arXiv Machine Learning By Sami Davies, Benjamin Moseley, Heather Newman

Online Correlation Clustering: Simultaneously Optimizing All $\ell_p$-norms

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arXiv:2510. 15076v2 Announce Type: replace Abstract: The $\ell_p$-norm objectives for correlation clustering present a fundamental trade-off between minimizing total disagreements (the $\ell_1$-norm) and ensuring fairness to individual nodes (the $\ell_\infty$-norm).

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arXiv Machine Learning
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The First-Order Oracle Complexity of Lipschitz Convex Optimization in Nondual Settings

arXiv:2609. 20687v1 Announce Type: cross Abstract: We study first-order black-box convex optimization over an $\ell_p$-ball for objectives Lipschitz in the $\ell_q$-norm, solving in the affirmative the nonsmooth version of the COLT open question (Guz15b) on whether the geometry of a smaller feasible set ($p < q$) can improve convergence rates in convex optimization, and matching prior lower bounds up to logarithmic factors.

By David Mart\'inez-Rubio, Brian Bullins, Crist\'obal Guzm\'an, Mathieu Molina
arXiv Machine Learning
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High-Dimensional Calibration from Swap Regret

arXiv:2505. 21460v2 Announce Type: replace Abstract: We study online calibration of multi-dimensional forecasts over an arbitrary convex set $P \subset \mathbb{R}^d$ relative to an arbitrary norm $|\cdot|$.

By Maxwell Fishelson, Noah Golowich, Mehryar Mohri, Jon Schneider
arXiv Machine Learning
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Clustering with Non-adaptive Subset Queries

arXiv:2409. 10908v3 Announce Type: replace-cross Abstract: Recovering the underlying $k$-clustering of a set $U$ of $n$ points by asking pair-wise same-cluster queries has garnered significant interest in the past few years.

By Hadley Black, Euiwoong Lee, Arya Mazumdar, Barna Saha
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Stable Movement for Nondual Lipschitz Convex Optimization: Efficiency and Nearly Optimal Oracle Rates

We study efficient algorithms for realizing the first-order oracle complexity of optimization of $G$-Lipschitz convex functions with respect to the $\ell_{q}$-norm over an $\ell_{p}$-ball of radius $R$, where $1\leq p,q\leq \infty$. For $p<q$, we obtain error $\widetilde{O}_{p,q}(GR/T^{1/p-(1/q-1/2)_{+}})$ after $T$ oracle queries, efficiently realizing the nearly optimal rates of (MBG+26), thereby resolving the nonsmooth end of the COLT 2015 open problem (Guz15b).