Imagen de Portada

SAFER: A Calibrated Risk-Aware Multimodal Recommendation Model for Dynamic Treatment Regimes

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Shen, Yishan, Ye, Yuyang, Xiong, Hui, Chen, Yong
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Dynamical Regimes of Multimodal Diffusion Models
por: Albrychiewicz, Emil, et al.
Publicado: (2026)

Stage-Aware Learning for Dynamic Treatments
por: Ye, Hanwen, et al.
Publicado: (2023)

G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes
por: Xiong, Hong, et al.
Publicado: (2024)

Harnessing Multimodal Large Language Models for Multimodal Sequential Recommendation
por: Ye, Yuyang, et al.
Publicado: (2024)

Geometry-Calibrated Conformal Abstention for Language Models
por: Xu, Rui, et al.
Publicado: (2026)

Calibrated Inference for the Conditional Average Treatment Effect in the Few-Placebo Regime via Gaussian Processes
por: Uehara, Eichi
Publicado: (2026)

Ranking-Aware Calibration for Reliable Multimodal Reinforcement Learning
por: Cui, Peng, et al.
Publicado: (2026)

Near-Equivalent Q-learning Policies for Dynamic Treatment Regimes
por: Yazzourh, Sophia, et al.
Publicado: (2026)

Censoring-Aware Tree-Based Reinforcement Learning for Estimating Dynamic Treatment Regimes with Censored Outcomes
por: Paul, Animesh Kumar, et al.
Publicado: (2025)

On Multiple Robustness of Proximal Dynamic Treatment Regimes
por: Gao, Yuanshan, et al.
Publicado: (2025)

Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation
por: Wang, Shuyao, et al.
Publicado: (2024)

Risk Awareness Injection: Calibrating Vision-Language Models for Safety without Compromising Utility
por: Wang, Mengxuan, et al.
Publicado: (2026)

Restoring Calibration for Aligned Large Language Models: A Calibration-Aware Fine-Tuning Approach
por: Xiao, Jiancong, et al.
Publicado: (2025)

POLAR: A Pessimistic Model-based Policy Learning Algorithm for Dynamic Treatment Regimes
por: Zhang, Ruijia, et al.
Publicado: (2025)

Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
por: Luo, Zhiyao, et al.
Publicado: (2024)

Evaluating and Learning Optimal Dynamic Treatment Regimes under Truncation by Death
por: Park, Sihyung, et al.
Publicado: (2025)

Medical Knowledge Integration into Reinforcement Learning Algorithms for Dynamic Treatment Regimes
por: Yazzourh, Sophia, et al.
Publicado: (2024)

Treatment Effects in Extreme Regimes
por: Aloui, Ahmed, et al.
Publicado: (2023)

Trie-Aware Transformers for Generative Recommendation
por: Xu, Zhenxiang, et al.
Publicado: (2026)

Dynamical Regimes of Diffusion Models
por: Biroli, Giulio, et al.
Publicado: (2024)

Towards Understanding The Calibration Benefits of Sharpness-Aware Minimization
por: Tan, Chengli, et al.
Publicado: (2025)

RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models
por: Hao, Sai, et al.
Publicado: (2026)

LRT-Diffusion: Calibrated Risk-Aware Guidance for Diffusion Policies
por: Sun, Ximan, et al.
Publicado: (2025)

Transformer-Based Predictive Maintenance for Risk-Aware Instrument Calibration
por: Parthasarathy, Adithya, et al.
Publicado: (2026)

Confidence-Aware Multi-Field Model Calibration
por: Zhao, Yuang, et al.
Publicado: (2024)

SWAN: World-Aware Adaptive Multimodal Networks for Runtime Variations
por: Wu, Jason, et al.
Publicado: (2026)

Are Recommenders Self-Aware? Label-Free Recommendation Performance Estimation via Model Uncertainty
por: Li, Jiayu, et al.
Publicado: (2025)

SMARTCAL: An Approach to Self-Aware Tool-Use Evaluation and Calibration
por: Shen, Yuanhao, et al.
Publicado: (2024)

Performative Risk Control: Calibrating Models for Reliable Deployment under Performativity
por: Li, Victor, et al.
Publicado: (2025)

Policy Learning for Optimal Dynamic Treatment Regimes with Observational Data
por: Sakaguchi, Shosei
Publicado: (2024)

Dynamic Multimodal Sentiment Analysis: Leveraging Cross-Modal Attention for Enabled Classification
por: Lee, Hui, et al.
Publicado: (2025)

Calibrated Value-Aware Model Learning with Probabilistic Environment Models
por: Voelcker, Claas, et al.
Publicado: (2025)

Calibrated Recommendations with Contextual Bandits
por: Feijer, Diego, et al.
Publicado: (2025)

Do Physics Foundation Models Learn Generalizable Physics? A Bias-Aware Benchmark Across Physical Regimes and Distribution Shifts
por: Chu, Mengdi, et al.
Publicado: (2026)

A Regime-Aware Fusion Framework for Time Series Classification
por: Chauhan, Honey Singh, et al.
Publicado: (2025)

Tree-of-Evidence: Efficient "System 2" Search for Faithful Multimodal Grounding
por: Nnamdi, Micky C., et al.
Publicado: (2026)

Trinity: A Scenario-Aware Recommendation Framework for Large-Scale Cold-Start Users
por: Zheng, Wenhao, et al.
Publicado: (2026)

Automated and Risk-Aware Engine Control Calibration Using Constrained Bayesian Optimization
por: Vlaswinkel, Maarten, et al.
Publicado: (2025)

HopCast: Calibration of Autoregressive Dynamics Models
por: Shahid, Muhammad Bilal, et al.
Publicado: (2025)

CALIBURN: A Regime-Sensitivity Study of Operationally Calibrated Streaming Intrusion Detection
por: Youssef, Michel A.
Publicado: (2026)