arXiv:2603. 25029v4 Announce Type: replace Abstract: We study online convex optimization (OCO) with two-point bandit feedback against a non-anticipating adaptive adversary.
By Haishan Ye
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:2608. 06337v1 Announce Type: cross Abstract: A monotone adversary observes an i.
By Anay Mehrotra
arXiv:2608. 15472v1 Announce Type: cross Abstract: The problem of networked information aggregation, studied in Kearns et al.
By Ambar Pal
arXiv:2609.06430v1 Announce Type: new
Abstract: We study the identity straight-through estimator (STE) for training a two-layer binary-activation network with hinge loss from the perspective of Stati...
By Yiming Ying
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