the availability of previous task data may be limited due to storage constraints or data privacy concerns when learning new tasks. This challenge is prevalent in continual learning, meta-learning, domain adaptation, generative model and
it poses a challenge to validate whether the distribution of the model after unlearning matches that of a fully retrained model using the remaining dataset. As a result, recent research has embraced the concept of approximate
We propose identifying the data subset that presents the most significant challenge for influence erasure, i.e. , pinpointing the worst-case forget set. Utilizing a bi-level optimization principle, we amplify unlearning challenges at the upper optimization level to emulate worst-case scenarios, while...
The increasing use of machine learning algorithms for nearly every aspect of our lives has brought a new challenge to the forefront, one of user-privacy. Once the data has been shared by the user online, it is difficult to revoke the acc... A Goyal,V Hassija,V Albuquerque - 2021 Thirte...
Feel free to dive deeper into this topic, experiment with different values, and explore the fascinating world of probability distributions in machine learning! August 21, 2024 Statistics Zarar Afzal Boosting Algorithms in Machine Learning: Enhancing Model Accuracy ...
We hope the response to this work goes beyond optimizing datasets to be “bigger” and “better”—a goal that does nothing to challenge the current paradigm of techniques idolizing speed and scale. Instead, we aspire for this survey to also prompt a more cautious and complex view of the co...
The broad application of artificial intelligence techniques in medicine is currently hindered by limited dataset availability for algorithm training and validation, due to the absence of standardized electronic medical records, and strict legal and ethic
In non-stationary learning settings, whereby the inputs presented to the system are characterized by statistics that change over time, a key challenge is mitigating forgetting of old representations learned as new ones get acquired. This is something that animals, including humans, exhibit as a ...
This poses a challenge to machine learning: how to proceed when an individual retracts permission to use data which has been part of the training process of a model? From this question emerges the field of machine unlearning, which could be broadly described as the investigation of how to "...
Machine learningMachine unlearningPrivacyRecently enacted legislation grants individuals certain rights to decide in what fashion their personal data may be used and in particular a "right to be forgotten". This poses a challenge to machine learning: how to proceed when an individual retracts ...