Arrow Research search

Author name cluster

Marc Castella

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
1 author row

Possible papers

2

TMLR Journal 2026 Journal Article

Kernel Matrix Estimation of a Determinantal Point Process from a Finite Set of Samples: Properties and Algorithms

  • Marc Castella
  • Jean-Christophe Pesquet

Determinantal point processes (DPPs) on finite sets have recently gained popularity because of their ability to promote diversity among selected elements in a given subset. The probability distribution of a DPP is defined by the determinant of a positive semi-definite, real-valued matrix. When estimating the DPP parameter matrix, it is often more convenient to express the maximum likelihood criterion using the framework of L-ensembles. However, the resulting optimization problem is non-convex and N P-hard to solve. In this paper, we establish conditions under which the maximum likelihood criterion has a well-defined optimum for a given finite set of samples. We demonstrate that regularization is generally beneficial for ensuring a proper solution. To solve the resulting optimization problem, we propose a proximal algorithm which minimizes a penalized criterion. Through simulations, we compare our algorithm with previously proposed approaches, illustrating their differing behaviors and providing empirical support for our theoretical findings.

PDF Details

AAAI Conference 2020 Conference Paper

Modeling Electrical Motor Dynamics Using Encoder-Decoder with Recurrent Skip Connection

  • Sagar Verma
  • Nicolas Henwood
  • Marc Castella
  • Francois Malrait
  • Jean-Christophe Pesquet

Electrical motors are the most important source of mechanical energy in the industrial world. Their modeling traditionally relies on a physics-based approach, which aims at taking their complex internal dynamics into account. In this paper, we explore the feasibility of modeling the dynamics of an electrical motor by following a data-driven approach, which uses only its inputs and outputs and does not make any assumption on its internal behaviour. We propose a novel encoder-decoder architecture which benefits from recurrent skip connections. We also propose a novel loss function that takes into account the complexity of electrical motor quantities and helps in avoiding model bias. We show that the proposed architecture can achieve a good learning performance on our high-frequency high-variance datasets. Two datasets are considered: the first one is generated using a simulator based on the physics of an induction motor and the second one is recorded from an industrial electrical motor. We benchmark our solution using variants of traditional neural networks like feedforward, convolutional, and recurrent networks. We evaluate various design choices of our architecture and compare it to the baselines. We show the domain adaptation capability of our model to learn dynamics just from simulated data by testing it on the raw sensor data. We finally show the effect of signal complexity on the proposed method ability to model temporal dynamics.

PDF Details