Dear all,
We are happy to announce that on Monday, September 25th, at 15h00 in Aula Dal Passo of Tor Vergata Math Department, RoMaDS (https://www.mat.uniroma2.it/~rds/about.php) will host Marco Carfagnini (University of California San Diego) with the seminar
"Spectral gaps via small deviations”
Abstract: In this talk we will discuss spectral gaps of second order differential operators and their connection to limit laws such as small deviations and Chung’s laws of the iterated logarithm. The main focus is on hypoelliptic diffusions such as the Kolmogorov diffusion and horizontal Brownian motions on Carnot groups. If time permits, we will discuss spectral properties and existence of spectral gaps on general Dirichlet metric measure spaces.This talk is based on joint works with Maria (Masha) Gordina and Alexander (Sasha) Teplyaev.
We encourage in-person partecipation. Should you be unable to come, here is the link to the Teams streaming:
https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwPU-iIPEmqet8NCg1%40thread.tacv2/1695123226259?context={%22Tid%22%3a%2224c5be2a-d764-40c5-9975-82d08ae47d0e%22%2c%22Oid%22%3a%22650fc4a8-4cec-4bd2-87bc-90d134074fe6%22} .
The seminar is part of the Excellence Project MatMod@TOV.
Dear all,
We are happy to announce that on Thursday, December 7th, at 15h00 in Aula Dal Passo of Tor Vergata Math Department, RoMaDS (https://www.mat.uniroma2.it/~rds/about.php) will host Matteo Quattropani (La Sapienza) with the seminar
"Mixing of the Averaging process on graphs” Abstract: The Averaging process (a.k.a. repeated averages) is a mass redistribution model over the vertex set of a graph. Given a graph G, the process starts with a non-negative mass associated to each vertex. The edges of G are equipped with Poissonian clocks: when an edge rings, the masses at the two extremes of the edge are equally redistributed on these two vertices. Clearly, as time grows to infinity, the state of the system will converge (in some sense) to a flat configuration in which all the vertices have the same mass. This very simple process has been introduced to the probabilistic community by Aldous and Lanoue in 2012. However, up to few years ago, there was no graph for which sharp quantitative results on the time needed to reach equilibrium were available. Indeed, the analysis of this process requires different tools compared to the classical Markov chain framework, and even in the case of seemingly straightforward geometries—such as the complete graph or the 1-d torus—it can be handled only by means of non trivial probabilistic and functional analytic techniques. During the talk, I’ll try to give a broad overview of the problem and of its difficulties, and I'll present the few examples that have been completely settled. Based on joint work with P. Caputo (Roma Tre) and F. Sau (Università di Trieste)
We encourage in-person partecipation. Should you be unable to come, here is the link to the Teams streaming: https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP...
The seminar is part of the Excellence Project MatMod@TOV.
Dear all,
On Thursday, January 18th, at 15h45 (change of usual time!) in Aula Dal Passo of Rome Tor Vergata Math Department, RoMaDS (https://www.mat.uniroma2.it/~rds/about.php) will host Andrea Clementi (Tor Vergata) with the seminar
"The Minority Dynamics and the Power of Synchronicity”
Abstract: We study the minority-opinion dynamics over a fully-connected network of n nodes with binary opinions. Upon activation, a node receives a sample of opinions from a limited number of neighbors chosen uniformly at random. Each activated node then adopts the opinion that is least common within the received sample. Unlike all other known consensus dynamics, we prove that this elementary protocol behaves in dramatically different ways, depending on whether activations occur sequentially or in parallel. Specifically, we show that its expected consensus time is exponential in n under asynchronous models, such as asynchronous GOSSIP. On the other hand, despite its chaotic nature, we show that it converges within O(log^2 n) rounds with high probability under synchronous models, such as synchronous GOSSIP. Finally, our results shed light on the bit-dissemination problem, that was previously introduced to model the spread of information in biological scenarios. Specifically, our analysis implies that the minority-opinion dynamics is the first stateless solution to this problem, in the parallel passive-communication setting, achieving convergence within a polylogarithmic number of rounds. This, together with a known lower bound for sequential stateless dynamics, implies a parallel-vs-sequential gap for this problem that is nearly quadratic in the number n of nodes. This is in contrast to all known results for problems in this area, which exhibit a linear gap between the parallel and the sequential setting. Joint work with: L. Becchetti, F. Pasquale, L. Trevisan, R. Vacus, and I. Ziccardi The results will be presented at: ACM-SIAM Symposium on Discrete Algorithms (SODA24) Full version of the paper is available here: https://arxiv.org/abs/2310.13558
We encourage in-person partecipation. Should you be unable to come, here is the link to the Teams streaming:
https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwPU-iIPEmqet8NCg1%40thread.tacv2/1704988914646?context={%22Tid%22%3a%2224c5be2a-d764-40c5-9975-82d08ae47d0e%22%2c%22Oid%22%3a%22650fc4a8-4cec-4bd2-87bc-90d134074fe6%22} https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP...
The seminar is part of the Excellence Project MatMod@TOV.
Dear all,
On Thursday, February 8th, at 15h00 in Aula Dal Passo of Rome Tor Vergata Math Department, RoMaDS (https://www.mat.uniroma2.it/~rds/about.php) will host Lorenzo Dello Schiavo (IST Austria) with the seminar
"Conformally invariant random fields, quantum Liouville measures, and random Paneitz operators on Riemannian manifolds of even dimension”
Abstract: On large classes of closed even-dimensional Riemannian manifolds M, we construct and study the Copolyharmonic Gaussian Field, i.e. a conformally invariant log-correlated Gaussian field of distributions on M. This random field is defined as the unique centered Gaussian field with covariance kernel given as the resolvent kernel of Graham—Jenne—Mason—Sparling (GJMS) operators of maximal order. The corresponding Gaussian Multiplicative Chaos is a generalization to the 2m-dimensional case of the celebrated Liouville Quantum Gravity measure in dimension two. We study the associated Liouville Brownian motion and random GJMS operator, the higher-dimensional analogues of the 2d Liouville Brownian Motion and of the random Laplacian. Finally, we study the Polyakov–Liouville measure on the space of distributions on M induced by the copolyharmonic Gaussian field, providing explicit conditions for its finiteness and computing the conformal anomaly. ( arXiv:2105.13925 https://arxiv.org/abs/2105.13925, joint work with Ronan Herry, Eva Kopfer, Karl-Theodor Sturm)
We encourage in-person partecipation. Should you be unable to come, here is the link to the Teams streaming:
https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP...
https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... The seminar is part of the Excellence Project MatMod@TOV.
Dear all,
For this semester the seminars at RoMaDS (https://www.mat.uniroma2.it/~rds/about.php) will take place on Wednesdays from 14:30 until 15:30 in Aula Dal Passo, unless specified otherwise.
We start on Wednesday, March 6th with Jodi Dianetti (Bielefeld University) with the seminar "Strong solutions to submodular mean field games with common noise and related McKean-Vlasov FBSDEs"
Abstract:
We study multidimensional mean field games with common noise and the related system of McKean-Vlasov forward-backward stochastic differential equations deriving from the stochastic maximum principle. We first propose some structural conditions which are related to the submodularity of the underlying mean field game and are a sort of opposite version of the well known Lasry-Lions monotonicity. By reformulating the representative player minimization problem via the stochastic maximum principle, the submodularity conditions allows to prove comparison principles for the forward-backward system, which correspond to the monotonicity of the best reply map. Building on this property, existence of strong solutions is shown via Tarski’s fixed point theorem, both for the mean field game and for the related McKean-Vlasov forward backward system. In both cases, the set of solutions enjoys a lattice structure with minimal and maximal solutions which can be approximated by the simple iteration of the best response map and by the Fictitious Play algorithm.
We encourage in-person partecipation. Should you be unable to come, here is the link to the Teams streaming:
https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... The seminar is part of the Excellence Project MatMod@TOV. https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP...
Dear all,
We are happy to announce that on Friday, March 15th, at 14h00 in Aula D’Antoni of Tor Vergata Math Department (different time and place than usual!), RoMaDS (https://www.mat.uniroma2.it/~rds/about.php) will host Maurizio Parton (Università di Chieti-Pescara).
Note: The seminar will be accessible also to non-experts and it is thought as an informal presentation + Q&A session about all you wanted to know about reinforcement learning and never dared to ask. Please spread the word with those (including students) who might be interested in the topic.
"The simple and magical interplay of deep learning and reinforcement learning”
Abstract: Reinforcement learning and deep learning are two completely different machine learning frameworks. Yet, they combine in a magical way to form deep reinforcement learning, which is the main driver of several of the recent breakthroughs in artificial intelligence, like game-playing AI, robotics, self-driving cars, advanced recommendation systems, chatbots, and beyond. I will give a concise overview of reinforcement learning and its magical interplay with deep learning. Questions are more than welcome, and for this reason I will keep the presentation under 30 minutes, maximizing time for discussion.
We encourage in-person partecipation. Should you be unable to come, here is the link to the Teams streaming:
https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... https://teams.microsoft.com/l/meetup-join/19:rfsL73KX-fw86y1YnXq2nk5VnZFwPU-iIPEmqet8NCg1@thread.tacv2/1709902389222?context=%7B%22Tid%22:%2224c5be2a-d764-40c5-9975-82d08ae47d0e%22,%22Oid%22:%22650fc4a8-4cec-4bd2-87bc-90d134074fe6%22%7D
The seminar is part of the Excellence Project MatMod@TOV.
https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP... https://teams.microsoft.com/l/meetup-join/19%3arfsL73KX-fw86y1YnXq2nk5VnZFwP...
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Michele Salvi