Copertina

Proactive Routing to Interpretable Surrogates with Distribution-Free Safety Guarantees

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Uddin, Iqtedar, Khider, Mazin, Bauer, André
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Conformal Approach To Gaussian Process Surrogate Evaluation With Coverage Guarantees
di: Jaber, Edgar, et al.
Pubblicazione: (2024)

FaiREE: Fair Classification with Finite-Sample and Distribution-Free Guarantee
di: Li, Puheng, et al.
Pubblicazione: (2022)

Statistical Inference Leveraging Synthetic Data with Distribution-Free Guarantees
di: Bashari, Meshi, et al.
Pubblicazione: (2025)

SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees
di: Shi, Tianyu, et al.
Pubblicazione: (2024)

Leveraging Interpretability in the Transformer to Automate the Proactive Scaling of Cloud Resources
di: Ba, Amadou, et al.
Pubblicazione: (2024)

Feature-Based Interpretable Surrogates for Optimization
di: Goerigk, Marc, et al.
Pubblicazione: (2024)

Towards Robust Interpretable Surrogates for Optimization
di: Goerigk, Marc, et al.
Pubblicazione: (2024)

One-Stage Top-$k$ Learning-to-Defer: Score-Based Surrogates with Theoretical Guarantees
di: Montreuil, Yannis, et al.
Pubblicazione: (2025)

Optimising Distributions with Natural Gradient Surrogates
di: So, Jonathan, et al.
Pubblicazione: (2023)

Proactive Depot Discovery: A Generative Framework for Flexible Location-Routing
di: Qu, Site, et al.
Pubblicazione: (2025)

Adaptive Sentencing Prediction with Guaranteed Accuracy and Legal Interpretability
di: Jin, Yifei, et al.
Pubblicazione: (2025)

Mathematical Foundation of Interpretable Equivariant Surrogate Models
di: Colombini, Jacopo Joy, et al.
Pubblicazione: (2025)

Surrogate Fitness Metrics for Interpretable Reinforcement Learning
di: Altmann, Philipp, et al.
Pubblicazione: (2025)

Surrogate Interpretable Graph for Random Decision Forests
di: Dubey, Akshat, et al.
Pubblicazione: (2025)

Theoretically Guaranteed Distribution Adaptable Learning
di: Xu, Chao, et al.
Pubblicazione: (2024)

Mastering Multiple-Expert Routing: Realizable $H$-Consistency and Strong Guarantees for Learning to Defer
di: Mao, Anqi, et al.
Pubblicazione: (2025)

Physics-Inspired Binary Neural Networks: Interpretable Compression with Theoretical Guarantees
di: Eamaz, Arian, et al.
Pubblicazione: (2025)

Interpreting CFD Surrogates through Sparse Autoencoders
di: Hu, Yeping, et al.
Pubblicazione: (2025)

Route Sparse Autoencoder to Interpret Large Language Models
di: Shi, Wei, et al.
Pubblicazione: (2025)

An Analysis of Safety Guarantees in Multi-Task Bayesian Optimization
di: Luebsen, Jannis O., et al.
Pubblicazione: (2025)

Coverage-Guaranteed Prediction Sets for Out-of-Distribution Data
di: Zou, Xin, et al.
Pubblicazione: (2024)

Distributional Offline Policy Evaluation with Predictive Error Guarantees
di: Wu, Runzhe, et al.
Pubblicazione: (2023)

Survival Kernets: Scalable and Interpretable Deep Kernel Survival Analysis with an Accuracy Guarantee
di: Chen, George H.
Pubblicazione: (2022)

No Certificate for Alignment: Two Independent Impossibilities and the Pareto Frontier of Achievable Safety Guarantees
di: Agarwal, Ayushi
Pubblicazione: (2026)

Calibrate-Then-Delegate: Safety Monitoring with Risk and Budget Guarantees via Model Cascades
di: Pona, Edoardo, et al.
Pubblicazione: (2026)

FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models
di: Raut, Riddhiman, et al.
Pubblicazione: (2025)

Dialectics of Alignment: Harnessing Unsafe Knowledge for Dynamic Safety Routing
di: Hashemzadeh, Maryam, et al.
Pubblicazione: (2026)

Practical and Robust Safety Guarantees for Advanced Counterfactual Learning to Rank
di: Gupta, Shashank, et al.
Pubblicazione: (2024)

Disentangled and Distilled Encoder for Out-of-Distribution Reasoning with Rademacher Guarantees
di: Rahiminasab, Zahra, et al.
Pubblicazione: (2025)

Handling Missing Data in Downstream Tasks With Distribution-Preserving Guarantees
di: Bordoloi, Rahul, et al.
Pubblicazione: (2025)

Anant-Net: Breaking the Curse of Dimensionality with Scalable and Interpretable Neural Surrogate for High-Dimensional PDEs
di: Menon, Sidharth S., et al.
Pubblicazione: (2025)

FactTest: Factuality Testing in Large Language Models with Finite-Sample and Distribution-Free Guarantees
di: Nie, Fan, et al.
Pubblicazione: (2024)

Frequentist Guarantees of Distributed (Non)-Bayesian Inference
di: Wu, Bohan, et al.
Pubblicazione: (2023)

Bridging Chemists and AI: An Expert-Augmented Framework for Interpretable Route Evaluation
di: Guo, Yujia, et al.
Pubblicazione: (2026)

Formal Mechanistic Interpretability: Automated Circuit Discovery with Provable Guarantees
di: Hadad, Itamar, et al.
Pubblicazione: (2026)

Faithful and Interpretable Explanations for Complex Ensemble Time Series Forecasts using Surrogate Models and Forecastability Analysis
di: Zhao, Yikai, et al.
Pubblicazione: (2025)

JacQuant: STE-Free Quantization-Aware Training via Learned Jacobian Surrogates
di: Yi, Kai, et al.
Pubblicazione: (2026)

Guaranteeing Out-Of-Distribution Detection in Deep RL via Transition Estimation
di: Prashant, Mohit, et al.
Pubblicazione: (2025)

TRACES: Proactive Safety Auditing for Multi-Turn LLM Agents via Trajectory-State Modeling
di: Li, Jiaqian, et al.
Pubblicazione: (2026)

Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI
di: Ji, Wenlong, et al.
Pubblicazione: (2025)