Peer-reviewed Conference Proceedings

• Kuo, P,-C., Hou, H., Dabney, W., & Walker, E. Y. (2025). Predictive Coding Enhances Meta-RL To Achieve Bayes-Optimal Belief Representation Under Partial Observability. In NeurIPS 2025. (24.5% acceptance)

• Bull, M. S., Kuo, P,-C., …, & Buice, M. A. (2025). Volume Transmission Implements Context Factorization to Target Online Credit Assignment and Enable Compositional Generalization. In NeurIPS 2025. (24.5% acceptance)

• Kuo, P,-C. & Walker, E. Y. (2025). An Information-Theoretical Approach To Optimizing Task Design For Differentiating Probabilistic Neural Codes. In Data on the Brain & Mind Workshop @ NeurIPS 2025. (Oral, top 5)

• Kuo, P,-C., Hou, H., Dabney, W., & Walker, E. Y. (2024). Learning Bayes-Optimal Representation in Partially Observable Environments via Meta-Reinforcement Learning with Predictive Coding. In The First Workshop on NeuroAI @ NeurIPS2024.

• Brenner, M., Hess, F., Mikhaeil, J. M., Bereska, L. F., Monfared, Z., Kuo, P.-C., & Durstewitz, D. (2022). Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems. In ICML 2022. (21.9% acceptance)

• Cheng, H.-T., Yeh, C.-F., Kuo, P.-C., … & Liu, T.-L. (2020). Self-similarity student for partial label histopathology image segmentation. In ECCV 2020. (27.1% acceptance)

Peer-reviewed Journal Articles

• Kuo, P.-C., Cheng, H.-Y., Chen, P.-F., … & Chuang, J.-H. (2020). Assessment of Expert-Level Automated Detection of Plasmodium falciparum in Digitized Thin Blood Smear Images. JAMA network open.

Preprints

• Yeh, C.-F., Cheng, H.-T., Wei, A., Chen, H.-M., Kuo, P.-C., … & Liu, T.-L. (2019). A cascaded learning strategy for robust covid-19 pneumonia chest x-ray screening. arXiv preprint.

Book Chapters

• Prize-winning Works of National Taiwan University Student Laureate for Philosophical Treatise, Vol. 3. Lee H.-C. National Taiwan University Press. 2016.

• [Upcoming, Dec 2025] [Oral, Poster] NeurIPS 2025, Data on the Brain & Mind Workshop. An Information-Theoretical Approach To Optimizing Task Design For Differentiating Probabilistic Neural Codes. Kuo, P,-C. and Walker, E. Y.

• [Upcoming, Dec 2025] [Poster] NeurIPS 2025. Predictive Coding Enhances Meta-RL To Achieve Bayes-Optimal Belief Representation Under Partial Observability. Kuo, P,-C., Hou, H., Dabney, W., & Walker, E. Y.

• [Upcoming, Dec 2025] [Poster] NeurIPS 2025. Volume Transmission Implements Context Factorization to Target Online Credit Assignment and Enable Compositional Generalization. Bull, M. S., Kuo, P,-C., Smith, A. L., & Buice, M. A.

• [Upcoming, Nov 2025] [Poster] Society for Neuroscience, SfN 2025. An Information-Theoretical Approach To Optimizing Task Design For Distinguishing Probabilistic Codes In Neural Populations. Kuo, P,-C. and Walker, E. Y.

• [Sep 2025] [Poster] Lake Conference – Neural Coding & Dynamics 2025. Task Structures Shape Underlying Dynamical Systems That Implement Computation. Kuo, P,-C., Walker, E. Y., & Driscoll, L.

• [Jun 2025] [Spotlight, Poster] Multi-disciplinary Conference on Reinforcement Learning and Decision Making, RLDM 2025. Learning Bayes-Optimal Representation in Partially Observable Environments via Meta-Reinforcement Learning with Predictive Coding. Kuo, P,-C., Hou, H., Dabney, W., & Walker, E. Y.

• [Mar 2025] [Poster, Presenter Travel Award] Computational and Systems Neuroscience, COSYNE 2025. Task Structures Shape Underlying Dynamical Systems That Implement Computation. Kuo, P,-C., Walker, E. Y., & Driscoll, L.

• [Dec 2024] [Poster] The First Workshop on NeuroAI @ NeurIPS2024. Learning Bayes-Optimal Representation in Partially Observable Environments via Meta-Reinforcement Learning with Predictive Coding. Kuo, P,-C., Hou, H., Dabney, W., & Walker, E. Y.

• [Aug 2024] [Poster] Analytical Connectionism Summer School 2024. Uncovering the Computation of Dynamic Foraging with Actor-critic Recurrent Neural Networks. Kuo, P,-C., Driscoll, L., & Walker, E. Y.

• [Aug 2024] [Poster] Cognitive Computational Neuroscience, CCN 2024 Adaptive Learning Under Uncertainty With Variational Belief Deep Reinforcement Learning. Kuo, P.-C., Hou, H., & Walker, E. Y.

• [Jun 2024] [Poster] Research in Encoding And Decoding of Neural Ensembles, AREADNE 2024.An Information-Theoretical Approach To Optimize Task Design For Distinguishing Probabilistic Codes In Neural Populations. Kuo, P.-C. and Walker, E. Y.

• [May 2024] [Poster] CoNectome 2024 Symposium. Bayesian Reinforcement Learning For The Computational Basis Of Dynamic Foraging. Kuo, P.-C. and Walker, E. Y.

• [Feb 2024] [Poster] Janelia Conference, Bridging Diverse Perspectives on the Mechanistic Basis of Foraging. Bayesian Reinforcement Learning As A Mechanistic Model For Dynamic Foraging Behavior. Kuo, P.-C. and Walker, E. Y.

• [Upcoming, Dec 2025] NeurIPS 2025, Data on the Brain & Mind Workshop. An Information-Theoretical Approach To Optimizing Task Design For Differentiating Probabilistic Neural Codes.

• [Oct 2025] University of Washington, Grey Matters Journal Club. A NeuroAI Approach to Understanding Adaptive Learning Under Uncertainty.

• [Sep 2025] Stanford University, Prof. Andreas Tolias’ Laboratory. Deciphering Neural Representations Supporting Perception and Decision-Making Under Uncertainty.

• [Aug 2025] Allen Institute, Summer Workshop on the Dynamic Brain. Recurrent Neural Networks for Dynamic Foraging.

• [Feb 2025] University of Washington, Prof. Bing Brunton’s Laboratory. Learning Bayes-optimal Representations Under Partial Observability via Meta-Reinforcement Learning with Predictive Coding.

• [Jul 2024] TReND-CaMinA: Computational Neuroscience and Machine Learning in Africa. From Neural Variability to Population Coding

• [Feb 2024] University of Washington, NEUSCI 403 Lecture (Computational Models For Cognitive Neuroscience). Adaptive learning under uncertainty: learning to reinforcement learn with actor-critic recurrent neural networks.

• [Aug 2023] Allen Institute for Brain Science, Summer Workshop on the Dynamic Brain. What gives rise to neural variability and dynamics?