Thu, 21 May 2015

16:00 - 17:00
L4

Machine learning using Hawkes processes and concentration for matrix martingales

Prof Stephane Gaiffas
(CMAP ecole polytechnique)
Abstract

We consider the problem of unveiling the implicit network structure of user interactions in a social network, based only on high-frequency timestamps. Our inference is based on the minimization of the least-squares loss associated with a multivariate Hawkes model, penalized by $\ell_1$ and trace norms. We provide a first theoretical analysis of the generalization error for this problem, that includes sparsity and low-rank inducing priors. This result involves a new data-driven concentration inequality for matrix martingales in continuous time with observable variance, which is a result of independent interest. The analysis is based on a new supermartingale property of the trace exponential, based on tools from stochastic calculus. A consequence of our analysis is the construction of sharply tuned $\ell_1$ and trace-norm penalizations, that leads to a data-driven scaling of the variability of information available for each users. Numerical experiments illustrate the strong improvements achieved by the use of such data-driven penalizations.

Thu, 29 Jan 2015
16:00
L4

Robust evaluation of risks under model uncertainty

Jocelyne Bion-Nadal
(CMAP ecole polytechnique)
Abstract

Dynamic risk measuring has been developed in recent years in the setting of a filtered probability space (Ω,(Ft)0≤t, P). In this setting the risk at time t is given by a Ft-measurable function defined as an ”ess-sup” of conditional expectations. The property of time consistency has been characterized in this setting. Model uncertainty means that instead of a reference probability easure one considers a whole set of probability measures which is furthermore non dominated. For example one needs to deal with this framework to make a robust evaluation of risks for derivative products when one assumes that the underlying model is a diffusion process with uncertain volatility. In this case every possible law for the underlying model is a probability measure solution to the associated martingale problem and the set of possible laws is non dominated.

In the framework of model uncertainty we face two kinds of problems. First the Q-conditional expectation is defined up to a Q-null set and second the sup of a non-countable family of measurable maps is not measurable. To encompass these problems we develop a new approach [1, 2] based on the “Martingale Problem”.

The martingale problem associated with a diffusion process with continuous coefficients has been introduced and studied by Stroock and Varadhan [4]. It has been extended by Stroock to the case of diffusion processes with Levy generators [3]. We study [1] the martingale problem associated with jump diffusions whose coefficients are path dependent. Under certain conditions on the path dependent coefficients, we prove existence and uniqueness of a probability measure solution to the path dependent martingale problem. Making use of the uniqueness of the solution we prove some ”Feller property”. This allows us to construct a time consistent robust evaluation of risks in the framework of model uncertainty [2].

References

[1] Bion-Nadal J., Martingale problem approach to path dependent diffusion processes with jumps, in preparation.

[2] Bion-Nadal J., Robust evaluation of risks from Martingale problem, in preparation.

[3] Strook D., Diffusion processes asociated with Levy generators, Z. Wahrscheinlichkeitstheorie verw. Gebiete 32, pp. 209-244 (1975).

[4] Stroock D. and Varadhan S., Diffusion processes with continuous coefficients, I and II, Communications on Pure and Applied Mathematics, 22, pp 345-400 (1969).

 

Mon, 24 Nov 2014
14:15
Oxford-Man Institute

Learning in high dimension with multiscale invariants

Stephane Mallat
(CMAP ecole polytechnique)
Abstract

   Stéphane Mallat

   Ecole Normale Superieure

Learning functionals in high dimension requires to find sources of regularity and invariants, to reduce dimensionality. Stability to actions of diffeomorphisms is a strong property satisfied by many physical functionals and most signal classification problems. We introduce a scattering operator in a path space, calculated with iterated multiscale wavelet transforms, which is invariant to rigid movements and stable to diffeomorphism actions. It provides a Euclidean embedding of geometric distances and a representation of stationary random processes. Applications will be shown for image classification and to learn quantum chemistry energy functionals.

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