Mark Kong — accepted AI-conference papers

2025

Are Greedy Task Orderings Better Than Random in Continual Linear Regression?

Matan Tsipory, Ran Levinstein, Itay Evron, Mark Kong, Deanna Needell, Daniel Soudry

NeurIPS 2025poster

We analyze task orderings in continual learning for linear regression, assuming joint realizability of training data. We focus on orderings that greedily maximize dissimilarity between consecutive tasks, a concept briefly explored in prior work but still surrounded by open questions. Using tools fro…

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2023

Nearly Optimal Bounds for Cyclic Forgetting

William Joseph Swartworth, Deanna Needell, Rachel Ward, Mark Kong, Halyun Jeong

NeurIPS 2023poster

We provide theoretical bounds on the forgetting quantity in the continual learning setting for linear tasks, where each round of learning corresponds to projecting onto a linear subspace. For a cyclic task ordering on $T$ tasks repeated $m$ times each, we prove the best known upper bound of $O(T^2/m…

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