Thu, 27 Nov 2025

12:00 - 13:00
L3

OCIAM TBC

Karel Devriendt
((Mathematical Institute University of Oxford))
Thu, 06 Nov 2025
17:00
L3

TBA

Vincenzo Mantova
(University of Leeds)
Abstract
TBA
Thu, 04 Dec 2025
17:00
L3

Sharply k-homogeneous actions on Fraïssé structures

Robert Sullivan
(Charles University, Prague)
Abstract
Given an action of a group G on a relational Fraïssé structure M, we call this action *sharply k-homogeneous* if, for each isomorphism f : A -> B of substructures of M of size k, there is exactly one element of G whose action extends f. This generalises the well-known notion of a sharply k-transitive action on a set, and was previously investigated by Cameron, Macpherson and Cherlin. I will discuss recent results with J. de la Nuez González which show that a wide variety of Fraïssé structures admit sharply k-homogeneous actions for k ≤ 3 by finitely generated virtually free groups. Our results also specialise to the case of sets, giving the first examples of finitely presented non-split infinite groups with sharply 2-transitive/sharply 3-transitive actions.
Thu, 27 Nov 2025

12:00 - 12:30
Lecture Room 4

TBA

Malena Sabaté Landman
(Mathematical Institute (University of Oxford))
Abstract

TBA

Thu, 13 Nov 2025

12:00 - 12:30
Lecture Room 4

TBA

Michael Hardman
(University of Oxford Department of Physics)
Abstract

TBA

Thu, 06 Nov 2025

12:00 - 12:30
Lecture Room 4

TBA

Nian Shao
(École Polytechnique Fédérale de Lausanne - EPFL)
Abstract

TBA

Thu, 30 Oct 2025

12:00 - 12:30
Lecture Room 4

TBA

Umberto Zerbinati
(Mathematical Institute (University of Oxford))
Abstract

TBA

Mon, 10 Nov 2025

14:00 - 15:00
Lecture Room 3

Reinforcement learning, transfer learning, and diffusion models

Prof Xin Guo
(Berkeley, USA)
Abstract

Transfer learning is a machine learning technique that leverages knowledge acquired in one domain to improve learning in another, related task. It is a foundational method underlying the success of large language models (LLMs) such as GPT and BERT, which were initially trained for specific tasks. In this talk, I will demonstrate how reinforcement learning (RL), particularly continuous time RL, can benefit from incorporating transfer learning techniques, especially with respect to convergence analysis. I will also show how this analysis naturally yields a simple corollary concerning the stability of score-based generative diffusion models.

Based on joint work with Zijiu Lyu of UC Berkeley.

 

 

Real loci in (log) Calabi–Yau manifolds via Kato–Nakayama spaces of toric degenerations
Argüz, H European Journal of Mathematics volume 7 issue 3 869-930 (23 Sep 2021)
Mirror symmetry for the Tate curve via tropical and log corals
Argüz, H Journal of the London Mathematical Society volume 105 issue 1 343-411 (05 Jan 2022)
Subscribe to