Optimality of LSTD and its Relation to MC. - Steffen Grünewälder; Sepp Hochreiter; Klaus Obermayer (IJCNN, 2007)

Modeling Short-term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex. - Arno Onken; Steffen Grünewälder; Matthias H. J. Munk; Klaus Obermayer (NIPS, 2008)

Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies. - Arno Onken; Steffen Grünewälder; Klaus Obermayer (NIPS, 2009)

Analyzing Short-Term Noise Dependencies of Spike-Counts in Macaque Prefrontal Cortex Using Copulas and the Flashlight Transformation. - Arno Onken; Steffen Grünewälder; Matthias H. J. Munk; Klaus Obermayer (PLoS Comput. Biol., 2009)

Application of statistical estimation theory, adaptive sensory systems and time series processing to reinforcement learning. - Steffen Grünewälder (2009)

Regret Bounds for Gaussian Process Bandit Problems. - Steffen Grünewälder; Jean-Yves Audibert; Manfred Opper; John Shawe-Taylor (AISTATS, 2010)

Regularized Sparse Kernel Slow Feature Analysis. - Wendelin Böhmer; Steffen Grünewälder; Hannes Nickisch; Klaus Obermayer (ECML/PKDD (1), 2011)

A Unified Framework for Risk-sensitive Markov Decision Processes with Finite State and Action Spaces - Yun Shen; Steffen Grünewälder; Klaus Obermayer (CoRR, 2011)

The optimal unbiased value estimator and its relation to LSTD, TD and MC. - Steffen Grünewälder; Klaus Obermayer (Mach. Learn., 2011)

Modelling transition dynamics in MDPs with RKHS embeddings. - Steffen Grünewälder; Guy Lever; Luca Baldassarre; Massimiliano Pontil; Arthur Gretton (ICML, 2012)

Conditional mean embeddings as regressors. - Steffen Grünewälder; Guy Lever; Arthur Gretton; Luca Baldassarre; Sam Patterson; Massimiliano Pontil (ICML, 2012)

Conditional mean embeddings as regressors - supplementary - Steffen Grünewälder; Guy Lever; Luca Baldassarre; Sam Patterson; Arthur Gretton; Massimiliano Pontil (CoRR, 2012)

Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis. - Wendelin Böhmer; Steffen Grünewälder; Hannes Nickisch; Klaus Obermayer (Mach. Learn., 2012)

Smooth Operators. - Steffen Grünewälder; Arthur Gretton; John Shawe-Taylor (ICML (3), 2013)

Construction of approximation spaces for reinforcement learning. - Wendelin Böhmer; Steffen Grünewälder; Yun Shen; Marek Musial; Klaus Obermayer (J. Mach. Learn. Res., 2013)

Optimistic Planning for the Stochastic Knapsack Problem. - Ciara Pike-Burke; Steffen Grünewälder (AISTATS, 2017)

Bandits with Delayed Anonymous Feedback. - Ciara Pike-Burke; Shipra Agrawal 001; Csaba Szepesvári; Steffen Grünewälder (CoRR, 2017)

Compact Convex Projections. - Steffen Grünewälder (J. Mach. Learn. Res., 2017)

Plug-in Estimators for Conditional Expectations and Probabilities. - Steffen Grünewälder (AISTATS, 2018)

Bandits with Delayed, Aggregated Anonymous Feedback. - Ciara Pike-Burke; Shipra Agrawal 001; Csaba Szepesvári; Steffen Grünewälder (ICML, 2018)

Recovering Bandits. - Ciara Pike-Burke; Steffen Grünewälder (NeurIPS, 2019)

Approximations of the Restless Bandit Problem. - Steffen Grünewälder; Azadeh Khaleghi (J. Mach. Learn. Res., 2019)

Ivanov-Regularised Least-Squares Estimators over Large RKHSs and Their Interpolation Spaces. - Stephen Page; Steffen Grünewälder (J. Mach. Learn. Res., 2019)

Oblivious Data for Fairness with Kernels. - Steffen Grünewälder; Azadeh Khaleghi (J. Mach. Learn. Res., 2021)

Compressed Empirical Measures (in finite dimensions). - Steffen Grünewälder (CoRR, 2022)

Support Collapse of Deep Gaussian Processes with Polynomial Kernels for a Wide Regime of Hyperparameters. - Daryna Chernobrovkina; Steffen Grünewälder (CoRR, 2025)