bayesian Quadrature for Neural Ensemble Search. Trans. Mach. Learn. Res. 2023 (2023)
Saad Hamid, Xingchen Wan, Martin Jørgensen, Binxin Ru, Michael A. Osborne
Bayesian Quadrature for Neural Ensemble Search. CoRR (2023)
Xingchen Wan, Binxin Ru, Pedro M. Esperança, Zhenguo Li
On Redundancy and Diversity in Cell-based Neural Architecture Search. CoRR (2022)
Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael A. Osborne
Bayesian Generational Population-Based Training. AutoML (2022)
Xingchen Wan, Cong Lu, Jack Parker-Holder, Philip J. Ball, Vu Nguyen, Binxin Ru, Michael A. Osborne
Bayesian Generational Population-Based Training. CoRR (2022)
Hao Yu, Xingchen Wan, Zhongxu Dong, Zhixi Zhang, Jiabin Jia
Estimation of Reference Voltages for Time-Difference Electrical Impedance Tomography. IEEE Trans. Instrum. Meas. 71 (2022)
Xingchen Wan, Binxin Ru, Pedro M. Esperança, Zhenguo Li
On Redundancy and Diversity in Cell-based Neural Architecture Search. ICLR (2022)
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)
Antoine Grosnit, Cédric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou-Ammar
BOiLS: Bayesian Optimisation for Logic Synthesis. DATE (2022)
Xingchen Wan, Binxin Ru, Pedro M. Esperança, Fabio Maria Carlucci
Approximate Neural Architecture Search via Operation Distribution Learning. WACV (2022)
Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat, Michael A. Osborne, Eytan Bakshy
Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization. CoRR (2022)
Xingchen Wan, Henry Kenlay, Binxin Ru, Arno Blaas, Michael A. Osborne, Xiaowen Dong
Adversarial Attacks on Graph Classification via Bayesian Optimisation. CoRR (2021)
Antoine Grosnit, Cédric Malherbe, Rasul Tutunov, Xingchen Wan, Jun Wang, Haitham Bou-Ammar
BOiLS: Bayesian Optimisation for Logic Synthesis. CoRR (2021)
Xingchen Wan, Binxin Ru, Pedro M. Esperança, Fabio Maria Carlucci
Approximate Neural Architecture Search via Operation Distribution Learning. CoRR (2021)
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)
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. CoRR (2021)
Xingchen Wan, Henry Kenlay, Robin Ru, Arno Blaas, Michael A. Osborne, Xiaowen Dong
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation. NeurIPS (2021)
Bin Xin Ru, Xingchen Wan, Xiaowen Dong, Michael A. Osborne
Interpretable Neural Architecture Search via Bayesian Optimisation with Weisfeiler-Lehman Kernels. ICLR (2021)
Diego Granziol, Xingchen Wan, Stephen Roberts
Iterate Averaging Helps: An Alternative Perspective in Deep Learning. CoRR (2020)
Xingchen Wan, Jie Yang, Slavi Marinov, Jan-Peter Calliess, Stefan Zohren, Xiaowen Dong
Sentiment Diffusion in Financial News Networks and Associated Market Movements. CoRR (2020)
Bin Xin Ru, Xingchen Wan, Xiaowen Dong, Michael A. Osborne
Neural Architecture Search using Bayesian Optimisation with Weisfeiler-Lehman Kernel. CoRR (2020)
Diego Granziol, Samuel Albanie, Xingchen Wan, Stephen J. Roberts
Explaining the Adaptive Generalisation Gap. CoRR (2020)
Diego Granziol, Xingchen Wan, Timur Garipov, Dmitry P. Vetrov, Stephen Roberts
MLRG Deep Curvature. CoRR (2019)