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Found 7 papers, 2 papers with code
Estimating the Mixing Coefficients of Geometrically Ergodic Markov Processes
We propose methods to estimate the individual $\beta$-mixing coefficients of a real-valued geometrically ergodic Markov process from a single sample-path $X_0, X_1, \dots, X_n$.
Compressed Empirical Measures (in finite dimensions)
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. Such an approximation gives under certain conditions rise to a coreset of data points.
Oblivious Data for Fairness with Kernels
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.
Recovering Bandits
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.
The Goldenshluger-Lepski Method for Constrained Least-Squares Estimators over RKHSs
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
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
Furthermore, copulas place a wide range of dependence structures at the disposal and can be used to analyze higher order interactions.
