CNRS@CREATE – ESSEC Workshop
Schedule and abstracts

The workshop will take place in Room 2C7 at ESSEC Business School, 5 Nepal Park, Singapore 139408.
The nearest MRT station is one-north MRT Station (CC23), on the Circle Line. When you reach the
one-north MRT station, take Exit B and walk up Nepal Park to reach the campus. Buses: 74, 91, 92, 95,191, 196, 198 or 200. Link: ESSEC on Google maps: https://maps.app.goo.gl/Kfd5WjMk5aj2inPB8

Schedule (June 11)• 08:45–09:15 Welcome coffee.
• 09:15–09:45 ESSEC and CNRS@CREATE general introduction, by Sonja Prokopec (ESSEC BusinessSchool) and Francisco Chinesta (CNRS).
• 09:45–10:45 Scientific talks:– 09:45–10:15 Archan Misra (Singapore Management University) and Reetika Gupta (ESSEC Business School): Shaping the Consumer-Centricity and Acceptance of Next-Generation Immersive-AI
based Retail Technologies.
– 10:15–10:45 Jeremy Heng (ESSEC Business School): Automated and Efficient Inference for BayesianTime Series Models.
• 10:45–11:00 Coffee break.
• 11:00–12:00 Scientific talks:– 11:00–11:30 Farah Benamara (CNRS): Generative AI for Social Good: A Myth or a Reality?
– 11:30–12:00 Benoit Delinchant (CNRS): Around dynamical systems identification, modelling, andoptimization.
• 12:00–13:30 Lunch break.
• 13:30–15:00 Scientific talks:– 13:30–14:00 Caroline Chaux-Moulin (CNRS): Proximal approaches for Non Negative Matrix/Tensor
Factorization.
– 14:00–14:30 Pierre Alquier (ESSEC Business School):PAC-Bayes bounds, or understanding thegeneralization of Bayesian learning algorithms.
– 14:30–15:00 Francisco Chinesta (CNRS): On advanced technologies for dynamical systems hybridmodelling.
• 15:00–15:30 Coffee break.
• 15:30–16:30 Round table: synergies and future collaborations.

1Scientific talks
Archan Misra (Singapore Management University) and Reetika Gupta (ESSEC Business School)

Shaping the Consumer-Centricity and Acceptance of Next-Generation Immersive-AI based RetailTechnologies. With e-commerce and online shopping continuing to grow rapidly (arguably even more due to theadded impetus of the pandemic), retailers and marketers have a strong interest in fostering strong consumer experiences via online, digital platforms. This project focuses on immersive AI technologies representing next-generation
retail technologies. It is unique as it combines a set of hitherto standalone technological capabilities, such as augmented reality, AI-based human instruction comprehension and big data analytics to create an immersive experiencethat minimises the human effort required in a single shopping episode simulating a near-human decision-making process. While building out these experiences require expertise with mobile/wearable sensing technologies and applications,the suitable design of such AR/AI-based workflows will require addressing a set of novel consumer behaviour-driven
questions such as 1) What are the benefits and costs to the consumer when interacting with immersive AI technology(vs. single AI-based technologies, conventional electronic catalogues) 2) Psychological and sociological factors that
influence the effectiveness of immersive-AI. 3) How can immersive-AI technologies incorporate the social/individual
challenges that the consumer face during the deployment of Apps?
We propose to investigate these research questions and test our hypotheses by designing a prototype mobile App
based on such immersive-AI technology and then setting up experiments where it can be compared with other alternatives. The findings will have substantive implications for organizational learning and deployment of such technologies
in the marketplace.

Jeremy Heng (ESSEC Business School)
Automated and Efficient Inference for Bayesian Time Series Models. The use of probabilistic modelling isprevalent in scientific applications and in some social science disciplines. In this project, we focus on a very flexible
class of models for time series data known as state space models (SSM) or hidden Markov models. These models havefound a wide range of applications in many disciplines such as engineering, financial econometrics, neuroscience andweather forecasting. Learning the model parameters of SSMs requires marginalizing over all latent states to obtainthe likelihood function. This is often a computationally challenging task in practical situations, when the dimensionality of the state space is large, the observations are highly informative or there are strong and complex nonlinear
dependencies in the model. Our objective is a two-pronged approach of methodological development and practicalapplication. Firstly, we aim to develop automated and efficient inference algorithms for SSMs that can handle all
the above-mentioned regimes. Our focus on automation is crucial as we seek methods that can work well for a large
class of SSMs with as little user-input as possible. Our computational approach is based on sequential Monte Carlo(SMC) methods, also commonly known as particle filters, which can provide state-of-the-art approximations. The
performance of SMC is highly dependent on the choice of proposal distributions. Our motivation is to push the limitsof the recently developed controlled-SMC method of (Heng et al., 2020) in challenging regimes, and further develop itfor widespread user-adoption. Our strategy is to exploit various advances in machine learning to push the capabilities of state-of-the-art SMC methods. We intend to harness the flexibility of deep neural networks to construct good
proposal distributions that respect the model structure of SSMs. This differs from the typical use of neural networks
to directly build models and allows us to inherit its black-box nature which has been a key component of its prolificuser-adoption. To develop a fully automated and efficient methodology, we will also rely on modern tools such as
automatic differentiation and stochastic gradient descent, and ideas from the reinforcement learning and variational
inference literature.
Secondly, we plan to apply our proposed methodology to push the boundaries of state space modelling in three challenging application areas: epidemiological agent-based models, dynamic structural models from economics and finance,and spatio-temporal models for solar irradiance forecasting.

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Farah Benamara (CNRS@CREATE)
Generative AI for Social Good: A Myth or a Reality?. Linguistically-informed processing of unstructured
textual interactions offers an important testing ground for hybrid AI and AI for social good, in particular when theattempt is to automatically understand beyond what is said. This talk is about the implicit nature of linguistic expressions investigating the role of context in their automatic processing: If humans need context, what about machines?
I attempt to answer this question on three particular NLP applications: Emotion detection, Hate speech detection
and crisis management. I review main findings of current studies and question the use of generative AI models inapplications with great social and ethical implications for society.

Benoit Delinchant (CNRS@CREATE)
Dynamical systems identification, modelling, and optimization applied to energy systems. Decisionmaking on dynamic systems requires the implementation of a complete modelling and algorithm chain, from data
sensing through digital twin to actuation. In this presentation, we will look at the key stages and issues associatedwith modelling and data assimilation through to optimization. We will apply this to the energy management ofbuildings.

Caroline Chaux-Moulin (CNRS@CREATE)
Proximal approaches for Non Negative Matrix/Tensor Factorization. Different approaches can be considered
for solving inverse problems in signal and image processing. We considered in this work the variational formulationof the problem, which consists in seeking the solution of the inverse problem as the solution of an optimization
problem. The criterion to be minimized depends not only on the observation model (data fidelity term), but also
on a priori assumptions about the target solution (regularization terms, possibly expressed in a transformed domainsuch as the wavelet domain). Once the minimization problem has been identified, proximal approaches can be usedto solve it, taking into account the mathematical properties of the terms constituting the criterion (differentiability,proximability, convexity, etc.). In this presentation, we will focus on two inverse problems. The first deals with source
separation in nuclear magnetic resonance (NMR) spectroscopy, for which we have proposed an iterative algorithm
to identify sources and their concentrations in different mixtures (NMF). The second addresses the problem of nonnegative tensor factorization (NTF) of order N (canonical polyadic decomposition) with penalization. Based on a block
coordinate forward-backward method, we have proposed a tensor decomposition algorithm and successfully applied itto fluorescence spectroscopy data.
Blind source separation: joint work with S. Anthoine, A. Cherni and B. Torrésani under the bifrost project.
Tensor factorization: joint work with X. Vu, N. Thirion-Moreau and S. Maire.

Pierre Alquier (ESSEC Business School)
PAC-Bayes bounds, or understanding the generalization of Bayesian learning algorithms. The PACBayesian theory provides tools to understand the accuracy of Bayes-inspired algorithms that learn probability distributions on parameters. This theory was initially developped by McAllester about 20 years ago, and applied successfully
to various classes of machine learning algorithms. Recently, it led to tight generalization bounds for deep neuralnetworks, a task that cannot be achieved by standard generalization approaches such as Vapnik-Chervonenkis theory.
In this talk, I will provide a short introduction to the PAC-Bayes theory. I will discuss a few example of successful
applications, including a recent application to off-line policy evaluation in a joint work with Nicolas Chopin (ENSAEParis) and Otmane Sahki (CRITEO).

Francisco Chinesta (CNRS@CREATE)On advanced technologies for dynamical systems hybrid modelling. This presentation will address informed
and augmented learning technologies applied to dynamical systems modelling and simulation. Two different types of3

data-driven models will be discussed: (i) the ones that proceed by learning the dynamics itself, and then using it for
performing time-integration; and (ii), the ones able to construct a regression from different experienced trajectoriesof the dynamical system. Special emphasis will be made on the treatment of the multiple parametric dimensions while
avoiding curse of dimensionality issues, as well as the possibility (and sometimes necessity) of learning time-multiscale dynamics.

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