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2025
2025
Learn to Teach: Improve Sample Efficiency in Teacher-student Learning for Sim-to-Real Transfer
IEEE Robotics and Automation Letters (RA-L), 2025
arXiv 2402.06783
Feiyang Wu, Xavier Nal, Jaehwi Jang, Wei Zhu, Zhaoyuan Gu, Anqi Wu, Ye Zhao
Learning Task-Agnostic Skill Bases to Uncover Motor Primitives in Animal Behaviors
arXiv 2506.15190
Jiyi Wang, Jingyang Ke, Bo Dai, Anqi Wu
The Forty-second International Conference on Machine Learning (ICML) 2025 (acceptance rate: 26.9%)
arXiv 2501.12633
Jingyang Ke, Feiyang Wu, Jiyi Wang, Jeffrey Markowitz, Anqi Wu
Learning Time-Varying Multi-Region Communications via Scalable Markovian Gaussian Processes
The Forty-second International Conference on Machine Learning (ICML) 2025 (oral: 1%)
arXiv 2407.00397
Weihan Li, Yule Wang, Chengrui Li, Anqi Wu
A Revisit of Total Correlation in Disentangled Variational Auto-Encoder with Partial Disentanglement
arXiv 2502.02279
Chengrui Li*, Yunmiao Wang*, Yule Wang, Weihan Li, Dieter Jaeger, Anqi Wu (* co-first authorships)
EmoBipedNav: Emotion-aware Social Navigation for Bipedal Robots with Deep Reinforcement Learning
arXiv 2503.12538
Wei Zhu, Abirath Raju, Abdulaziz Shamsah, Anqi Wu, Seth Hutchinson, Ye Zhao
2024
2024
Exploring Behavior-Relevant and Disentangled Neural Dynamics with Generative Diffusion Models
The Annual Conference on Neural Information Processing Systems (NeurIPS) 2024 (acceptance rate: 25.8%)
Yule Wang, Chengrui Li, Weihan Li, Anqi Wu
Transactions on Machine Learning Research (TMLR)
Chengrui Li, Anqi Wu
A Differentiable POGLM with Forward-Backward Message Passing
The Forty-first International Conference on Machine Learning (ICML) 2024 (acceptance rate: 27.5%)
Chengrui Li, Weihan Li, Yule Wang, Anqi Wu
The Forty-first International Conference on Machine Learning (ICML) 2024 (acceptance rate: 27.5%)
Weihan Li, Chengrui Li, Yule Wang, Anqi Wu
The International Conference on Robotics and Automation (ICRA) 2024
Feiyang Wu, Zhaoyuan Gu, Hanran Wu, Anqi Wu*, Ye Zhao* (*co-senior authorships)
Forward χ2 Divergence Based Variational Importance Sampling
The International Conference on Learning Representations (ICLR) 2024 (spotlight: 5%)
Chengrui Li, Yule Wang, Weihan Li, Anqi Wu
One-hot Generalized Linear Model for Switching Brain State Discovery
The International Conference on Learning Representations (ICLR) 2024 (acceptance rate: 31%)
Chengrui Li, Soon Ho Kim, Chris Rodgers, Hannah Choi, Anqi Wu
2023
2023
The Annual Conference on Neural Information Processing Systems (NeurIPS) 2023 (spotlight: 3%)
Yule Wang, Zijing Wu, Chengrui Li, Anqi Wu
Inverse Reinforcement Learning with the Average Reward Criterion
The Annual Conference on Neural Information Processing Systems (NeurIPS) 2023 (acceptance rate: 26%)
Feiyang Wu, Jingyang Ke, Anqi Wu
JGAT: A Joint Spatio-temporal Graph Attention Model for Brain Decoding
arXiv 2306.05286
Han Yi Chiu, Liang Zhao, Anqi Wu
2022
2022
Exp-\alpha: Beyond Proportional Aggregation in Federated Learning
Junjiao Tian, Xiaoliang Dai, Chih-Yao Ma, Zecheng He, Yen-Cheng Liu, Sayan Ghosh, Peter Vajda, Anqi Wu, Zsolt Kira
Semi-supervised sequence modeling for improved behavior segmentation
Computational and Systems Neuroscience (Cosyne), 2022
Matt Whiteway, Anqi Wu, Mia Bramel, Kelly Buchanan, Catherine Chen, Neeli Mishra, Evan Schaffer, Andres Villegas, The International Brain Laboratory, Liam Paninski
SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework
Computational and Systems Neuroscience (Cosyne), 2022, arXiv:2204.07072
Ari Blau, Christoph Gebhardt, Andrés Bendesky, Liam Paninski, Anqi Wu
2021
2021
Domain Generalization via Domain-based Covariance Minimization
arXiv 2110.06298
Anqi Wu
Partitioning variability in behavioral videos using semi-supervised deep generative models
PLOS Computational Biology. [PLOS][bioRxiv][code]
Matthew R. Whiteway, Dan Biderman, Yoni Friedman, Mario Dipoppa, E. Kelly Buchanan, Anqi Wu, John Zhou, Niccolò Bonacchi, Nathaniel J. Miska, Jean-Paul Noel, Erica Rodriguez, Michael Schartner, Karolina Socha, Anne E. Urai, C. Daniel Salzman, The International Brain Laboratory, John P. Cunningham, Liam Paninski
Brain kernel: a new spatial covariance function for fMRI data
NeuroImage. [web link][bioRxiv][code]
Anqi Wu, Samuel A. Nastase, Christopher A. Baldassano, Nicholas B. Turk-Browne, Kenneth A. Norman, Barbara E. Engelhardt, Jonathan W. Pillow
Neural Latents Benchmark ‘21: Evaluating latent variable models of neural population activity
NeurIPS 2021 Datasets and Benchmarks Track. [github.io]
Felix Pei, Joel Ye, David Zoltowski, Anqi Wu, Raeed H. Chowdhury, Hansem Sohn, Joseph E. O’Doherty, Krishna V. Shenoy, Matthew T. Kaufman, Mark Churchland, Mehrdad Jazayeri, Lee E. Miller, Jonathan Pillow, Il Memming Park, Eva L. Dyer, Chethan Pandarinath
Semi-supervised sequence modeling for improved behavioral segmentation
CV4Animals: Computer Vision for Animal Behavior Tracking and Modeling, in conjunction with Computer Vision and Pattern Recognition (CVPR), 2021. [code]
Matthew R Whiteway, Evan S Schaffer, Anqi Wu, E Kelly Buchanan, Omer F Onder, Neeli Mishra, Liam Paninski
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking
Computational and Systems Neuroscience (Cosyne), 2021
Anqi Wu*, E. Kelly Buchanan*, Matthew Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, Neeli Mishra, C. Daniel Salzman, Dora Angelaki, Andrés Bendesky, The International Brain Laboratory, John Cunningham, Liam Paninski (* equal contributions)
2020
2020
Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis
Neuropsychologia. [arXiv][web link]
Mingbo Cai, Michael Shvartsman, Anqi Wu, Hejia Zhang, Xia Zhu (all authors contributed equally)
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking
Advances in Neural Information Processing Systems (NeurIPS), 2020, (acceptance rate: 20%), [paper][bioRxiv][code][neurocaas]
Anqi Wu*, E. Kelly Buchanan*, Matthew Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, Neeli Mishra, C. Daniel Salzman, Dora Angelaki, Andrés Bendesky, The International Brain Laboratory, John Cunningham, Liam Paninski (* equal contribution)
2019
2019
Bayesian latent structure discovery for large-scale neural recordings
Thesis
Anqi Wu
Dependent relevance determination for smooth and structured sparse regression
Journal of Machine Learning Research (JMLR), [JMLR][arXiv][code]
Anqi Wu, Oluwasanmi Koyejo, Jonathan W. Pillow
Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks
Conference on Uncertainty in Artificial Intelligence (UAI), 2019, (oral presentation: 6.8%), [code][talk]
Roger She, Anqi Wu
Deterministic variational inference for robust Bayesian neural networks
Seventh International Conference on Learning Representations (ICLR), 2019, (oral presentation: 1.5%), [code-tensorflow][code-tensor2tensor][talk]
Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt
Gradient-based analysis of wide-field calcium imagining data using Gaussian processes
Computational and Systems Neuroscience (Cosyne), 2019
Mikio Aoi, Benjamin Scott, Anqi Wu, Stephan Thiberge, Carlos Brody, David W. Tank, Jonathan Pillow
Learning a latent manifold of odor representations in piriform cortex
Computational and Systems Neuroscience (Cosyne), 2019, (oral presentation: 3%), [talk]
Anqi Wu, Stan Pashkovski, Bob Datta, Jonathan W Pillow
2018
2018
Learning a latent manifold of odor representations from neural responses in piriform cortex
Advances in Neural Information Processing Systems (NIPS), 2018, (acceptance rate: 20.67%), [code]
Anqi Wu, Stan Pashkovski, Bob Datta, Jonathan W Pillow
Extracting nonlinear manifolds from spike train data with Gaussian process latent variables
Computational and Systems Neuroscience (Cosyne), 2018
Anqi Wu, Nicholas Roy, Stephen Keeley, Jonathan W. Pillow
Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature
arXiv 1704.00060
Anqi Wu, Mikio C. Aoi, Jonathan W. Pillow
2017
2017
Gaussian process based nonlinear latent structure discovery in multivariate spike train data
Advances in Neural Information Processing Systems (NIPS), 2017, (acceptance rate: 20.93%), [code]
Anqi Wu, Nicholas Roy, Stephen Keeley, Jonathan W Pillow
Brain Kernel: A covariance function for fMRI data using a large-scale Gaussian process latent variable model
The 11th conference on Bayesian Nonparametrics (BNP), 2017 (oral presentation)
Anqi Wu, Barbara Engelhardt, Jonathan W. Pillow
2016
2016
A Bayesian approach to structured sparsity for fMRI decoding
Computational and Systems Neuroscience (Cosyne), 2016
Anqi Wu, Oluwasanmi Koyejo, Jonathan W. Pillow
2015
2015
Convolutional spike-triggered covariance analysis for neural subunit models
Advances in Neural Information Processing Systems (NIPS), 2015, (acceptance rate: 21.93%), [code]
Anqi Wu, Il Memming Park, Jonathan W. Pillow
Convolutional spike-triggered covariance analysis for neural subunit models
Computational and Systems Neuroscience (Cosyne), 2015
Anqi Wu, Il Memming Park, Jonathan W. Pillow
2014
2014
Sparse bayesian structure learning with dependent relevance determination priors
Advances in Neural Information Processing Systems (NIPS), 2014, (acceptance rate: 24.67%), [code]
Anqi Wu, Mijung Park, Oluwasanmi O. Koyejo, Jonathan W. Pillow
2013
2013
Weighted task regularization for multitask learning
IEEE 13th International Conference on Data Mining Workshops (ICDMW), 2013
Yintao Liu, Anqi Wu, Dong Guo, Ke-Thia Yao, Cauligi S. Raghavendra
Global model for failure prediction for rod pump artificial lift systems
SPE Western Regional & AAPG Pacific Section Meeting 2013 Joint Technical Conference
Cauligi S. Raghavendra, Yintao Liu, Anqi Wu, Oluwafemi Balogun, Iraj Ershaghi, Jingwen Zheng, Dong Guo, Ke-Thia Yao
2011
2011
Dynamic time warping constraint learning for large margin nearest neighbor classification
Information Sciences. 181.13, 2011
Daren Yu, Xiao Yu, Qinghua Hu, Jinfu Liu, Anqi Wu
Making the nearest neighbor meaningful for time series classification
The 4th International Congress on Image and Signal Processing, 2011
Daren Yu, Xiao Yu, Anqi Wu
2010
2010
Large margin dimensionality reduction for time series
The first International Conference on Pervasive Computation, Signal Processing and Applications, 2010
Xiao Yu, Anqi Wu, Daren Yu
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