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Washington State University

Academic institutionnorthamerica · us
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Research library157linked papers
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Selected work

Representative Papers

Facets of Disparate Impact: Evaluating Legally Consistent Bias in Machine Learning

Oct 21, 2024International Conference on Information and Knowledge Management

This paper addresses the misalignment between algorithmic bias assessment and legal standards by proposing a quantification framework rigorously grounded in U.S. anti-discrimination law. Methodologically, it distinguishes legally salient discriminatory testing from systemic disparity through legal contextualization, and introduces the Objective Fairness Index (OFI)—a metric integrating objective test theory and measurement stability, using marginal benefit as a proxy to quantify legal compliance of algorithmic decisions. Its key contribution lies in being the first fairness metric to embed legal admissibility directly into its design, enabling a paradigm shift in algorithmic auditing from statistical fairness to legally grounded fairness. Empirical evaluation on real-world judicial prediction systems—including COMPAS—demonstrates that OFI reliably detects unlawful discrimination, offering regulators and auditors the first quantitative tool with both legal interpretability and operational utility.

3 citations1 influentialRead paper

Boosting Offline Optimizers with Surrogate Sensitivity

Mar 06, 2025International Conference on Machine Learning

Offline optimization of expensive black-box functions in materials engineering suffers from poor robustness due to the high sensitivity of surrogate models to parameter perturbations. Method: We propose, for the first time, an optimizable surrogate sensitivity metric and design a sensitivity-aware regularization method orthogonal to existing frameworks. This approach integrates gradient-based sensitivity analysis with deep-learning-based surrogate modeling and is compatible with mainstream paradigms such as offline Bayesian optimization. Contribution/Results: Evaluated on multiple materials design benchmarks, our method significantly improves optimization success rate (average gain of +23.6%) and solution quality (objective value improvement up to 17.4%). Empirical results demonstrate that explicit sensitivity control delivers critical performance gains for offline optimization of expensive black-box functions in materials engineering.

2 citations1 influentialRead paper

Revisiting Kernel Attention with Correlated Gaussian Process Representation

Feb 27, 2025

This work addresses the limitation in Transformer-based uncertainty calibration—specifically, the restrictive symmetric kernel assumption of Gaussian processes (GPs) in existing GP-Transformer models. We propose a Correlated Gaussian Process (CGP) attention mechanism. Methodologically, we formulate self-attention as the cross-covariance between two correlated yet asymmetric GPs, thereby relaxing the symmetry constraint inherent in conventional GP-Transformers and enhancing representational capacity. To ensure scalability, we further introduce a sparse variational CGP approximation. Empirical evaluation across multiple benchmark tasks demonstrates that our approach consistently outperforms state-of-the-art GP-based Transformers, validating substantial improvements in uncertainty calibration accuracy, modeling flexibility, and predictive performance.

2 citationsRead paper

Comprehensive Performance Evaluation of YOLO11, YOLOv10, YOLOv9 and YOLOv8 on Detecting and Counting Fruitlet in Complex Orchard Environments

Jul 01, 2024

Accurate detection and counting of immature and young apples in complex orchard environments remains challenging due to occlusion, varying lighting, and dense foliage. Method: This study systematically evaluates 22 model configurations spanning YOLOv8 to YOLOv11 across four apple cultivars (e.g., Scifresh, Honeycrisp), using field-collected data from both iPhone and industrial machine vision sensors. Performance is assessed via mAP@50, recall, and millisecond-level inference latency. Contribution/Results: We present the first multi-dimensional comparison of state-of-the-art models—including YOLOv11 (s/m/n) and YOLOv9 Gelan-series—under real-world orchard conditions, and propose a “lightweightness–accuracy–speed” co-design principle for agricultural automation. Results show YOLOv11s and YOLOv9 Gelan-base achieve top-tier mAP@50 of 0.933 and 0.935, respectively; YOLOv11n attains ultra-low latency of 2.4 ms—over 40% faster than YOLOv8n—demonstrating feasibility of edge-deployable, real-time fruit counting.

2 citationsRead paper

Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data

Feb 06, 2026

This work addresses the challenge of semantic misalignment in federated learning caused by heterogeneous multimodal client data and missing input-level features. To tackle this issue, the paper proposes the first federated multimodal prompt tuning framework, which enables collaborative optimization and effective fusion of prompt instructions across clients and modalities. The approach integrates client-specific prompt tuning with a server-side semantic-aware aggregation mechanism, establishing a novel paradigm that supports prompt alignment and aggregation under heterogeneous missing-data patterns. By innovatively combining federated learning with multimodal prompt tuning, the method achieves significant performance gains over state-of-the-art baselines across multiple multimodal benchmark datasets, demonstrating its effectiveness and robustness.

1 citationsRead paper
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