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Analysis of the Memorization and Generalization Capabilities of AI Agents: Are Continual Learners Robust?

Minsu Kim, Walid Saad

TL;DR

The paper tackles robustness in continual learning under non-stationary environments by jointly optimizing memorization and generalization using a capacity-limited memory $|\mathcal{M}_t|$. It introduces a probabilistic relaxation of worst-case risk across environments via the inverse-CDF $F_{\mathcal{R}}^{-1}(\alpha;\theta_t)$ and estimates the risk distribution from memory and current data. Theoretical results bound the memorization-generalization tradeoff as a function of memory size, showing that larger memory can hinder simultaneous optimality across all stored environments. Empirically, on rotated MNIST, the approach yields improved generalization to unseen rotations compared with memory-based baselines, supporting its potential for robust deployment in autonomous systems.

Abstract

In continual learning (CL), an AI agent (e.g., autonomous vehicles or robotics) learns from non-stationary data streams under dynamic environments. For the practical deployment of such applications, it is important to guarantee robustness to unseen environments while maintaining past experiences. In this paper, a novel CL framework is proposed to achieve robust generalization to dynamic environments while retaining past knowledge. The considered CL agent uses a capacity-limited memory to save previously observed environmental information to mitigate forgetting issues. Then, data points are sampled from the memory to estimate the distribution of risks over environmental change so as to obtain predictors that are robust with unseen changes. The generalization and memorization performance of the proposed framework are theoretically analyzed. This analysis showcases the tradeoff between memorization and generalization with the memory size. Experiments show that the proposed algorithm outperforms memory-based CL baselines across all environments while significantly improving the generalization performance on unseen target environments.

Analysis of the Memorization and Generalization Capabilities of AI Agents: Are Continual Learners Robust?

TL;DR

The paper tackles robustness in continual learning under non-stationary environments by jointly optimizing memorization and generalization using a capacity-limited memory . It introduces a probabilistic relaxation of worst-case risk across environments via the inverse-CDF and estimates the risk distribution from memory and current data. Theoretical results bound the memorization-generalization tradeoff as a function of memory size, showing that larger memory can hinder simultaneous optimality across all stored environments. Empirically, on rotated MNIST, the approach yields improved generalization to unseen rotations compared with memory-based baselines, supporting its potential for robust deployment in autonomous systems.

Abstract

In continual learning (CL), an AI agent (e.g., autonomous vehicles or robotics) learns from non-stationary data streams under dynamic environments. For the practical deployment of such applications, it is important to guarantee robustness to unseen environments while maintaining past experiences. In this paper, a novel CL framework is proposed to achieve robust generalization to dynamic environments while retaining past knowledge. The considered CL agent uses a capacity-limited memory to save previously observed environmental information to mitigate forgetting issues. Then, data points are sampled from the memory to estimate the distribution of risks over environmental change so as to obtain predictors that are robust with unseen changes. The generalization and memorization performance of the proposed framework are theoretically analyzed. This analysis showcases the tradeoff between memorization and generalization with the memory size. Experiments show that the proposed algorithm outperforms memory-based CL baselines across all environments while significantly improving the generalization performance on unseen target environments.
Paper Structure (7 sections, 3 theorems, 11 equations, 1 figure, 2 tables, 1 algorithm)

This paper contains 7 sections, 3 theorems, 11 equations, 1 figure, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Setup
  4. Problem Formulation
  5. Tradeoff between Memorization and Generalization
  6. Experiments
  7. Conclusion

Key Result

Theorem 1

For time $t$, let $\theta_{\mathcal{M}_t}^*$ be a global solution for all environments $\tau \in [1, \dots, |\mathcal{M}_t|]$ in $\mathcal{M}_t$ and suppose loss function $l (\cdot)$ to be $\lambda$-strongly convex and $L$-Lipschitz-continuous. Then, for the current model $\theta_t \in \Theta$ and $ where $\hat{\theta}_{\mathcal{M}_t}$ is the empirical solution for all environments $\tau \in [1, \

Figures (1)

  • Figure 1: Impact of the memory size on the memorization and generalization.

Theorems & Definitions (5)

  • Theorem 1
  • proof
  • Lemma 1
  • Proposition 1
  • proof