arXiv Machine Learning By Wenzhi Gao, Zhaonan Qu, Yinyu Ye, Madeleine Odell

Tight Nonasymptotic Local Convergence of Sinkhorn-Knopp

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arXiv:2608. 11760v1 Announce Type: cross Abstract: We revisit the Sinkhorn-Knopp (SK) algorithm for the matrix scaling problem.

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arXiv Machine Learning
Jul 21

Scaling Limits of Constant-Stepsize SGD at Flat Minima

arXiv:2607. 16384v1 Announce Type: new Abstract: For stochastic gradient descent (SGD) with a constant stepsize $\alpha$, the invariant law of the iterates, centered at a minimizer, describes the behavior of the algorithm over long time horizons.

By Jingyi Zhang, Cheng Mao, Debankur Mukherjee
arXiv Machine Learning
Jul 27

On the Convergence of Stochastic Low-Rank Adaptation

arXiv:2607. 21975v1 Announce Type: new Abstract: Low-rank adaptation (LoRA) optimizes $J(B,A)=\mathcal L(W_\mathrm{base}+sBA)$ over two adapters $B \in \mathbb{R}^{m \times r}$ and $A \in \mathbb{R}^{r \times n}$ that form a low-rank update to a frozen pretrained weight matrix $W_\mathrm{base} \in \mathbb{R}^{m \times n}$.

By Ru Wang, Chengchang Liu, John C. S. Lui