Search Results for author: Steffen Grünewälder

Found 6 papers, 2 papers with code

Compressed Empirical Measures (in finite dimensions)

no code implementations19 Apr 2022 Steffen Grünewälder

We study approaches for compressing the empirical measure in the context of finite dimensional reproducing kernel Hilbert spaces (RKHSs). In this context, the empirical measure is contained within a natural convex set and can be approximated using convex optimization methods.

Oblivious Data for Fairness with Kernels

1 code implementation7 Feb 2020 Steffen Grünewälder, Azadeh Khaleghi

We derive a closed-form solution for this relaxed optimization problem and complement the result with a study of the dependencies between the newly generated features and the sensitive ones.

Fairness

Recovering Bandits

1 code implementation NeurIPS 2019 Ciara Pike-Burke, Steffen Grünewälder

We study the recovering bandits problem, a variant of the stochastic multi-armed bandit problem where the expected reward of each arm varies according to some unknown function of the time since the arm was last played.

Gaussian Processes

The Goldenshluger-Lepski Method for Constrained Least-Squares Estimators over RKHSs

no code implementations2 Nov 2018 Stephen Page, Steffen Grünewälder

In the RKHS regression context, we choose our non-adaptive estimators to be clipped least-squares estimators constrained to lie in a ball in an RKHS.

Statistics Theory Statistics Theory

Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies

no code implementations NeurIPS 2009 Arno Onken, Steffen Grünewälder, Klaus Obermayer

The linear correlation coefficient is typically used to characterize and analyze dependencies of neural spike counts.

Modeling Short-term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex

no code implementations NeurIPS 2008 Arno Onken, Steffen Grünewälder, Matthias Munk, Klaus Obermayer

Furthermore, copulas place a wide range of dependence structures at the disposal and can be used to analyze higher order interactions.

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