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Adaptive Optimizers Can Get a Policy Ranking Wrong: Momentum, Dependent Data, and a Two-Run Remedy
Sunyoung An, Xiaoming Huo
Operators choosing among decision rules, such as when a battery charges or where an idle taxi goes, rank them by estimated long-run value. A standard estimator, linear temporal-dif…
The Noise Premium in Adversarial Training for Kernel Regression
Yiling Xie, Xiaoming Huo
Adversarial training can improve the robustness of predictive models to bounded perturbations, often at the cost of statistical efficiency. We study this trade-off in kernel regres…
Matching Rates and Optimal Allocation for Federated Probe-Logit Distillation under Heterogeneous Bandwidth Budgets
Prasanjit Dubey, Xiaoming Huo
In federated language modeling, nodes each hold samples but cannot pool data or exchange full-precision gradients or weights. We study the minimax rate at which a condition…
Certified Adaptive Refresh: Anytime-Valid Monitoring for Federated Conformal RAG
Prasanjit Dubey, Xiaoming Huo
Question-answering services built on retrieval-augmented generation (RAG), in which a language model answers from retrieved documents, are inspected continuously and upgraded repea…
Federated Language Models Under Bandwidth Budgets: Distillation Rates and Conformal Coverage
Prasanjit Dubey, Xiaoming Huo
Hospitals, companies, and research consortia want language models that learn from, and answer questions over, data that must stay where it is held, over links with limited bandwidt…
Kernel Selection is Model Selection: A Unified Complexity-Penalized Approach for MMD Two-Sample Tests
Yijin Ni, Xiaoming Huo
The Maximum Mean Discrepancy (MMD) is a cornerstone statistic for nonparametric two-sample testing, but its test power is dictated entirely by the chosen kernel. Because any fixed…
Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes
Hyunouk Ko, Xiaoming Huo
In this paper, we prove the universal consistency of wide and deep ReLU neural network classifiers trained on the logistic loss. We also give sufficient conditions for a class of p…