Description
Date depot: 1 janvier 1900
Titre: Machine learning under constrained budget for information extraction and search applications
Directeur de thèse:
Ludovic DENOYER (Facebook)
Directeur de thèse:
Thierry ARTIÈRES (LIS)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Non defini
Resumé:
Automated tools for machine learning are usually designed to maximize performance criteria like classification accuracy or ranking maximization, regression error minimization, etc . The learning criterion of quality is directly integrated into a training loss to be optimized via some optimization technique like optimization algorithms or gradient descent for example. However, for many modern applications, real learning criteria should also consider external constraints such as the cost of data or feature acquisition, computation time, memory usage, or even very concrete external factors such as power consumption, development time, etc. Just to give a few examples, in medicine, there is a cost associated with each medical procedure ((blood test, x-ray, biological analysis, etc) and acquiring new features or data, deciding new tests may be costly or even dangerous for patients. More recently this problem has started to receive attention in the context of the Big Data framework. Mining very large amounts of data involved in many Big Data applications, for example for information retrieval or information extraction, for the analysis of social networks, for marketing applications on large populations is unfeasible. Therefore, it is important to devise algorithms able to learn specific tasks under different types of constraints that limit the possible exploitation of additional data or more generally additional information. This is a new problem in machine learning, which is emerging in the context, where huge quantities of data have to be processed for different types of applications. This general problem has recently been explored through different directions under the names of Budget Learning or Cost Sensitive Learning.
Doctorant.e: Contardo Gabriella