Spectral and Optimization Techniques for Robust Recovery, Combinatorial Constructions, and Distributed Algorithms

Discrepancy Minimization via Regularization

Autori: Pesenti, Lucas; Vladu, Adrian
Pubblicato in: https://hal.science/hal-03868123, Numero 9, 2023
Editore: SIAM
DOI: 10.1137/1.9781611977554.ch66

A Ihara-Bass Formula for Non-Boolean Matrices and Strong Refutations of Random CSPs

Autori: Tommaso d’Orsi, Luca Trevisan
Pubblicato in: 2022
Editore: Online
DOI: 10.48550/arxiv.2204.10881

The Minority Dynamics and the Power of Synchronicity

Autori: Becchetti, Luca; Clementi, Andrea; Pasquale, Francesco; Trevisan, Luca; Vacus, Robin; Ziccardi, Isabella
Pubblicato in: Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) ISBN: 9781611977912, Numero 12, 2024
Editore: Society for Industrial and Applied Mathematics
DOI: 10.1137/1.9781611977912.144

Cut Sparsification of the Clique Beyond the Ramanujan Bound: A Separation of Cut Versus Spectral Sparsification

Autori: Chen, Antares; Shi, Jonathan; Trevisan, Luca
Pubblicato in: Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Pagina/e 3693 - 3731
Editore: SIAM
DOI: 10.1137/1.9781611977073.146

Consensus vs Broadcast, with and Without Noise (Extended Abstract)

Autori: Clementi, Andrea ; Gualà, Luciano ; Natale, Emanuele ; Pasquale, Francesco ; Scornavacca, Giacomo ; Trevisan, Luca
Pubblicato in: 11th Innovations in Theoretical Computer Science Conference (ITCS 2020), Numero January 6, 2020, 2020, Pagina/e 42:1--42:13, ISBN 978-3-95977-134-4
Editore: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik
DOI: 10.4230/lipics.itcs.2020.42

Subexponential LPs Approximate Max-Cut

Autori: Samuel B. Hopkins, Tselil Schramm, Luca Trevisan
Pubblicato in: 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS), 2020, Pagina/e 943-953, ISBN 978-1-7281-9621-3
Editore: IEEE
DOI: 10.1109/focs46700.2020.00092

Distributed Self-Stabilizing MIS with Few States and Weak Communication

Autori: George Giakkoupis; Isabella Ziccardi
Pubblicato in: PODC 2023 - ACM Symposium on Principles of Distributed Computing, Numero 10, 2023, ISBN 9798400701214
Editore: Association for Computing Machinery
DOI: 10.1145/3583668.3594581

Limitations of Local Quantum Algorithms on Random MAX-k-XOR and Beyond

Autori: Chi-Ning Chou, Peter J. Love, Juspreet Singh Sandhu, Jonathan Shi
Pubblicato in: 49th International Colloquium on Automata, Languages, and Programming (ICALP), 2022
Editore: Schloss Dagstuhl-Leibniz-Zentrum für Informatik

Deep Networks on Toroids: Removing Symmetries Reveals the Structure ofFlat Regions in the Landscape Geometry

Autori: Fabrizio Pittorino, Antonio Ferraro, Gabriele Perugini, Christoph Feinauer, Carlo Baldassi, Riccardo Zecchina
Pubblicato in: arXiv preprint arXiv:2202.03038, 2022
Editore: arXiv preprint arXiv:2202.03038

Percolation and Epidemic Processes in One-Dimensional Small-World Networks

Autori: Luca Becchetti, Andrea Clementi, Riccardo Denni, Francesco Pasquale, Luca Trevisan, Isabella Ziccardi
Pubblicato in: LATIN 2022: Theoretical Informatics, 2022, Pagina/e pp 476–492
Editore: Latin American Symposium on Theoretical Informatics

Expansion and Flooding in Dynamic Random Networks withNode Churn

Autori: Luca Becchetti, Andrea Clementi, Francesco Pasquale, Luca Trevisan, Isabella Ziccardi
Pubblicato in: 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS), 2021
Editore: Institute of Electrical and Electronics Engineers

New Notions and Constructions of Sparsification for Graphs and Hypergraphs

Autori: Nikhil Bansal, Ola Svensson, Luca Trevisan
Pubblicato in: 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS), 2019, Pagina/e 910-928, ISBN 978-1-7281-4952-3
Editore: IEEE
DOI: 10.1109/focs.2019.00059

New SDP Roundings and Certifiable Approximation for Cubic Optimization

Autori: Hsieh, Jun-Ting; Kothari, Pravesh K.; Pesenti, Lucas; Trevisan, Luca
Pubblicato in: Proceedings of the 2024 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) ISBN: 9781611977912, Numero 9, 2023, ISBN 978-1-61197-791-2
Editore: Society for Industrial and Applied Mathematics
DOI: 10.1137/1.9781611977912.83

Entropic gradient descent algorithms and wide flat minima

Autori: Fabrizio Pittorino; Carlo Lucibello; Christoph Feinauer; Gabriele Perugini; Carlo Baldassi; Elizaveta Demyanenko; Riccardo Zecchina
Pubblicato in: Journal of Statistical Mechanics: Theory and Experiment, Numero 12, 2021, Pagina/e 124015, ISSN 1742-5468
Editore: Institute of Physics
DOI: 10.1088/1742-5468/ac3ae8

Find Your Place: Simple Distributed Algorithms for Community Detection

Autori: Luca Becchetti; Andrea E. F. Clementi; Emanuele Natale; Francesco Pasquale; Luca Trevisan
Pubblicato in: SIAM Journal on Computing, Numero vol 49 n 4, 2020, Pagina/e 821-864, ISSN 0097-5397
Editore: Society for Industrial and Applied Mathematics
DOI: 10.1137/19m1243026

Finding a Bounded-Degree Expander Inside a Dense One

Autori: Luca Becchetti, Andrea Clementi, Emanuele Natale, Francesco Pasquale, Luca Trevisan
Pubblicato in: Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2020, Pagina/e 1320-1336, ISBN 978-1-61197-599-4
Editore: Society for Industrial and Applied Mathematics
DOI: 10.1137/1.9781611975994.80

A New Algorithm for the Robust Semi-random Independent Set Problem

Autori: Theo McKenzie, Hermish Mehta, Luca Trevisan
Pubblicato in: Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 2020, Pagina/e 738-746, ISBN 978-1-61197-599-4
Editore: Society for Industrial and Applied Mathematics
DOI: 10.1137/1.9781611975994.45

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