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Results: 7
Number of items: 7
  • Open Access
    Xiao, Z., Yan, S., Hong, J., Cai, J., Jiang, X., Hu, Y., Shen, J., Wang, Q., & Snoek, C. G. M. (2025). DynaPrompt: Dynamic Test-Time Prompt Tuning. In The Thirteenth International Conference on Learning Representations: ICLR 2025 https://openreview.net/forum?id=EFZEdHB3Mp
  • Open Access
    Shen, J., Wang, Q., Xiao, Z., Van Noord, N., & Worring, M. (2025). GO4Align: Group Optimization for Multi-Task Alignment. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, & C. Zhang (Eds.), 38th Conference on Neural Information Processing Systems (NeurIPS 2024): 10-15 December 2024, Vancouver, Canada (pp. 111382-111405). (Advances in Neural Information Processing Systems; Vol. 37). Neural Information Processing Systems Foundation. https://doi.org/10.52202/079017-3537
  • Open Access
    Van Hoof, H., & Wang, Q. (2023). Learning Expressive Meta-Representations with Mixture of Expert Neural Processes. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), 36th Conference on Neural Information Processing Systems (NeurIPS 2022): New Orleans, Louisiana, USA, 28 November-9 December 2022 (Vol. 34, pp. 26242-26255). (Advances in Neural Information Processing Systems; Vol. 35). Neural Information Processing Systems Foundation. https://papers.nips.cc/paper_files/paper/2022/hash/a815fe7cad6af20a6c118f2072a881d2-Abstract-Conference.html
  • Open Access
    Wang, Q., Federici, M., & van Hoof, H. (2023). Bridge the Inference Gaps of Neural Processes via Expectation Maximization. In The Eleventh International Conference on Learning Representations: ICLR 2023 https://openreview.net/forum?id=A7v2DqLjZdq
  • Open Access
    Wang, Q., & van Hoof, H. (2022). Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models and Amortized Policy Search. Proceedings of Machine Learning Research, 162, 23055-23077. https://proceedings.mlr.press/v162/wang22z.html
  • Open Access
    Wang, Q. (2022). Functional representation learning for uncertainty quantification and fast skill transfer. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Wang, Q., & van Hoof, H. (2020). Doubly Stochastic Variational Inference for Neural Processes with Hierarchical Latent Variables. Proceedings of Machine Learning Research, 119, 10018-10028. http://proceedings.mlr.press/v119/wang20s.html
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