Publications (49)
Aggregation with Exponential Weights is Optimal in Expectation
Mikael Møller Høgsgaard, Patrick Rebeschini, Tobias Wegel
The aggregation with exponential weights (AEW) estimator is not fully understood in the basic setting of model selection aggregation with squared loss. In particular, whether it is…
Linear Convergence for Natural Policy Gradient with Log-linear Policy Parametrization
Carlo Alfano, Patrick Rebeschini
We analyze the convergence rate of the unregularized natural policy gradient algorithm with log-linear policy parametrizations in infinite-horizon discounted Markov decision proces…
Can local particle filters beat the curse of dimensionality?
Patrick Rebeschini, Ramon van Handel
The discovery of particle filtering methods has enabled the use of nonlinear filtering in a wide array of applications. Unfortunately, the approximation error of particle filters t…
Comparison Theorems for Gibbs Measures
Patrick Rebeschini, Ramon van Handel
The Dobrushin comparison theorem is a powerful tool to bound the difference between the marginals of high-dimensional probability distributions in terms of their local specificatio…
Fast Mixing for Discrete Point Processes
Patrick Rebeschini, Amin Karbasi
We investigate the systematic mechanism for designing fast mixing Markov chain Monte Carlo algorithms to sample from discrete point processes under the Dobrushin uniqueness conditi…
Generalization Bounds for Label Noise Stochastic Gradient Descent
Jung Eun Huh, Patrick Rebeschini
We develop generalization error bounds for stochastic gradient descent (SGD) with label noise in non-convex settings under uniform dissipativity and smoothness conditions. Under a…