提出可压缩样本的持续学习方法，实现可靠预测的量化保证。
Sample Compression for Self Certified Continual Learning
- 基于样本压缩理论，有原则地保留每轮任务的代表性样本。
- 首次在持续学习中获得非平凡的、可计算的泛化误差上界。
- 适合关注模型可靠性与可解释性的研究者与工业应用。
持续学习算法旨在从任务序列中学习。为避免灾难性遗忘，现有方法多依赖启发式策略，缺乏可计算的学习保证。本文提出持续选样学习（CoP2L），基于样本压缩理论，以原则性且高效的方式保留每项任务的代表性样本。由此可推导出每次任务后学习预测器的泛化损失的非平凡、数值可计算上界。在标准持续学习基准上，于类增量与任务增量设置下评估表明，CoP2L有效缓解灾难性遗忘。其性能在实证上与基线方法相当，同时在持续学习中提供非平凡的预测器可靠性保证。
原文摘要 · Abstract (English)
Continual learning algorithms aim to learn from a sequence of tasks. In order to avoid catastrophic forgetting, most existing approaches rely on heuristics and do not provide computable learning guarantees. In this paper, we introduce Continual Pick-to-Learn (CoP2L), a method grounded in sample compression theory that retains representative samples for each task in a principled and efficient way. This allows us to derive non-vacuous, numerically computable upper bounds on the generalization loss of the learned predictors after each task. We evaluate CoP2L on standard continual learning benchmarks under Class-Incremental and Task-Incremental settings, showing that it effectively mitigates catastrophic forgetting. It turns out that CoP2L is empirically competitive with baseline methods while certifying predictor reliability in continual learning with a non-vacuous bound.
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