We investigate the question of when a prover can aid a verifier to reliably compute a function faster than if the verifier were to compute the function on its own. Our focus is ...
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
We investigate experimentally the Domatic Partition (DP) problem, the Independent Domatic Partition (IDP) problem and the Idomatic partition problem in Random Geometric Graphs (RG...
Dhia Mahjoub, Angelika Leskovskaya, David W. Matul...
We address the problem of finding sparse wavelet representations of high-dimensional vectors. We present a lower-bounding technique and use it to develop an algorithm for computi...
Estimating the arrival rate function of a non-homogeneous Poisson process based on observed arrival data is a problem naturally arising in many applications. Cubic spline function...
Farid Alizadeh, Jonathan Eckstein, Nilay Noyan, G&...