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Xiaoyu Li

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#machine learning Preprint Sep 2026

Nearly Tight Rademacher Bounds for Sparsely Activated Neural Networks

An input may activate few hidden units even when different inputs collectively use an entire network. We study the statistical complexity of this input-dependent sparsity in the one-hidden-layer ReLU model of Awasthi et al. (COLT 2024). For width $s$, at most $k$ active units per input, and effective weight and bias bo...

Xiao-Yu Li, Zhizhou Sha, Jiao-Jiao Jiang et al. · 0 citations
Preprint Jul 2026

TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories

LLM agents reach users through resellers, who may rebrand a developer's agent or substitute a cheaper model. When provenance is disputed, attribution rests on the trajectory log (the record of tool calls, observations, and executed actions, not the model's reasoning), which the reseller stores and processes to meter us...

Zhengguang Gao, Xiaoyu Li, Xiaoyang Feng et al. · 1 citation
Preprint Jul 2026

The Exact Worst-Case Tail Probability under Bounded Kurtosis

We determine exactly what a kurtosis bound buys for one-sided tail control. For the class $\mathcal{C}(\kappa)$ of real random variables with mean $0$, variance $1$, and fourth moment at most $\kappa$, the skewness left free, we compute the worst-case tail probability $V_1(t,\kappa)=\sup_{X\in\mathcal{C}(\kappa)}\mathb...

Xiaoyu Li, Andi Han, Jiaojiao Jiang et al. · 0 citations
Preprint Aug 2026

Optimistic Rates for Multiclass PAC Learning

Worst-case multiclass bounds do not become smaller when the best classifier is already nearly correct: what is missing is an optimistic rate, a guarantee whose fluctuation scales with the oracle risk itself. For a class of Natarajan dimension $d_N$ and Daniely-Shalev-Shwartz dimension $d_{DS}$, the optimal excess risk...

Xiao-Yu Li, Andi Han, Jiao-Jiao Jiang et al. · 1 citation

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