Portrait of Jacob Comeau is unavailable

Jacob Comeau

Master's Research - Université Laval
Supervisor
Cem Subakan
Research Topics
Deep Learning
Machine Learning Theory
Representation Learning

Publications

Sample Compression for Continual Learning
Jacob Comeau
Mathieu Bazinet
Pascal Germain
Cem Subakan
2025-03-13
ArXiv (preprint)
arxiv.org
arxiv.org
Sample Compression for Continual Learning
Jacob Comeau
Mathieu Bazinet
Pascal Germain
Cem Subakan
2025-03-13
ArXiv (preprint)
doi.org
arxiv.org
Sample Compression for Self Certified Continual Learning
Jacob Comeau
Mathieu Bazinet
Pascal Germain
Cem Subakan
Continual learning algorithms aim to learn from a sequence of tasks, making the training distribution non-stationary. The majority of existi… (see more)ng continual learning approaches in the literature rely on heuristics and do not provide learning guarantees. In this paper, we present a new method called Continual Pick-to-Learn (CoP2L), which is able to retain the most representative samples for each task in an efficient way. CoP2L combines the Pick-to-Learn algorithm (rooted in the sample compression theory) and the experience replay continual learning scheme. This allows us to provide non-vacuous upper bounds on the generalization loss of the learned predictors, numerically computable after each task. We empirically evaluate our approach on several standard continual learning benchmarks across Class-Incremental, Task-Incremental, and Domain-Incremental settings. Our results show that CoP2L is highly competitive across all setups, often outperforming existing baselines, and significantly mitigating catastrophic forgetting compared to vanilla experience replay in the Class-Incremental setting. It is possible to leverage the bounds provided by CoP2L in practical scenarios to certify the predictor reliability on previously learned tasks, in order to improve the trustworthiness of the continual learning algorithm.
2025-03-13
ArXiv (preprint)
arxiv.org
arxiv.org
Sample Compression for Self Certified Continual Learning
Jacob Comeau
Mathieu Bazinet
Pascal Germain
Cem Subakan
Continual learning algorithms aim to learn from a sequence of tasks, making the training distribution non-stationary. The majority of existi… (see more)ng continual learning approaches in the literature rely on heuristics and do not provide learning guarantees. In this paper, we present a new method called Continual Pick-to-Learn (CoP2L), which is able to retain the most representative samples for each task in an efficient way. CoP2L combines the Pick-to-Learn algorithm (rooted in the sample compression theory) and the experience replay continual learning scheme. This allows us to provide non-vacuous upper bounds on the generalization loss of the learned predictors, numerically computable after each task. We empirically evaluate our approach on several standard continual learning benchmarks across Class-Incremental, Task-Incremental, and Domain-Incremental settings. Our results show that CoP2L is highly competitive across all setups, often outperforming existing baselines, and significantly mitigating catastrophic forgetting compared to vanilla experience replay in the Class-Incremental setting. It is possible to leverage the bounds provided by CoP2L in practical scenarios to certify the predictor reliability on previously learned tasks, in order to improve the trustworthiness of the continual learning algorithm.
2025-03-13
ArXiv (preprint)
arxiv.org
arxiv.org