no code implementations • 5 Mar 2024 • Samuel I. Berchuck, Felipe A. Medeiros, Sayan Mukherjee, Andrea Agazzi
The generalized linear mixed model (GLMM) is a popular statistical approach for handling correlated data, and is used extensively in applications areas where big data is common, including biomedical data settings.
no code implementations • 12 Mar 2023 • Andrea Agazzi, Jianfeng Lu, Sayan Mukherjee
We analyze Elman-type Recurrent Reural Networks (RNNs) and their training in the mean-field regime.
no code implementations • 25 Feb 2021 • Andrea Agazzi, Luisa Andreis, Robert I. A. Patterson, D. R. Michiel Renger
We prove a large-deviation principle (LDP) for the sample paths of jump Markov processes in the small noise limit when, possibly, all the jump rates vanish uniformly, but slowly enough, in a region of the state space.
Probability Molecular Networks 60F10, 60J75, 80A30
no code implementations • 23 Nov 2020 • Mauro Salazar, Dario Paccagnan, Andrea Agazzi, W. P. M. H., Heemels
In this paper we address this question within a repeated game framework and propose a fair incentive mechanism based on artificial currencies that routes selfish agents in a system-optimal fashion, while accounting for their temporal preferences.
no code implementations • ICLR 2021 • Andrea Agazzi, Jianfeng Lu
We study the problem of policy optimization for infinite-horizon discounted Markov Decision Processes with softmax policy and nonlinear function approximation trained with policy gradient algorithms.
no code implementations • 25 Sep 2019 • Andrea Agazzi, Jianfeng Lu
We then give examples of such convergence results in the case of models that diverge if trained with non-lazy TD learning, and in the case of neural networks.
no code implementations • 27 May 2019 • Andrea Agazzi, Jianfeng Lu
We finally give examples of our convergence results in the case of models that diverge if trained with non-lazy TD learning, and in the case of neural networks.
no code implementations • 21 Aug 2014 • Jimmy Dubuisson, Jean-Pierre Eckmann, Andrea Agazzi
We introduce, test and discuss a method for classifying and clustering data modeled as directed graphs.