Counterfactual explanations (CFEs) are essential for interpreting machine learning model predictions by illustrating how minimal input changes can alter outcomes in counterfactual scenarios. Effective CFEs should satisfy two key properties: feasibility and diversity. Feasibility ensures that the generated CFEs are realistic and applicable across various contexts, while diversity provides multiple perspectives on model predictions. However, existing methods face significant limitations, such as difficulty in generating multiple CFEs and reliance on a convex loss landscape, which hinders adaptability to complex scenarios and diverse applications. To overcome these challenges, we propose a novel approach for generating diverse CFEs applicable to both convex and non-convex machine learning models. Our method leverages a Farthest Point Sampling-enhanced Genetic Algorithm to improve diversity, while a sample-based Manhattan distance regularization enhances feasibility. Experimental results demonstrate that our approach outperforms state-of-the-art methods in generating CFEs that are both feasible and diverse.
Machine Learning as a Service (MLaaS) systems face a fundamental tension between regulatory demands for explainability and security vulnerabilities from model extraction attacks. While global regulations increasingly mandate transparent AI systems, emerging evidence suggests increasing model explainability can increase the vulnerability to model extraction attacks. This paper advocates for investigating the underexplored relationship between model extractability and explainability. Like how bulletproof glass provides visibility without vulnerability, we envision theoretical frameworks enabling MLaaS systems to achieve human-level explainability without enabling extraction attacks. Impeding this vision, we identify three critical technical gaps: the absence of general frameworks to predict extraction risk, standardized extractability metrics, and predictive security approaches. Institutional fragmentation across model owners, security teams, explainability researchers, and regulators further blocks coordinated solutions. The urgency of this agenda stems from rapid MLaaS adoption, expanding regulatory mandates, and increasing AI-related security incidents. Without an explainability-extractability framework, organizations must choose not to comply with regulatory transparency requirements or face exploitation through model theft and training data extraction. Success in this area can extend to complex systems beyond AI, from financial markets to government operations, wherever transparency is desirable but potentially dangerous.
The proliferation of open-sourced Large Language Models (LLMs) and diverse downstream tasks necessitates efficient model selection, given the impracticality of fine-tuning all candidates due to computational constraints. Despite the recent advances in LLM selection, a fundamental research question largely remains nascent: how can we model the dynamic behaviors of LLMs during fine-tuning, thereby enhancing our understanding of their generalization performance across diverse downstream tasks? In this work, we propose a novel theoretical framework that provides a proper lens to assess the generalization capabilities of LLMs, thereby enabling accurate and efficient LLM selection for downstream applications. In particular, we first derive a PAC-Bayesian Generalization Bound that unveils fine-tuning dynamics of LLMs and then introduce LensLLM, a Neural Tangent Kernel (NTK)-based Rectified Scaling Model that enables accurate performance predictions across diverse tasks while maintaining computational efficiency. Extensive empirical results on 3 large-scale benchmarks demonstrate that our model achieves up to 91.1% accuracy and reduces up to 88.5% computational cost in LLM selection, outperforming 5 state-of-the-art methods. We open-source our proposed LensLLM model and corresponding results at LensLLM.io.
Current solar flare predictions often lack precise quantification of their reliability, resulting in frequent false alarms, particularly when dealing with datasets skewed towards extreme events. To improve the trustworthiness of space weather forecasting, it is crucial to establish confidence intervals for model predictions. Conformal prediction, a machine learning framework, presents a promising avenue for this purpose by constructing prediction intervals that ensure valid coverage in finite samples without making assumptions about the underlying data distribution. In this study, we explore the application of conformal prediction to regression tasks in space weather forecasting. Specifically, we implement full-disk solar flare prediction using images created from magnetic field maps and adapt four pre-trained deep learning models to incorporate three distinct methods for constructing confidence intervals: conformal prediction, quantile regression, and conformalized quantile regression. Our experiments demonstrate that conformalized quantile regression achieves higher coverage rates and more favorable average interval lengths compared to alternative methods, underscoring its effectiveness in enhancing the reliability of solar weather forecasting models.
Influenza is a seasonal respiratory virus with the potential to evolve into pandemic strains. With the unprecedented COVID-19 pandemic taking the lives of millions, enhancing viral surveillance is critical for future pandemic preparedness. In this study, the authors have applied the Differential Population Growth Rate (DPGR), a sliding-window and data-driven pairwise comparison method to measure relative transmission fitness among circulating influenza A/H1N1, A/H3N2, and B strains across several time periods and geographic locations. DPGR considers variants as internal controls to reduce sampling bias. DPGR is suitable for time intervals where the logarithmic ratio between two variants follows an approximately linear trend. This study marks the first cross-species application of the DPGR framework beyond SARS-CoV-2. DPGR estimates fitness by analyzing weekly log-transformed growth ratios between two variants. The authors also have constructed distance and difference matrices to reveal epidemiological relationships and fitness dynamics. The results identify specific periods and regions where particular influenza strains exhibit stronger fitness advantages, offering insights into regional viral dominance. Finally, the authors have presented a transmission-fitness model for influenza that captures shifting evolutionary trends.
