Seminari - Dipartimento Computer Science validi dal 24.05.2024 al 24.05.2025. https://www.di.univr.it/?ent=seminario&lang=en&rss=0 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6323 Relatore: Francesco Leofante; Provenienza: Centre for Explainable AI at Imperial College London (UK); Data inizio: 2024-06-18; Ora inizio: 14.30; Note orario: Sala Verde; Referente interno: Alessandro Farinelli; Riassunto: Abstract : Counterfactual explanations (CXs) are advocated as being ideally suited to providing algorithmic recourse for subjects affected by the predictions of machine learning models. While CXs can be beneficial to affected individuals, recent work has exposed severe issues related to the robustness of state-of-the-art methods for obtaining CXs. Since a lack of robustness may compromise the fairness of CXs, techniques to mitigate this risk are in order. In this talk we will begin by introducing the problem of (lack of) robustness, discuss its implications on fairness and present a recent solution we developed to compute robust (and fair) CXs. Bio : Francesco is an Imperial College Research Fellow affiliated with the Centre for Explainable Artificial Intelligence at Imperial College London. His research focuses on safe and explainable AI, with special emphasis on counterfactual explanations and algorithmic recourse. Since 2022, he leads the project ldquo;ConTrust: Robust Contrastive Explanations for Deep Neural Networksrdquo;, a four-year effort devoted to the formal study of robustness issues arising in XAI. More details about Francesco and his research can be found at https://fraleo.github.io/ . Tue, 18 Jun 2024 14:30:00 +0200 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6323 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6319 Relatore: Mario Onorato; Provenienza: Head of Enterprise Risk Management, IBM Italia S.p.A.; Data inizio: 2024-06-10; Ora inizio: 11.30; Note orario: Sala Verde (presenza e da remoto); Referente interno: Alessandra Di Pierro; Riassunto: ABSTRACT: quot;High Performance Computingrdquo; (HPC) already provides cutting-edge solutions in terms of processing power today. In many business areas, the achieved results are already considered optimal. Over time, the deployment of this state-of-the-art technology in other business areas is already included in industrial and strategic plans of FS Institutions to maintain, if not improve, one#39;s ability to compete and retain market leadership. The applications of quantum computing that can lead to significant improvements and cannot be achieved by HPC computing will essentially be categorized into four areas: stochastic simulation, optimization, machine learning, and security. These four areas lead to hundreds of potentially disruptive business use cases in some core business areas. Identifying these disruptive core business use cases is one of the key success factors. It#39;s not just about understanding the technology, but also about intercepting the processes where the application of quantum computing will be a game changer once it becomes available, so that we can be prepared. In the target architecture, quantum computers will not completely replace traditional computers; instead, they will work synergistically with them. The #39;quantum advantage#39; is indeed the moment when a quantum machine outperforms the most powerful classical computer in a practical and relevant task. The #39;Quantum Advantage Enterprise#39; represents a potentially disruptive advantage in CORE areas, which could necessitate significant business restructuring for those without it. BIO: Mario has more than 30 years of risk modeling and governance experience in the banking and insurance sectors. He is Associate Partner and responsible for the IBM Consulting Offering of Risk, Compliance, Quantum and Security Solution. Mario is also the Enterprise Risk Management Practice Leader of Promontory a Division of IBM Consulting. Prior to joining Promontory in 2017, Mario was the Group Head of Financial and Credit Risk at Generali, where he regularly acted as liaison to regulators in countries where the Group was active on all financial, credit and liquidity risk regulatory matters, while at the same time ensuring appropriate communication to the Board and Senior Management of the business implications of modelling choices. Prior to that he was the Practice Leader of Balance Sheet & Capital Management Solutions at Algorithmics, an IBM Company. In this role, Mario was responsible for the development of Asset and Liability Management, Liquidity Risk, Market Risk, Credit Risk, Economic and Regulatory Capital solutions for commercial and investment banks worldwide. During his career Mario has also held a number of academic positions in The Netherlands, Italy and in the UK where he was Honorary Professor of the Risk Management Practice at Bayes Business School, City University, London. He is author of several books and research papers on various risk management topics in the areas of credit, market, liquidity risk and risk adjusted performance. Mario holds a PhD in Finance from Bayes Business S chool, UK. Zoom Meeting ID: 953 4208 1925 Passcode: 624304. Mon, 10 Jun 2024 11:30:00 +0200 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6319 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6293 Relatore: Dott. Maurizio Tavelli; Provenienza: Università di Bolzano; Data inizio: 2024-06-05; Ora inizio: 11.00; Note orario: Sala Verde; Referente interno: Elena Gaburro; Riassunto: Abstract: In this talk we present a class of semi-implicit methods on unstructured staggered meshes in 2D and 3D. The numerical solution is computed using a DG approach, but on staggered meshes. We will consider several PDE systems such as the incompressible/compressible Navier Stokes and linear elasticity. The proper implicit discretization of the terms associated to the fast scale, i.e. the pressure, allows to obtain a weak CFL stability restriction. The resulting maximum time step is then limited by the local velocity and not by the celerity, allowing the algorithm to perform well in the low Mach number regime. The resulting linear system is symmetric and at least semi-positive definite for most of the applications, so an efficient matrix-free implementation of the conjugate gradient method can be used for the solution of the linear part. Thanks to the chosen discretization, the stencil results very compact and involves only the direct neighbors. Besides, high order in time can also be obtained using a genuine space-time DG or a simple IMEX approach. Several extensions to ph-adaptivity and GPU computing will be discussed, as well as a possible application to networks of compliant pipes, useful to simulate physiological flows. . Wed, 5 Jun 2024 11:00:00 +0200 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6293 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6318 Relatore: Prof. Scott Hazelhurst; Provenienza: University of the Witwatersrand - Johannesburg - South Africa.; Data inizio: 2024-06-04; Ora inizio: 14.30; Referente interno: Zsuzsanna Liptak; Riassunto: Da definire. Tue, 4 Jun 2024 14:30:00 +0200 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6318 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6279 Relatore: Riccardo Volpi; Provenienza: Naver Labs Europe - France; Data inizio: 2024-06-04; Ora inizio: 13.45; Note orario: Aula C (solo presenza); Referente interno: Vittorio Murino; Riassunto: Abstract: In recent years, the vision-and-language paradigm has revolutionized the way we learn and rely on computer vision models. A major drawback of learning visual representation has always been the lack of data: when coupling our vision model with large, pre-trained language models we can partially mitigate these issues by building on large amounts of previously learned information. In this talk, we will discuss how using language can i) broaden the comfort zone of model vision models for tasks such as object detection and classification and ii) improve their interpretability. We will go through the basis of the vision-and-language paradigm, highlight some of its inherent limitations and discuss some innovative solutions, for example to make CLIP-like models robust to arbitrary vocabularies selected by the user. Tue, 4 Jun 2024 13:45:00 +0200 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6279 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6322 Relatore: A. Daducci; Provenienza: Universita' di Verona; Data inizio: 2024-06-04; Ora inizio: 10.30; Referente interno: Giacomo Albi; Riassunto: Diffusion-weighted magnetic resonance imaging (DW-MRI) is an invaluable tool in neuroscience, as it allows researchers to study non-invasively the neuronal architecture of the brain using the so-called quot;tractographyquot; algorithms. Despite this unique and compelling ability, recent studies have showed that the reconstructions are unfortunately neither quantitative nor anatomical accurate, de facto limiting their application in clinical studies. This course will introduce the basics of DW-MRI and the main tractography algorithms, followed by a critical analysis of their strengths and, most importantly, their limitations. I will then present a novel field of research named quot;microstructure informed tractographyquot; which aims to drastically improve the quality of the reconstructions and, in particular, I will focus on the Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) and will show some possibilities that this framework offers. Schedule: 4/06. Tue 10:30-12:30. Aula G 6/06. Thu 10:30-12:30. Aula G 11/06 Tue 10:30-13:30. Aula G . Tue, 4 Jun 2024 10:30:00 +0200 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6322 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6315 Relatore: Matteo Sfragara; Viktorya Vardanyan; Provenienza: Stockholm University; Università di Trento; Data inizio: 2024-06-03; Ora inizio: 16.30; Note orario: Sala Verde; Referente interno: Giacomo Canevari; Riassunto: Matteo Sfragara (Stockholm University) Queue-based random-access protocols for wireless networks In this talk, we discuss mathematical models that address fundamental challenges in wireless networks. We first introduce Carrier-Sense Multiple-Access (CSMA) protocols, distributed algorithms that involve randomness to prevent the devices to transmit simultaneously and hencetheir signals to interfere with each other. Thesemodels can be viewed as interacting particle systems on graphs, where the interference is captured by a hard-core interaction model.Wedescribe how they exhibit metastability and how understanding metastability properties iscrucial to design mechanisms to counter starvation effectsand improve the performance of the network. In particular, we focus on random-access models where the transmission rates depend on the queues at the nodes. We discuss three different network topologies: we start with complete bipartite networks, we then generalize our results to arbitrary bipartite networks, and finally we explore dynamic bipartite networks in which the interference graph changes over time, which allows us to capture some effects of user mobility.This talk is based on three joint workswithFrank den Hollander (Leiden University), Sem Borst (Eindhoven University of Technology) and Francesca R.Nardi (University of Florence). Viktorya Vardanyan (Universitagrave; di Trento) Mean field games with terminal state constraints We investigate a mean field game (MFG) characterized by state dynamics modeled through stochastic differential equations influenced by both idiosyncratic and common noise. These dynamics are subject to the constraint that the terminal state variable resides withina nonempty convex closed set. Additionally, the mean field interaction affects both the state and control in the dynamics and the costs. Motivated by the work of Ji and Zhou [Comm. Inf. Syst. 6(4), 2006], we introduce an auxiliary MFG problem and establish the stochastic maximum principle (SMP) for an auxiliary optimization problem with fixed flows. Throughthe formulation of a suitable forward-backward stochastic differential equation (FBSDE) of conditional McKean-Vlasov type, we demonstrate the existence of its solution and confirm itsrole as a MFG equilibrium. Furthermore, we apply these findings within a financial context. The talk is based on a joint work with Luca Di Persio (University of Verona) and Luciano Campi(University of Milan). Further information at this link. Mon, 3 Jun 2024 16:30:00 +0200 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6315 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6300 Relatore: Dr. Ingo Blechschmidt; Provenienza: Universität Augsburg (Germania); Data inizio: 2024-06-03; Ora inizio: 14.00; Note orario: - data e ora da confermare - 6 ore in totale; Referente interno: Peter Michael Schuster; Riassunto: Construction, realisability and double negation. Mon, 3 Jun 2024 14:00:00 +0200 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6300 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6266 Relatore: Thiago D. Simão; Provenienza: Eindhoven University of Technology; Data inizio: 2024-05-29; Ora inizio: 12.30; Referente interno: Alberto Castellini; Riassunto: Safety is a crucial concern when deploying reinforcement learning (RL) algorithms in real-world scenarios. In this two-part lecture series, we delve into safety considerations from two perspectives: ensuring reasonable performance and adhering to predefined constraints. - PART 1. In the first segment, we investigate the offline setting where the RL agent solely accesses a fixed dataset of prior trajectories, devoid of direct interaction with the environment. Given the availability of the behavior policy responsible for data collection, the primary challenge is crafting a policy that outperforms such behavior policy. We study algorithms that leverage the behavior policy to compute an improved policy with high probability and discuss how to improve their sample efficiency. - PART 2. Transitioning to the latter segment, we confront the limitations inherent in specifying the behavior expected from an agent solely via a reward function. We introduce a model that mitigates this issue using constraints, and we discuss how to compute the corresponding optimal policy when the problem is known. Finally, we study algorithms that can efficiently explore the environment and eventually converge to an optimal policy when the model is unknown. Schedule: May 29, 12.30-14.30 (Room B) May 29, 15.30-17.30 (Room 1.02) May 31, 15.30-17.30 (Room 1.02) June 5, 12.30-14.30 (Room B) June 5, 15.30-17.30 (Room 1.02 June 7, 15.30-17.30 (Room 1.02) The minicourse is related to the quot;Reinforcement Learningquot; course (Master in Artificial Intelligence). Wed, 29 May 2024 12:30:00 +0200 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6266 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6302 Relatore: Davide Pastorello; Provenienza: Alma Mater Studiorum, Università di Bologna, Department of Mathematics; Data inizio: 2024-05-27; Ora inizio: 14.00; Note orario: Aula I; Referente interno: Alessandra Di Pierro; Riassunto: Abstract: The application of quantum computing to machine learning offers some interesting solutions characterized by a quantum advantage with respect to the classical counterparts in terms of time and space complexity, expressive power, generalization capability, at least on a theoretical level. In this lecture, we give an overview on some quantum machine learning algorithms including distance-based classifiers and different kinds of quantum neural networks. In particular, we focus on the most promising approaches relating to existing and near-term quantum machines. Short cv: Davide Pastorello received the M.Sc. degrees in Physics and the Ph.D. degree in Mathematics from the University of Trento in 2011 and 2014, respectively. From 2015 to 2019 he was postdoc researcher at Deptartment of Mathematics, University of Trento, also with a 2-year grant from Fondazione Caritro as P.I. of a project on quantum computing. From 2020 to 2023 he was Assistant Professor at the Department of Information Engineering and Computer Science (DISI), University of Trento. Since 2023, he is Assistant Professor at Deptartment of Mathematics, University of Bologna. His main research interests are: mathematical foundations of quantum mechanics, mathematical methods in quantum computing and quantum machine learning. . Mon, 27 May 2024 14:00:00 +0200 https://www.di.univr.it/?ent=seminario&lang=en&rss=0&id=6302