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Pierre-Cyril Aubin-Frankowski

Post-doctoral researcher in Machine learning

INRIA SIERRA

Biography

Since September 2021, I am a post-doctoral researcher at INRIA SIERRA, working with Alessandro Rudi.

I defended my PhD in July 2021. The manuscript can be found here, the slides are here.

I obtained my PhD in July 2021 from PSL MINES ParisTech (Paris), at the CAS laboratory, where I was advised by Nicolas Petit, working on shape/state constraints in optimal control and nonparametric regression through kernel methods. This revolves around optimization problems in infinite dimensions with infinitely many constraints. I am interested in the near future in studying connections between kernels and dynamical constrained/controlled systems, among which measures, continuity equations (see ICML21) and structured learning problems.

During my PhD, I defined a new positive definite kernel, dubbed the Linear-Quadratic kernel. It is a matrix-valued reproducing kernel, instrumental in Linear-Quadratic optimal control, related to the Gramian of controllability and to the dual Riccati equation (see pdf for an introduction and pdf for the application to state constraints).

I have also developped a general, convex and modular framework to handle shape constraints in RKHSs (see pdf), applied for instance to trajectory reconstruction (see NeuriPS 2020 video for a quick overview in 180s). This is a line of work, originally inspired by non-crossing quantile regression, with Zoltán Szabó. We were particularly motivated by econometrics and finance studies.

I graduated from École polytechnique (X2013) in 2017, then obtained my Master degree (MVA, Mathematics-Vision-Learning) with Highest Honours after an internship with Jean-Philippe Vert (CBIO-Google) on gene network inference (based on single-cell RNA sequencing).

My research and my lyricomania, a passion I share within the association Juvenilia, do not leave me so much time to spare, but I occasionnaly paint.

Contact: pierre-cyril[dot]aubin(at)inria[dot]fr

Interests

  • Kernel Methods
  • Control Theory
  • Optimal Control
  • Nonparametric Estimation
  • Shape constraints

Education

  • PhD in Machine Learning, 2018-2021

    MINES ParisTech

  • MS (M2) in Machine Learning, 2016-2017

    ENS Paris-Saclay

  • MS (M1) in Applied Maths, 2013-2016

    École polytechnique

Recent & Upcoming

I sucessfully defended my PhD in July 2021. The manuscript can be found here, the slides are here.

-I attended CEMRACS 2021, July-August 2021

-Gave a talk at CT 2021, July 2021, video

-Gave a talk at Congrès SMAI 2021, June 2021, slides

-Gave a talk at Learning & Adaptive Systems Group at ETH Zurich (Zurich), February 2021

-Gave a talk at Séminaire du groupe contrôle at SIERRA (INRIA Paris), January 2021, slides

-Gave a talk at Séminaire du CAS at MINES ParisTech (Paris), December 2020, slides, video

-Gave a talk at Séminaire de mathématiques appliquées du CERMICS at ENPC (Marne-la-Vallée), October 2020, slides

-Gave a talk at Séminaire DEVI at ENAC (Toulouse), October 2020, slides

-Presented a poster at SPIGL'20, information geometry summer school (Les Houches), July 2020, poster

-Presented a poster at virtual MLSS 2020 Tübingen, machine learning summer school, July 2020, slides

-Gave a talk at virtual IFAC World Congress, July 2020, slides, video

-Gave a talk at virtual European Control Conference, May 2020, slides, video

Publications

I sucessfully defended my PhD in July 2021. The manuscript can be found here, the slides are here.

Anna Korba, PCAF, Szymon Majewski and Pierre Ablin, Kernel Stein Discrepancy Descent, ICML 2021 (long oral), July 2021, article, arXiv, code/website, pdf

(Under revision) PCAF and Zoltan Szabo, Handling Hard Affine SDP Shape Constraints in RKHSs, January 2021, [article], arXiv, HAL, pdf

PCAF, Linearly-constrained Linear Quadratic Regulator from the viewpoint of kernel methods, SIAM Journal on Control and Optimization, February 2021, (to appear), arXiv, HAL, pdf

PCAF, Interpreting the dual Riccati equation through the LQ reproducing kernel, Comptes Rendus - Mathématique, January 2021, article, arXiv, HAL, pdf

PCAF and Zoltan Szabo, Hard Shape-Constrained Kernel Machines, NeurIPS 2020, December 2020, article, arXiv, HAL, pdf

PCAF, Nicolas Petit and Zoltan Szabo, Kernel Regression for Trajectory Reconstruction of Vehicles under Speed and Inter-Vehicular Distance Constraints, Proceedings IFAC WC 2020, July 2020, article, pdf, slides, video

PCAF and Jean-Philippe Vert, Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference, Bioinformatics, June 2020, article, biorXiv, pdf, supp

PCAF and Nicolas Petit, Data-driven approximation of differential inclusions and application to detection of transportation modes, Proceedings ECC 2020, May 2020, article, pdf, slides, video

PCAF, Lipschitz regularity of the minimum time function of differential inclusions with state constraints, Systems & Control Letters, April 2020, article, pdf

Experience

 
 
 
 
 

Post-doctoral researcher

INRIA SIERRA

Sep 2021 – Present Paris
Kernel methods for constrained optimization problems
 
 
 
 
 

PhD student

MINES ParisTech

Sep 2018 – Jul 2021 Paris

Title: Estimation and Control under Constraints through Kernel Methods.

Abstract: Pointwise state and shape constraints in control theory and nonparametric estimation are difficult to handle as they often involve convex optimization problem with an infinite number of inequality constraints. Satisfaction of these constraints is critical in many applications, such as path-planning or joint quantile regression. Reproducing kernels are propitious for pointwise evaluations. However representer theorems, which ensure the numerical applicability of kernels, cannot be applied for an infinite number of evaluations. Through constructive algebraic and geometric arguments, we prove that an infinite number of affine real-valued constraints over derivatives of the model can be tightened into a finite number of second-order cone constraints when looking for functions in vector-valued reproducing kernel Hilbert spaces. We show that state-constrained Linear-Quadratic (LQ) optimal control is a shape-constrained regression over the Hilbert space of linearly-controlled trajectories defined by an explicit LQ kernel related to the Riccati matrix. The efficiency of the developed approach is illustrated on various examples from both linear control theory and nonparametric estimation. Finally, we provide some results for general differential inclusions in minimal time problems and identification of the graph of the set-valued map. Most of all we bring to light a novel connection between reproducing kernels and optimal control theory, identifying the Hilbertian kernel of linearly controlled trajectories.

 
 
 
 
 

Public consultant/Graduate student

AgroParisTech and ENPC

Sep 2017 – Sep 2018 Paris

Hired as top civil servant (Corps des IPEF). Specialized in:

  • Banking and macroeconomics
  • General and Labour law
  • Environmental dialogue

Worked on artificial intelligence tailored to the strategies of the technical and scientific network of the French Ministry of Environment. I handed a report shortly after the Villani mission “For a meaningful Artificial Intelligence”. This report focuses on conceptualizing machine learning approaches and details its possible effects in institutions transforming due to the Digital Revolution.

 
 
 
 
 

Graduate student

École Normale Supérieure Paris-Saclay

Sep 2016 – Sep 2017 Cachan
Diploma obtained with Highest Honours. Specialized in:

  • General Machine Learning
  • Convex optimization
  • Kernel methods
 
 
 
 
 

Graduate student

École polytechnique

Sep 2016 – Sep 2017 Palaiseau
Diploma obtained with Highest Honours. Specialized in:

  • Applied Mathematics
  • Quantum Physics
  • (Neuro)biology