Tag: news

I’m happy to announce two papers recently accepted for publication in IEEE Transactions on Automatic Control:

• N. Bastianello, L. Madden, R. Carli, E. Dall’Anese. “A Stochastic Operator Framework for Optimization and Learning with Sub-Weibull Errors.” IEEE Trans. Automatic Control [to appear] doi, link, arXiv
• N. Bastianello^*, D. Deplano^*, M. Franceschelli, K. H. Johansson. “Robust Online Learning over Networks.” IEEE Trans. Automatic Control [to appear] [^* equal contribution] arXiv

I’m thankful to all my co-authors.

I’m very happy to announce that three new conference papers have been accepted. For SysDO:

• G. Carnevale, N. Bastianello, R. Carli, G. Notarstefano. “Distributed Newton Optimization with ADMM-Based Consensus.” Symposium on Systems Theory in Data and Optimization 2024

and for CDC’24:

• X. Ren, N. Bastianello, K. H. Johansson, T. Parisini. “Distributed Learning by Local Training ADMM.” 2024 IEEE Conference on Decision and Control (CDC’24)
• N. Bastianello, A. I. Rikos, K. H. Johansson. “Asynchronous Distributed Learning with Quantized Finite-Time Coordination.” 2024 IEEE Conference on Decision and Control (CDC’24)

Thanks to all my co-authors!

I’m happy to announce three new pre-prints:

• U. Casti, N. Bastianello, R. Carli, S. Zampieri. “A Control Theoretical Approach to Online Constrained Optimization.” arXiv
• G. Carnevale, N. Bastianello, G. Notarstefano, R. Carli. “ADMM-Tracking Gradient for Distributed Optimization over Asynchronous and Unreliable Networks.” arXiv
• N. Bastianello^*, D. Deplano^*, M. Franceschelli, K. H. Johansson. “Online Distributed Learning over Random Networks.” [^* equal contribution] arXiv

I’m grateful to all my collaborators on these papers.

I’m happy to report that our paper has been accepted for publication in IEEE TAC:

N. Bastianello, R. Carli, S. Zampieri. “Internal Model-Based Online Optimization.” IEEE Trans. Automatic Control, to appear arXiv