ESSEC METALAB

RESEARCH

Expanding Scientific Knowledge

ESSEC’s approach to AI, Data Science and Technology is original and reflects our strengths and tradition of excellence of research in Data Science, Economics, Management, Law and Social Sciences. Through their academic research, the 160 Research Professors at ESSEC improve society's understanding of the profound transformations caused by the advancement of science. They all prove everyday that social, economic and management sciences play a key role in fostering scientific and human progress.

Workplace & Business models

AI & Business Data Science

AI Policy & Regulation

Culture & Sustainability

DYNAMIC TRAVELING SALESMAN PROBLEM WITH STOCHASTIC RELEASE DATES

[ARTICLE] This paper addresses the Dynamic Traveling Salesman Problem with Stochastic Release Dates (DTSP-srd), proposing a Markov Decision Process and ...

A BRANCH-AND-CUT ALGORITHM FOR THE INVENTORY ROUTING PROBLEM WITH PICKUPS AND DELIVERIES

[ARTICLE] This paper introduces a mixed integer linear programming model for the Inventory Routing Problem with Pickups and Deliveries (IRP-PD), ...

ROBUST INFERENCE IN STRUCTURAL VECTOR AUTOREGRESSIONS WITH LONG-RUN RESTRICTIONS

[ARTICLE] This paper presents a robust inference method for structural vector autoregressions with near unit roots by combining the Anderson-Rubin ...

EXPLAINING AND PREDICTING THE IMPACT OF AUTHORS WITHIN A COMMUNITY: AN ASSESSMENT OF THE BIBLIOMETRIC LITERATURE AND APPLICATION OF MACHINE LEARNING

[ARTICLE] This paper evaluates how well bibliometric data and theories predict author impact and breakthroughs in research, finding limited explanatory ...

EXACT APPROACHES FOR NETWORK DESIGN PROBLEM WITH RELAYS

[ARTICLE] This paper proposes two integer programming formulations for the directed network design problem with relays (DNDPR) and demonstrates that ...

CONSOLIDATING UNORGANISED RETAIL BUSINESSES THROUGH DIGITAL PLATFORMS: IMPLICATIONS FOR ACHIEVING THE UN SUSTAINABLE DEVELOPMENT GOALS

[ARTICLE] This study develops a framework showing how digitalization of unorganized retail in emerging markets transforms socio-economic conditions for subsistence ...

INFINITE-DIMENSIONAL GRADIENT-BASED DESCENT FOR ALPHA-DIVERGENCE MINIMISATION

[ARTICLE] This paper presents the (α,Γ)-descent algorithm for Bayesian α-divergence minimization, demonstrating improved optimization for mixture models in high dimensions ...

ROBUST HIGH DIMENSIONAL LEARNING FOR LIPSCHITZ AND CONVEX LOSSES

[ARTICLE] This paper establishes risk bounds for Regularized Empirical Risk Minimizers (RERM) under both subgaussian and heavy-tailed assumptions, introducing an ...

More

TWITTER
LINKEDIN