LSE creators

Number of items: 14.
Article
  • Kalinke, Florian, Szabo, Zoltan, Sriperumbudur, Bharath K. (2025). Nyström Kernel Stein Discrepancy. Proceedings of Machine Learning Research, 258, 388 - 396. picture_as_pdf
  • Toth, Csaba, Oberhauser, Harald, Szabo, Zoltan (2025). Random Fourier signature features. SIAM Journal on Mathematics of Data Science, 7(1), 329 - 354. https://doi.org/10.1137/23M1620478 picture_as_pdf
  • Kalinke, Florian, Szabo, Zoltan (2023). Nyström M-Hilbert-Schmidt independence criterion. Proceedings of Machine Learning Research, 216, 1005-1015. picture_as_pdf
  • Aubin-Frankowski, Pierre-Cyril, Szabo, Zoltan (2022). Handling hard affine SDP shape constraints in RKHSs. Journal of Machine Learning Research, picture_as_pdf
  • Schrab, Antonin, Jitkrittum, Wittawat, Szabo, Zoltan, Sejdinovic, Dino, Gretton, Arthur (2022). Discussion of ‘Multi-scale Fisher’s independence test for multivariate dependence’. Biometrika, 109(3), 597 – 603. https://doi.org/10.1093/biomet/asac028 picture_as_pdf
  • Lambert, Alex, Bouche, Dimitri, Szabo, Zoltan, d'Alché-Buc, Florence (2022). Functional output regression with infimal convolution: exploring the Huber and ε-insensitive losses. Proceedings of Machine Learning Research, 162, 11844 - 1186. picture_as_pdf
    • Chapter
  • Kalinke, Florian, Szabo, Zoltan (2024). The minimax rate of HSIC estimation for translation-invariant kernels. In Globerson, A., Mackey, L., Belgrave, D., Fan, A., Paquet, U., Tomczak, J., Zhang, C. (Eds.), Advances in Neural Information Processing Systems 37 (NeurIPS 2024) . Neural Information Processing Systems Foundation. picture_as_pdf
  • Bonnier, Patric, Oberhauser, Harald, Szabo, Zoltan (2023). Kernelized cumulants: beyond kernel mean embeddings. In Advances in Neural Information Processing Systems 36 . Curran Associates, Inc.. picture_as_pdf
    • Conference or Workshop Item
  • Lambert, Alex, Bouche, Dimitri, Szabo, Zoltan, d'Alché-Buc, Florence (2022-07-17 - 2022-07-23) Functional Output Regression with Infimal Convolution: Exploring the Huber and ϵ-insensitive Losses [Paper]. International Conference on Machine Learning, Baltimore, MD, United States, USA. picture_as_pdf
  • Lambert, Alex, Parekh, Sanjeel, Szabo, Zoltan, d'Alché-Buc, Florence (2021-12-13) Continuous emotion transfer using kernels [Paper]. Controllable Generative Modeling in Language and Vision: CtrlGen Workshop at NeurIPS 2021, Online. picture_as_pdf
    • Report
  • Ma, Tao, Yang, Xuzhi, Szabo, Zoltan (2024). To switch or not to switch? Balanced policy switching in offline reinforcement learning. arXiv. https://doi.org/10.48550/arXiv.2407.01837 picture_as_pdf
  • Hao, Meiling, Su, Pingfan, Hu, Liyuan, Szabo, Zoltan, Zhao, Qianyu, Shi, Chengchun (2024). Forward and backward state abstractions for off-policy evaluation. arXiv. picture_as_pdf
  • Chamakh, Linda, Szabo, Zoltan (2021). Keep it tighter -- A story on analytical mean embeddings. arXiv. https://doi.org/10.48550/ARXIV.2110.09516 picture_as_pdf
    • Working paper
  • Cribeiro-Ramallo, Jose, Aich, Agnideep, Kalinke, Florian, Baran Aich, Ashit, Szabo, Zoltan (2025). The minimax lower bound of kernel Stein discrepancy estimation. arXiv. picture_as_pdf