arXiv subjects

Ziao Yang

Publications and source records attributed to Ziao Yang.

7 recordsLinked to original sources

arXiv2026

Validation-Induced Shapley Shifts: How Validation Structure Distorts Data Valuation

Shapley values are widely used to attribute value to training data based on their marginal contribution to performance on a validation set. Existing practice often assumes these values are stable once the training data and model are fixed. In this work, we uncover a systematic vulnerability: even modest changes to the validation set, such as introducing noises, cause directional shifts in Shapley distributions. As noises are added, Shapley values of training samples compress toward zero. We trace this to a noise-induced neighborhood reshuffling effect: perturbations alter the local rank order between validation and training samples, flattening the valuation landscape. Using the KNN-Shapley framework, we show through synthetic and real data that these shifts are consistent and reproducible. Our findings challenge the assumption of Shapley stability and reveal a new axis of fragility in data valuation. We propose normalization and boundary-aware validation strategies to mitigate these distortions and enable more robust, interpretable valuation in machine learning marketplaces.

cs.LG↗

arXiv2026

Recontextualizing Famous Quotes for Brand Slogan Generation

Slogans are concise and memorable catchphrases that play a crucial role in advertising by conveying brand identity and shaping public perception. However, advertising fatigue reduces the effectiveness of repeated slogans, creating a growing demand for novel, creative, and insightful slogan generation. While recent work leverages large language models (LLMs) for this task, existing approaches often produce stylistically redundant outputs that lack a clear brand persona and appear overtly machine-generated. We argue that effective slogans should balance novelty with familiarity and propose a new paradigm that recontextualizes persona-related famous quotes for slogan generation. Well-known quotes naturally align with slogan-length text, employ rich rhetorical devices, and offer depth and insight, making them a powerful resource for creative generation. Technically, we introduce a modular framework that decomposes slogan generation into interpretable subtasks, including quote matching, structural decomposition, vocabulary replacement, and remix generation. Extensive automatic and human evaluations demonstrate marginal improvements in diversity, novelty, emotional impact, and human preference over three state-of-the-art LLM baselines.

cs.CL↗

arXiv2025

Tunable Electronic Transport in Pd$_3$O$_2$Cl$_2$ Kagome Bilayers: Interplay of Stacking Configuration and Strain

Kagome lattice bilayers offer unique opportunities for engineering electronic properties through interlayer stacking and strain. We report a comprehensive first-principles study of Pd$_3$O$_2$Cl$_2$ kagome bilayers, examining four stacking configurations (AA, AA$'$, AB, AB$'$). Our calculations reveal dramatic stacking-dependent band gap modulation from 0.08 to 0.76~eV, with the AB$'$ configuration being the most thermodynamically stable. All stackings exhibit robust mechanical stability with Young's moduli of 54.82-61.97~N/m and ductile behavior suitable for flexible electronics. Carrier effective masses show significant stacking dependence, ranging from 2.39-6.35~$m_0$ for electrons and 0.67-1.55~$m_0$ for holes. Strain engineering of the AB$'$ bilayer demonstrates non-monotonic band gap tuning and asymmetric modulation of carrier masses, with hole effective masses showing stronger strain sensitivity. These results establish Pd$_3$O$_2$Cl$_2$ bilayers as a promising platform for strain-engineered kagome-based quantum devices, where stacking order and mechanical deformation provide complementary control over electronic transport.

cond-mat.mtrl-sci↗

arXiv2025

Layer-Aware Influence for Online Data Valuation Estimation

Data-centric learning emphasizes curating high-quality training samples to boost performance rather than designing new architectures. A central problem is to estimate the influence of training sample efficiently. Prior studies largely focus on static influence measured on a converged model, overlooking how data valuation dynamically changes during optimization. This omission neglects the dynamic nature of sample influence during optimization, especially in deep models. To address the computational burden of frequent influence estimation, we develop a layer-aware online estimator that requires only loss-to-output gradients. This design avoids parameter-level and full-network gradients while preserving ranking fidelity. Extensive experiments across LLM pretraining, fine-tuning, and image classification show our method improves accuracy with substantially lower time and memory cost, making dynamic data curation efficient and scalable in practice.

cs.LG↗

arXiv2024

On the Inflation of KNN-Shapley Value

Shapley value-based data valuation methods, originating from cooperative game theory, quantify the usefulness of each individual sample by considering its contribution to all possible training subsets. Despite their extensive applications, these methods encounter the challenge of value inflation - while samples with negative Shapley values are detrimental, some with positive values can also be harmful. This challenge prompts two fundamental questions: the suitability of zero as a threshold for distinguishing detrimental from beneficial samples and the determination of an appropriate threshold. To address these questions, we focus on KNN-Shapley and propose Calibrated KNN-Shapley (CKNN-Shapley), which calibrates zero as the threshold to distinguish detrimental samples from beneficial ones by mitigating the negative effects of small-sized training subsets. Through extensive experiments, we demonstrate the effectiveness of CKNN-Shapley in alleviating data valuation inflation, detecting detrimental samples, and assessing data quality. We also extend our approach beyond conventional classification settings, applying it to diverse and practical scenarios such as learning with mislabeled data, online learning with stream data, and active learning for label annotation.

cs.LG↗

arXiv2024

Revisit, Extend, and Enhance Hessian-Free Influence Functions

Influence functions serve as crucial tools for assessing sample influence in model interpretation, subset training set selection, noisy label detection, and more. By employing the first-order Taylor extension, influence functions can estimate sample influence without the need for expensive model retraining. However, applying influence functions directly to deep models presents challenges, primarily due to the non-convex nature of the loss function and the large size of model parameters. This difficulty not only makes computing the inverse of the Hessian matrix costly but also renders it non-existent in some cases. Various approaches, including matrix decomposition, have been explored to expedite and approximate the inversion of the Hessian matrix, with the aim of making influence functions applicable to deep models. In this paper, we revisit a specific, albeit naive, yet effective approximation method known as TracIn. This method substitutes the inverse of the Hessian matrix with an identity matrix. We provide deeper insights into why this simple approximation method performs well. Furthermore, we extend its applications beyond measuring model utility to include considerations of fairness and robustness. Finally, we enhance TracIn through an ensemble strategy. To validate its effectiveness, we conduct experiments on synthetic data and extensive evaluations on noisy label detection, sample selection for large language model fine-tuning, and defense against adversarial attacks.

cs.LG↗

arXiv2022

PTCT: Patches with 3D-Temporal Convolutional Transformer Network for Precipitation Nowcasting

Precipitation nowcasting is to predict the future rainfall intensity over a short period of time, which mainly relies on the prediction of radar echo sequences. Though convolutional neural network (CNN) and recurrent neural network (RNN) are widely used to generate radar echo frames, they suffer from inductive bias (i.e., translation invariance and locality) and seriality, respectively. Recently, Transformer-based methods also gain much attention due to the great potential of Transformer structure, whereas short-term dependencies and autoregressive characteristic are ignored. In this paper, we propose a variant of Transformer named patches with 3D-temporal convolutional Transformer network (PTCT), where original frames are split into multiple patches to remove the constraint of inductive bias and 3D-temporal convolution is employed to capture short-term dependencies efficiently. After training, the inference of PTCT is performed in an autoregressive way to ensure the quality of generated radar echo frames. To validate our algorithm, we conduct experiments on two radar echo dataset: Radar Echo Guangzhou and HKO-7. The experimental results show that PTCT achieves state-of-the-art (SOTA) performance compared with existing methods.

cs.CV↗