Inefficiency of CFMs: hedging perspective and agent-based simulations
Cohen, S Sabaté-Vidales, M Šiška, D Szpruch, Ł Financial Cryptography and Data Security. FC 2023 International Workshops. FC 2023 303-319 (05 Dec 2023)
Convergence of policy gradient methods for finite-horizon stochastic linear-quadratic control problems
Giegrich, M Reisinger, C Zhang, Y SIAM Journal on Control and Optimization volume 62 issue 2 1060-1092 (22 Mar 2024)
Tue, 06 Feb 2024

16:00 - 17:00
L6

Non-constant ground configurations in the disordered ferromagnet and minimal cuts in a random environment.

Michal Bassan
(University of Oxford )
Abstract
The disordered ferromagnet is a disordered version of the ferromagnetic Ising model in which the coupling constants are quenched random, chosen independently from a distribution on the non-negative reals. A ground configuration is an infinite-volume configuration whose energy cannot be reduced by finite modifications. It is a long-standing challenge to ascertain whether the disordered ferromagnet on the Z^D lattice admits non-constant ground configurations. When D=2, the problem is equivalent to the existence of bigeodesics in first-passage percolation, so a negative answer is expected. We provide a positive answer in dimensions D>=4, when the distribution of the coupling constants is sufficiently concentrated.

 
The talk will discuss the problem and its background, and present ideas from the proof. Based on joint work of with Shoni Gilboa and Ron Peled.
Tue, 23 Jan 2024

16:00 - 17:00
L6

Combinatorial moment sequences

Natasha Blitvic
(Queen Mary University of London)
Abstract

We will look at a number of interesting examples — some proven, others merely conjectured — of Hamburger moment sequences in combinatorics. We will consider ways in which this positivity may be expected: for instance, in different types of combinatorial statistics on perfect matchings that encode moments of noncommutative analogues of the classical Central Limit Theorem. We will also consider situations in which this positivity may be surprising, and where proving it would open up new approaches to a class of very hard open problems in combinatorics.

An automated method for tendon image segmentation on ultrasound using grey-level co-occurrence matrix features and hidden Gaussian Markov random fields
Scott, I Connell, D Moulton, D Waters, S Namburete, A Arnab, A Malliaras, P Computers in Biology and Medicine volume 169 (20 Dec 2023)
Thu, 15 Feb 2024
14:00

Algorithmic Insurance

Agni Orfanoudaki
(Oxford University Saïd Business School)
Abstract

As machine learning algorithms get integrated into the decision-making process of companies and organizations, insurance products are being developed to protect their providers from liability risk. Algorithmic liability differs from human liability since it is based on data-driven models compared to multiple heterogeneous decision-makers and its performance is known a priori for a given set of data. Traditional actuarial tools for human liability do not consider these properties, primarily focusing on the distribution of historical claims. We propose, for the first time, a quantitative framework to estimate the risk exposure of insurance contracts for machine-driven liability, introducing the concept of algorithmic insurance. Our work provides ML model developers and insurance providers with a comprehensive risk evaluation approach for this new class of products. Thus, we set the foundations of a niche area of research at the intersection of the literature in operations, risk management, and actuarial science. Specifically, we present an optimization formulation to estimate the risk exposure of a binary classification model given a pre-defined range of premiums. Our approach outlines how properties of the model, such as discrimination performance, interpretability, and generalizability, can influence the insurance contract evaluation. To showcase a practical implementation of the proposed framework, we present a case study of medical malpractice in the context of breast cancer detection. Our analysis focuses on measuring the effect of the model parameters on the expected financial loss and identifying the aspects of algorithmic performance that predominantly affect the risk of the contract.

Paper Reference: Bertsimas, D. and Orfanoudaki, A., 2021. Pricing algorithmic insurance. arXiv preprint arXiv:2106.00839.

Paper link: https://arxiv.org/pdf/2106.00839.pdf

Smooth numbers and the Dickman ρ function
Gorodetsky, O Journal d'Analyse Mathematique volume 151 issue 1 139-169 (22 Dec 2023)
Regularized integrals and manifolds with log corners
Dupont, C Panzer, E Pym, B (29 Dec 2023)
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