1 citations · 2 across the 6 of their papers we have counts for
6 papers
A Job I Like or a Job I Can Get: Designing Job Recommender Systems Using Field Experiments
Guillaume Bied, Philippe Caillou, Bruno Crépon +3
Recommendation systems (RSs) are increasingly used to guide job seekers on online platforms, yet the algorithms currently deployed are typically optimized for predictive objectives…
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning
Shuyu Dong, Michèle Sebag, Kento Uemura +4
Causal learning tackles the computationally demanding task of estimating causal graphs. This paper introduces a new divide-and-conquer approach for causal graph learning, called DC…
Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges
Audrey Poinsot, Alessandro Leite, Nicolas Chesneau +2
This paper provides a comprehensive review of deep structural causal models (DSCMs), particularly focusing on their ability to answer counterfactual queries using observational dat…
KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag
This paper investigates the control of an ML component within the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) devoted to black-box optimization. The known CMA-ES weakn…
Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling
Thomas Philip Runarsson, Marc Schoenauer, Michèle Sebag
Greedy heuristics may be attuned by looking ahead for each possible choice, in an approach called the rollout or Pilot method. These methods may be seen as meta-heuristics that can…
Alternative Restart Strategies for CMA-ES
Ilya Loshchilov, Marc Schoenauer, Michèle Sebag
This paper focuses on the restart strategy of CMA-ES on multi-modal functions. A first alternative strategy proceeds by decreasing the initial step-size of the mutation while doubl…