Publications

Preprint

  • S. Grünewälder, “Compressed Empirical measures (in finite dimensions)”, ArXiv, 2023. [pdf]

Conference & Journal Publications

  • S. Grünewälder and A. Khaleghi, “Oblivious Data for Fairness with Kernels”, in Journal of Machine Learning Research, 2021. [pdf] [GitHub]
  • S. Page and S. Grünewälder, “The Goldenshluger-Lepski Method for Constrained Least-Squares Estimators over RKHSs”, in Bernoulli, 2021. [pdf]
  • C. Pike-Burke and S. Grünewälder, “Recovering Bandits”, in Advances in Neural Information Processing Systems (NeurIPS), 2019. [pdf]
  • S. Page and S. Grünewälder, “Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces”, in Journal of Machine Learning Research, 2019. [pdf]
  • S. Grünewälder and A. Khaleghi, “Approximations of the Restless Bandit Problem”, in Journal of Machine Learning Research, 2019. [pdf]
  • C. Pike-Burke, S. Agrawal, C. Szepesvári and S. Grünewälder, “Bandits with Delayed Anonymous Feedback”, International Conference on Machine Learning (ICML), 2018. [pdf]
  • S. Grünewälder, “Compact Convex Projections”, in Journal of Machine Learning Research, 2018. [pdf]
  • S. Grünewälder, “Plug-in Estimators for Conditional Expectations and Probabilities”, in Artificial Intelligence and Statistics (AISTATS), 2018. [pdf]
  • C. Pike-Burke and S. Grünewälder, “Optimistic Planning for the Stochastic Knapsack Problem,” in Artificial Intelligence and Statistics (AISTATS), 2017. [pdf]
  • F. Broekhuis, S. Grünewälder, J. McNutt, and D. W. Macdonald, “Optimal hunting conditions drive circalunar behavior of a diurnal canivore,” Behavioral Ecology, 2014.
  • S. Grünewälder, A. Gretton, and J. Shawe-Taylor, “Smooth operators,” in International Conference on Machine Learning (ICML), 2013. [pdf] [supplementary]
  • W. Böhmer, S. Grünewälder, Y. Shen, M. Musial, and K. Obermayer, “Construction of approximation spaces for reinforcement learning,” in Journal of Machine Learning Research, 2013.
  • S. Grünewälder*, G. Lever*, L. Baldassarre, M. Pontil, and A. Gretton, “Modelling transition dynamics in mdps with rkhs embeddings,” in International Conference on Machine Learning (ICML), 2012. [pdf]
  • S. Grünewälder*, G. Lever*, A. Gretton, L. Baldassarre, S. Patterson, and M. Pontil, “Conditional mean embeddings as regressors,” in International Conference on Machine Learning (ICML), 2012. [pdf]
  • W. Böhmer, S. Grünewälder, H. Nickisch, and K. Obermayer, “Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis,” Machine Learning (special issue for best ECML papers), 2012.
  • S. Grünewälder, F. Broekhuis, D. Macdonald, A. Wilson, J. McNutt, J. Shawe-Taylor, and S.Hailes, “Movement activity based classification of animal behaviour with an application to data from cheetah (Acinonyx jubatus),” In PLoS One, 2012. [PLOS One]
  • S. Grünewälder and K. Obermayer, “The optimal unbiased value estimator and its relation to LSTD, TD and MC,” in Machine Learning, 2011. [pdf]
  • W. Böhmer, S. Grünewälder, H. Nickisch, and K. Obermayer, “Regularized sparse kernel slow feature analysis,” in European Conference on Machine Learning (ECML), 2011.
  • S. Grünewälder, J.-Y. Audibert, M. Opper, and J. Shawe-Taylor, “Regret bounds for gaussian process bandit problems,” in Artificial Intelligence and Statistics (AISTATS), 2010. [pdf]
  • A. Onken, S. Grünewälder, M. Munk, and K. Obermayer, “Analyzing short-term noise dependencies of spike-counts in macaque prefrontal cortex using copulas and the flashlight transformation,” in PLoS Computational Biology, 2009.
  • A. Onken, S. Grünewälder, and K. Obermayer, “Correlation coefficients are insufficient for analyzing spike count dependencies,” in Advances in Neural Information Processing Systems (NeurIPS), 2009.
  • A. Onken, S. Grünewälder, M. Munk, and K. Obermayer, “Modeling short-term noisedependence of spike counts in macaque prefrontal cortex,” in Advances in Neural Information Processing Systems (NeurIPS), 2008.
  • S. Grünewälder, S. Hochreiter, and K. Obermayer, “Optimality of LSTD and its relation to MC,” in International Joint Conference on Neural Networks, 2007.
  • S. Grünewälder and K. Obermayer, “Attention driven memory,” in Annual Conference of the Cognitive Science Society, 2005. [pdf]