347 results on '"Michèle Sebag"'
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2. Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges.
3. Frugal Generative Modeling for Tabular Data.
4. Modelling Dynamical Systems: Learning ODEs with No Internal ODE Resolution.
5. Toward Job Recommendation for All.
6. RECTO : REcommandation diminuant la Congestion par Transport Optimal.
7. Cartolabe: A Web-Based Scalable Visualization of Large Document Collections
8. From Graphs to DAGs: A Low-Complexity Model and a Scalable Algorithm.
9. Fairness in job recommendations: estimating, explaining, and reducing gender gaps.
10. I2SL: Learn How to Swarm Autonomous Quadrotors Using Iterative Imitation Supervised Learning.
11. Towards causal modeling of nutritional outcomes.
12. On the Identifiability of Hierarchical Decision Models.
13. Iterative Learning for Model Reactive Control: Application to Autonomous Multi-agent Control.
14. Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals.
15. Learning meta-features for AutoML.
16. Important Topics in Causal Analysis: Summary of the CAWS 2021 Round Table Discussion.
17. Automated Machine Learning with Monte-Carlo Tree Search.
18. From Abstract Items to Latent Spaces to Observed Data and Back: Compositional Variational Auto-Encoder.
19. Agnostic Feature Selection.
20. Dynamic Time Lag Regression: Predicting What & When.
21. ActivMetal: Algorithm Recommendation with Active Meta Learning.
22. Language Modelling for Collaborative Filtering: Application to Job Applicant Matching.
23. ASAP.V2 and ASAP.V3: Sequential optimization of an Algorithm Selector and a Scheduler.
24. Multi-Domain Adversarial Learning.
25. Matching Jobs and Resumes: a Deep Collaborative Filtering Task.
26. Anti Imitation-Based Policy Learning.
27. A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention.
28. Interactive Metric Learning-Based Visual Data Exploration: Application to the Visualization of a Scientific Social Network.
29. Coupling Evolution and Information Theory for Autonomous Robotic Exploration.
30. Maximum Likelihood-Based Online Adaptation of Hyper-Parameters in CMA-ES.
31. Combination of One-Class Support Vector Machines for Classification with Reject Option.
32. Experimental Design in Dynamical System Identification: A Bandit-Based Active Learning Approach.
33. Programming by Feedback.
34. A Recommender System for Process Discovery.
35. Learning Sparse Features with an Auto-Associator.
36. From Graphs to DAGs: A Low-Complexity Model and a Scalable Algorithm
37. Hybridizing Constraint Programming and Monte-Carlo Tree Search: Application to the Job Shop Problem.
38. Exploration vs Exploitation vs Safety: Risk-Aware Multi-Armed Bandits.
39. Multi-dimensional sparse structured signal approximation using split bregman iterations.
40. Fast Adaptive Object Detection towards a Smart Environment by a Mobile Robot.
41. Intensive surrogate model exploitation in self-adaptive surrogate-assisted cma-es (saacm-es).
42. Bi-population CMA-ES agorithms with surrogate models and line searches.
43. Sustainable cooperative coevolution with a multi-armed bandit.
44. Bandit-Based Search for Constraint Programming.
45. Collaborative hyperparameter tuning.
46. Alternative Restart Strategies for CMA-ES.
47. Pilot, Rollout and Monte Carlo Tree Search Methods for Job Shop Scheduling.
48. Upper Confidence Tree-Based Consistent Reactive Planning Application to MineSweeper.
49. BenchNN: On the broad potential application scope of hardware neural network accelerators.
50. APRIL: Active Preference Learning-Based Reinforcement Learning.
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