arXiv:2209. 01754v5 Announce Type: replace-cross Abstract: The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those that will be faced when the decision rule is deployed.
By Roshni Sahoo, Lihua Lei, Stefan Wager
The paper investigates how policy learning algorithms should balance expected welfare against sampling risk in evidence-based policymaking. It demonstrates that algorithmic stability—specifically, a policy’s insensitivity to the replacement of a single experimental unit—limits sampling risk. The authors introduce policy‑vote bagging, which trains on many subsamples and averages their votes, preserving expected welfare while improving expected utility for risk‑averse researchers, and provide sharp bounds linking estimation accuracy, subsample size, and welfare variation, including an exact guarantee under CARA utility.
By Harvey Barnhard, Giacomo Opocher, Rahul Singh
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
The paper addresses the mismatch between learner and client data distributions in federated learning, noting that traditional client selection methods often ignore this misalignment. It introduces a dynamic, influence-aware client selection framework that uses a small proxy dataset to estimate each client's utility for the learner’s objective, prioritizing informative sources while mitigating noise and heterogeneity. Experiments on CIFAR-10 with heterogeneous partitions show the proposed method outperforms static and dynamic baselines, achieving faster convergence and higher accuracy.
By Yiming Xie, Lili Su, Ningfang Mi
arXiv:2606. 30372v1 Announce Type: new Abstract: Quantitative research across the social and behavioral sciences depends on human subject experiments that are expensive, slow, and subject to sampling bias.
By Haobo Yang
The paper investigates regression when outcomes are observed only after passing through selection filters that depend on both covariates and outcomes, a common issue in fields such as clinical trials, labor markets, and auctions. It provides a complete characterization of the minimal assumptions on the functional forms of selection processes that allow regression to remain possible, and shows that the regression function can sometimes be identified even when the selection filter itself cannot. Under stronger identification conditions, the authors also deliver finite‑sample estimation guarantees, explicit convergence rates, and oracle‑efficient algorithms, offering the first general‑purpose estimation method for this broad class of selection problems.
By Vikram Kher, Jane H. Lee, Anay Mehrotra, Manolis Zampetakis