Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning
The field of machine learning (ML) has experienced a major boom in the past years, both in theoretical developments and application areas. However, the rampant adoption of ML methodologies has revealed that models are usually adopted to make decisions without taking into account the uncertainties in...
| Autor: | |
|---|---|
| Tipo de recurso: | tesis doctoral |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/3546 |
| Acceso en línea: | https://hdl.handle.net/20.500.14352/3546 |
| Access Level: | acceso abierto |
| Palabra clave: | 004.85(043.2) Machine Learning Aprendizaje automático (Inteligencia artificial) Inteligencia artificial (Informática) 1203.04 Inteligencia Artificial |
| Sumario: | The field of machine learning (ML) has experienced a major boom in the past years, both in theoretical developments and application areas. However, the rampant adoption of ML methodologies has revealed that models are usually adopted to make decisions without taking into account the uncertainties in their predictions. More critically, they can be vulnerable to adversarial examples, strategic manipulations of the data with the goal of fooling those systems. For instance, in retailing, a model may predict very high expected sales for the next week, given a certain advertisement budget. However, the predicted variance may also be quite big, thus making the prediction almost useless depending on the risk tolerance of the company. Similarly, in the case of spam detection, an attacker may insert additional words in a given spam email to evade being classified as spam by making it to appear more legit. Thus, we believe that developing ML systems that take into account predictive uncertainties and are robust against adversarial examples is a must for critical, real-world tasks. This thesis is a step towards achieving this goal... |
|---|