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Seminars, for informal dissemination of research results, exploratory work by research teams, outreach activities, etc., constitute the simplest form of meetings at a Mathematics research centre.
CAMGSD has recorded the calendar of its seminars for a long time, this page serving both as a means of public announcement of forthcoming activities but also as a historic record.
For a full search interface see the Mathematics Department seminar page.
Topological Quantum Field Theory
Bertrand Patureau-Mirand, Université de Bretagne-Sud.
Weaving 4-Dimensional TQFTs with Ribbon Categories.
I will describe some requirements on a non semi-simple ribbon category that ensure its admissible skein modules form the state spaces of a 3+1-dimensional TQFT.
Room P3.10, Mathematics Building
Ana Cannas, ETH Zurich.
Real Toric Lagrangians.
We fix an arbitrary symplectic toric manifold M. Its real toric lagrangians are the lagrangian submanifolds of M whose intersection with each torus orbit is clean and an orbit of the subgroup of elements that square to the identity of the torus (basically that subgroup is $\{ 1 , -1\}^n$). In particular, real toric lagrangians are transverse to the principal torus orbits and retain as much symmetry as possible.
This talk will explain why any two real toric lagrangians in M are related by an equivariant symplectomorphism and, therefore, any real toric lagrangian must be the real locus for a real structure preserving the moment map. This is joint work with Yael Karshon.
Room P3.10, Mathematics Building
Mathematics for Artificial Intelligence
Pedro A. Santos, INESC & Instituto Superior Técnico.
Introduction to Reinforcement Learning and Markov Decision Processes.
I will offer an introductory exploration into the field of Reinforcement Learning (RL) with a focus on Markov Decision Processes (MDPs). The first session provides a foundational understanding of RL, covering key concepts such as agents, environments, rewards, and actions. It explains the RL problem framework and introduces MDPs, exploring their role as the mathematical framework underpinning RL.
The second session delves into core algorithms, including Q-learning and policy gradients. The lecture highlights the connection between MDPs and dynamic programming techniques, emphasizing policy iteration and value iteration. Time allowing, I will finalize with a brief description of some recent research topics and results.
A good introduction to RL is the 2018 book on the subject by Sutton and Barto. We will talk about topics in Chapters 1,2-6 and 13.
A more rigorous introduction to MDPs, including convergence results, can be found in the book by Puterman:
Martin L. Puterman, Markov Decision Processes: Discrete Stochastic Dynamic Programming, Wiley, 2005.
Room P3.10, Mathematics Building
Mathematics for Artificial Intelligence
Pedro A. Santos, INESC & Instituto Superior Técnico.
Introduction to Reinforcement Learning and Markov Decision Processes.
I will offer an introductory exploration into the field of Reinforcement Learning (RL) with a focus on Markov Decision Processes (MDPs). The first session provides a foundational understanding of RL, covering key concepts such as agents, environments, rewards, and actions. It explains the RL problem framework and introduces MDPs, exploring their role as the mathematical framework underpinning RL.
The second session delves into core algorithms, including Q-learning and policy gradients. The lecture highlights the connection between MDPs and dynamic programming techniques, emphasizing policy iteration and value iteration. Time allowing, I will finalize with a brief description of some recent research topics and results.