3 citations · 12 across the 15 of their papers we have counts for
20 papers
Causal Inference Theory with Information Dependency Models
Benjamin Heymann, Michel de Lara, Jean-Philippe Chancelier
Inferring the potential consequences of an unobserved event is a fundamental scientific question. To this end, Pearl's celebrated do-calculus provides a set of inference rules to d…
Topological Conditional Separation
Michel de Lara, Jean-Philippe Chancelier, Benjamin Heymann
Pearl's d-separation is a foundational notion to study conditional independence between random variables. We define the topological conditional separation and we show that it is eq…
Conditional Separation as a Binary Relation. A Coq Assisted Proof
Jean-Philippe Chancelier, Michel de Lara, Benjamin Heymann
The concept of d-separation holds a pivotal role in causality theory, serving as a fundamental tool for deriving conditional independence properties from causal graphs. Pearl defin…
Decomposition-Coordination Method for Finite Horizon Bandit Problems
Michel de Lara, Benjamin Heymann, Jean-Philippe Chancelier
Optimally solving a multi-armed bandit problem suffers the curse of dimensionality. Indeed, resorting to dynamic programming leads to an exponential growth of computing time, as th…
Best Convex Lower Approximations of the l 0 Pseudonorm on Unit Balls
Thomas Bittar, Jean-Philippe Chancelier, Michel de Lara
Whereas the norm of a vector measures amplitude (and is a 1-homogeneous function), sparsity is measured by the 0-homogeneous l0 pseudonorm, which counts the number of nonzero compo…
Conditional Infimum and Hidden Convexity in Optimization
Jean-Philippe Chancelier, Michel de Lara
Detecting hidden convexity is one of the tools to address nonconvex minimization problems. After giving a formal definition of hidden convexity, we introduce the notion of conditio…