Permutation Invariant Learning with High-Dimensional Particle Filters

Akhilan Boopathy, Aneesh Muppidi, Peggy Yang, Abhiram Iyer, William Yue, Ila R Fiete

Keywords: permutation-invariant learning, continual learning, loss of plasticity, catastrophic forgetting, particle filter, high-dimensional

TL;DR: We propose a particle-filter based learning algorithm that is invariant to the permutations of the batches or tasks presented to it.

Abstract: Sequential learning in deep models often suffers from challenges such as catastrophic forgetting and loss of plasticity, largely due to the permutation dependence of gradient-based algorithms, where the order of training data impacts the learning outcome. In this work, we introduce a novel approximately permutation-invariant learning framework based on high-dimensional particle filters. We theoretically demonstrate that particle filters are invariant to the sequential ordering of training minibatches or tasks, offering a principled solution to mitigate catastrophic forgetting and loss-of-plasticity. We develop an efficient particle filter for optimizing high-dimensional models, combining the strengths of Bayesian methods with gradient-based optimization. Through extensive experiments on continual supervised and reinforcement learning benchmarks, including SplitMNIST, SplitCIFAR100, and ProcGen, we empirically show that our method consistently improves performance, while reducing variance compared to standard baselines.

Primary Area: other topics in machine learning (i.e., none of the above)

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Submission Number: 8062