Demo

Science · Publications

What is published. What is in preparation. Where to verify it.

What has been peer-reviewed and accepted, what is still in preparation, who the authors are and where to read the source documents.

01Published

A peer-reviewed article, accepted in Frontiers in Artificial Intelligence.

Peer-reviewed · Accepted for publicationResearch article

Maximum Entropy Relaxation of Multi-Way Cardinality Constraints for Synthetic Population Generation

François Pachet, Jean-Daniel Zucker

The article underwent scientific peer review, followed by an acceptance decision from the journal.

Journal
Frontiers in Artificial Intelligence
Status
Peer-reviewed · Accepted for publication
Accepted
30 September 2026
Final version
Final typeset version forthcoming.
View the article on Frontiers →Preprint version on arXiv (arXiv:2603.22558) →

The article addresses the population generation method; validation of behavioural simulations relies on separate protocols.

View details →

Formalizes the generation of synthetic populations satisfying multidimensional unary, binary and ternary constraints. Introduces the convex dual formulation and the L-BFGS solver, and systematically compares the method with generalized raking on NPORS-derived benchmarks spanning 4 to 40 attributes.

SourceFrançois Pachet, Jean-Daniel Zucker — Frontiers in Artificial Intelligence — Accepted on 30 September 2026 — DOI 10.3389/frai.2026.1840945 View
Final typeset version forthcoming.

02In preparation

What is not yet a published result.

This work is documented as being in preparation. It has neither been submitted nor peer reviewed: no result from it can be cited as established at this stage.

In preparation · Not submitted to date

Balanced Exact Population Synthesis via Maximum Entropy Repair

François Pachet, Jean-Charles Régin

Date
No submission date
Source / identifier
Not submitted to date
View details →

Work in preparation. No result from it is presented as established.

Type
Methodological paper
Publication / submission
Not submitted to date
Identifier
Aucun identifiant

SourceFrançois Pachet, Jean-Charles Régin — Not submitted to date
Work in preparation: no source document is available to date.

In preparation · Not submitted to date

Scalable Maximum Entropy Population Synthesis via Persistent Contrastive Divergence

Imagine All The People scientific team

Date
No submission date
Source / identifier
Not submitted to date
View details →

Work in preparation. No result from it is presented as established.

Type
Methodological paper
Publication / submission
Not submitted to date
Identifier
Aucun identifiant

SourceImagine All The People scientific team — Not submitted to date
Work in preparation: no source document is available to date.

03Editorial contribution

Co-editing a Research Topic is not the same as publishing an article.

Our presence in Frontiers is twofold and the two must not be confused: the MaxEnt research article, peer-reviewed and accepted for publication (section 01, whose arXiv deposit is the preprint version of the same article), and, separately, the co-editing of the Synthetic Populations Research Topic.

Editorial contribution

Synthetic Populations

François Pachet, Frédéric Kaplan (École Polytechnique Fédérale de Lausanne), Mirko Degli Esposti (Université de Bologne)

Role
Co-editing of a Research Topic
Source
Frontiers in Artificial Intelligence
View the Research Topic →
View details →

An edition bringing together international work on the generation, use, validation and ethical issues of synthetic populations.

SourceSynthetic Populations, Frontiers in Artificial Intelligence View
Co-editing of a thematic issue: the collected articles come from other research teams.

An editorial contribution or collaboration between researchers constitutes neither institutional validation of Imagine All The People nor certification of its results.

04Methodological lineage

A lineage of methods, not a continuity of results.

  1. Musical Harmonization with Constraints: A Survey

    François Pachet, Pierre Roy — Constraints Journal

    Constraint programming applied to creative generation.

  2. Generating 1/f Noise Sequences as Constraint Satisfaction: The Voss Constraint

    François Pachet, Pierre Roy, Alexandre Papadopoulos, Jason Sakellariou — IJCAI 2015

    Global statistical properties under constraints.

  3. Generating all Possible Palindromes from Ngram Corpora

    Alexandre Papadopoulos, Pierre Roy, Jean-Charles Régin, François Pachet — IJCAI 2015

    Generation of structures satisfying invariants.

  4. Sampling Markov Models under Constraints: Complexity Results for Binary Equalities and Grammar Membership

    Stéphane Rivaud, François Pachet — ArXiv:1711.10436

    Complexity results for sampling under constraints.

  5. Maximum Entropy Relaxation of Multi-Way Cardinality Constraints for Synthetic Population Generation

    François Pachet, Jean-Daniel Zucker — Frontiers in Artificial Intelligence — DOI 10.3389/frai.2026.1840945

    Generation of synthetic populations under multi-way constraints.

SourceFrançois Pachet — Full list of publications (personal website) View

05Authors & contributors

Who authored the work referenced on this page.

  • Portrait of François Pachet

    François Pachet

    Co-author of the MaxEnt article accepted in Frontiers in Artificial Intelligence, co-author of the work in preparation Balanced Exact Population Synthesis, co-editor of the Synthetic Populations Research Topic.

  • Portrait of Jean-Daniel Zucker

    Jean-Daniel Zucker

    Co-author of the MaxEnt article accepted in Frontiers in Artificial Intelligence.

  • Jean-Charles Régin

    Co-author of the work in preparation Balanced Exact Population Synthesis via Maximum Entropy Repair.

View scientific profile

Continue the demonstration

Your next decision

Which decision do you want to explore?

Describe your need. We can point you to the right level of support.

Talk about your project →

What if you tested
your next decision?

Define your decision. Compare the trajectories it could lead to.

Explore the product