Thu, 07 Mar 2019

13:00 - 14:00
L4

Optimal execution with rough path signatures

Imanol Perez
((Oxford University))
Further Information


 

Abstract

We consider a well-studied optimal execution problem under little assumptions on the underlying midprice process. We do so by using signatures from rough path theory, that allows converting the original problem into a more computationally tractable problem. We include a few numerical experiments where we show that our methodology is able to retrieve the theoretical optimal execution speed for several problems studied in the literature, as well as some cases not included in the literatture. We also study some estensions of our framework to other settings.
 

Thu, 14 Feb 2019

13:00 - 14:00
L4

Pathwise functional portfolio generation and optimal transport

Micheal Monoyios
((Oxford University))
Further Information

We make precise a remarkable connection, first observed by Pal and Wong (2016) and further analysed in the doctoral thesis of Vervuurt (2016), between functionally generated investments and optimal transport, in a model-free discrete-time financial market. A functionally generated portfolio (FGP) computes the investment in each stock through the prism of the super-differential of the logarithm of a concave function (the generating function of the FGP) of the market weight vector. Such portfolios have been shown to outperform the market under suitable conditions. Here, in our pathwise discrete-time scenario, we equate the convex-analytic cyclical monotonicity property characterising super-differentials, with a $c$-cyclical monotonicity property of the unique Monge solution of an appropriately constructed optimal transport problem with cost function $c$, which transfers the market portfolio distribution to the FGP distribution. Using the super-differential characterisation of functional investments, we construct optimal transport problems for both traditional (multiplicative) FGPs, and an ``additive'' modification introduced by Karatzas and Ruf (2017), featuring the same cost function in both cases, which characterise the functional investment. In the multiplicative case, the construction differs from Pal and Wong (2016) and Vervuurt (2016), who used a ``multiplicative'' cyclical monotonicity property, as opposed to the classical cyclical monotonicity property used here.
  
We establish uniqueness of the solution to the relevant optimal transport problem, elevating the connection observed by Pal and Wong (2016) to an exact equivalence between optimal transport and functional generation. We explore ramifications, including pathwise discrete-time master equations for the evolution of the relative wealth of the investment when using the market portfolio as numeraire. We take the pathwise continuous time limit, assuming continuous paths which admit well-defined quadratic variation, to establish model-free continuous-time master equations for both types of functionally generated investment, providing an alternative derivation to the recent proof of Schied et al (2018) of the master equation for multiplicative FGPs, as well as an extension to the case of additive functionally generated trading strategies.

Fri, 01 Mar 2019
16:00
L1

Maths meets Computer Vision

Further Information

Speaker 1: Pawan Kumar
Title: Neural Network Verification
Abstract: In recent years, deep neural networks have started to find their way into safety critical application domains such as autonomous cars and personalised medicine. As the risk of an error in such applications is very high, a key step in the deployment of neural networks is their formal verification: proving that a network satisfies a desirable property, or providing a counter-example to show that it does not. In this talk, I will formulate neural network verification as an optimization problem, briefly present the existing branch-and-bound style algorithms to compute a globally optimal solution, and highlight the outstanding mathematical challenges that limit the size of problems we can currently solve.

Speaker 2: Samuel Albanie
Title: The Design of Deep Neural Network Architectures: Exploration in a High-Dimensional Search Space
Abstract: Deep Neural Networks now represent the dominant family of function approximators for tackling machine perception tasks. In this talk, I will discuss the challenges of working with the high-dimensional design space of these networks. I will describe several competing approaches that seek to fully automate the network design process and the open mathematical questions for this problem.

Fri, 25 Jan 2019
16:00
L1

Ethics for mathematicians

Maurice Chiodo
(Cambridge)
Abstract

Teaching ethics to the mathematicians who need it most
For the last 20 years it has become increasingly obvious, and increasingly pressing, that mathematicians should be taught some ethical awareness so as to realise the impact of their work. This extends even to those more highly trained, like graduate students and postdocs. But which mathematicians should we be teaching this to, what should we be teaching them, and how should we do it? In this talk I’ll explore the idea that all mathematicians will, at some stage, be faced with ethical challenges stemming from their work, and yet few are ever told beforehand.
 

Fri, 18 Jan 2019
16:00
L1

North meets South colloquium

Mohit Dalwadi and Thomas Prince
Abstract

Thomas Prince The double life of the number 24.

The number 24 appears in a somewhat surprising result in the study of polyhedra with integer lattice points. In a different setting, the number 24 is the Euler characteristic of a K3 surface: a four (real) dimensional object which plays a central role in algebraic geometry. We will hint at why both instances of 24 are in fact the same, and suggest that integral affine geometry can be used to interpolate between the realm of integral polytopes and the world of complex algebraic geometry.

Mohit Dalwadi A multiscale mathematical model of bacterial nutrient uptake

In mathematical models that include nutrient delivery to bacteria, it is prohibitively expensive to include many small bacterial regions acting as volumetric nutrient sinks. To combat this problem, such models often impose an effective uptake instead. However, it is not immediately clear how to relate properties on the bacterial scale with this effective result. For example, one may intuitively expect the effective uptake to scale with bacterial volume for weak first-order uptake, and with bacterial surface area for strong first-order uptake. I will present a general model for bacterial nutrient uptake, and upscale the system using homogenization theory to determine how the effective uptake depends on the microscale bacterial properties. This will show us when the intuitive volume and surface area scalings are each valid, as well as the correct form of the effective uptake when neither of these scalings is appropriate.
 

Mon, 21 Jan 2019
15:45
L6

Dilation of formal groups, and potential applications

Neil Strickland
(University of Sheffield)
Abstract


I will describe an extremely easy construction with formal group laws, and a 
slightly more subtle argument to show that it can be done in a coordinate-free
way with formal groups.  I will then describe connections with a range of other
phenomena in stable homotopy theory, although I still have many more 
questions than answers about these.  In particular, this should illuminate the
relationship between the Lambda algebra and the Dyer-Lashof algebra at the
prime 2, and possibly suggest better ways to think about related things at 
odd primes.  The Morava K-theory of symmetric groups is well-understood
if we quotient out by transfers, but somewhat mysterious if we do not pass
to that quotient; there are some suggestions that dilation will again be a key
ingredient in resolving this.  The ring $MU_*(\Omega^2S^3)$ is another
object for which we have quite a lot of information but it seems likely that 
important ideas are missing; dilation may also be relevant here.
 

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