Publications by Giovanni Piccioli

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This thesis focuses on the development of advanced algorithmic techniques, primarily Markov Chain Monte Carlo (MCMC) methods, and message passing algorithms, to tackle high-dimensional optimization and inference problems. The algorithms used have a probabi ...

The sum-of-squares (SOS) approximation method is a technique used in optimization problems to derive lower bounds on the optimal value of an objective function. By representing the objective function as a sum of squares in a feature space, the SOS method t ...

In this paper we study a fully connected planted spin glass named the planted XY model. Motivation for studying this system comes both from the spin glass field and the one of statistical inference where it models the angular synchronization problem. We de ...

Path optimization is a fundamental concern across various real-world scenarios, ranging from traffic congestion issues to efficient data routing over the Internet. The traffic assignment problem (TAP) is a classic continuous optimization problem in this fi ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

The problem of aligning Erdos-Renyi random graphs is a noisy, average-case version of the graph isomorphism problem, in which a pair of correlated random graphs is observed through a random permutation of their vertices. We study a polynomial time message- ...

Giovanni Piccioli

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Advances in Algorithms for Sampling, Optimization and Inference in Disordered Systems

Theory and applications of the sum-of-squares technique

Planted XY model: Thermodynamics and inference

Integer traffic assignment problem: Algorithms and insights on random graphs

Gibbs sampling the posterior of neural networks

Aligning random graphs with a sub-tree similarity message-passing algorithm