arXiv Machine Learning By Haichen Hu, David Simchi-Levi

Sharp Structure-Agnostic Minimax Risk for Partial Linear Models

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arXiv:2609. 07997v1 Announce Type: new Abstract: We characterize the sharp structure-agnostic minimax risk for coefficient estimation in the partial linear model when the outcome and treatment nuisances are learned by two distinct black-box learners, which resolves the open problem in double machine learning posed by Gu (2025).

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arXiv Machine Learning
Jun 15

A Complexity Measure for Active Learning in Multi-group Mean Estimation

arXiv:2606. 14690v1 Announce Type: new Abstract: We study a \emph{max-risk} objective for active learning in a multi-group mean estimation $d$-armed bandits: a learner adaptively allocates a budget of $T$ samples across $d$ groups to minimize the worst-case uncertainty index $\max_{k\in[d]}\sigma_k^2/n_k$, where $\sigma_k$ is the standard deviation of the distribution of arm $d$, and $n_k$ is the number of times arm $d$ is sampled.

By Abdellah Aznag, Rachel Cummings, Adam N. Elmachtoub
arXiv Statistics ML
Sep 7

Reconciling Universal and Uniform Learning with $Q$-Aggregation

The paper investigates regression with bounded responses, comparing two learning frameworks: model selection aggregation, which requires improper algorithms to achieve minimax excess risk, and universal learning, where empirical risk minimization suffices for exponential learning rates. For finite hypothesis classes, the authors show that the $Q$-aggregation estimator simultaneously attains minimax optimal tails and exponential universal rates, while other common estimators fail to do so. For countably infinite classes, they prove an inherent trade‑off between exponential universal and minimax uniform rates, resolved by combining optimal algorithms from each framework via $Q$-aggregation.

By Mikael M{\o}ller H{\o}gsgaard, Patrick Rebeschini, Tobias Wegel