Imagen de Portada

Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Belakaria, Syrine, Letham, Benjamin, Doppa, Janardhan Rao, Engelhardt, Barbara, Ermon, Stefano, Bakshy, Eytan
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Non-Myopic Multi-Objective Bayesian Optimization
por: Belakaria, Syrine, et al.
Publicado: (2024)

Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization
por: Ahmadianshalchi, Alaleh, et al.
Publicado: (2024)

Preference-Guided Diffusion for Multi-Objective Offline Optimization
por: Annadani, Yashas, et al.
Publicado: (2025)

Sharpe Ratio-Guided Active Learning for Preference Optimization in RLHF
por: Belakaria, Syrine, et al.
Publicado: (2025)

Sample Efficient Preference Alignment in LLMs via Active Exploration
por: Mehta, Viraj, et al.
Publicado: (2023)

Embedding by Elicitation: Dynamic Representations for Bayesian Optimization of System Prompts
por: Lin, Zhiyuan Jerry, et al.
Publicado: (2026)

LILO: Bayesian Optimization with Natural Language Feedback
por: Kobalczyk, Katarzyna, et al.
Publicado: (2025)

Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning
por: Namkoong, Hongseok, et al.
Publicado: (2020)

Offline Model-Based Optimization via Policy-Guided Gradient Search
por: Chemingui, Yassine, et al.
Publicado: (2024)

Robust Gaussian Processes via Relevance Pursuit
por: Ament, Sebastian, et al.
Publicado: (2024)

Constraint-Adaptive Policy Switching for Offline Safe Reinforcement Learning
por: Chemingui, Yassine, et al.
Publicado: (2024)

Online Optimization for Offline Safe Reinforcement Learning
por: Chemingui, Yassine, et al.
Publicado: (2025)

Learning Surrogates for Offline Black-Box Optimization via Gradient Matching
por: Hoang, Minh, et al.
Publicado: (2025)

Clinician-in-the-Loop Smart Home System to Detect Urinary Tract Infection Flare-Ups via Uncertainty-Aware Decision Support
por: Ugwu, Chibuike E., et al.
Publicado: (2025)

BONSAI: Bayesian Optimization with Natural Simplicity and Interpretability
por: Daulton, Samuel, et al.
Publicado: (2026)

Experimenting, Fast and Slow: Bayesian Optimization of Long-term Outcomes with Online Experiments
por: Feng, Qing, et al.
Publicado: (2025)

Streamflow Prediction with Uncertainty Quantification for Water Management: A Constrained Reasoning and Learning Approach
por: Gharsallaoui, Mohammed Amine, et al.
Publicado: (2024)

SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking
por: Cundy, Chris, et al.
Publicado: (2023)

Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise
por: Yang, Xiaoxuan, et al.
Publicado: (2021)

Scaling Gaussian Processes for Learning Curve Prediction via Latent Kronecker Structure
por: Lin, Jihao Andreas, et al.
Publicado: (2024)

Dataflow-Aware PIM-Enabled Manycore Architecture for Deep Learning Workloads
por: Sharma, Harsh, et al.
Publicado: (2024)

Informed Initialization for Bayesian Optimization and Active Learning
por: Hvarfner, Carl, et al.
Publicado: (2025)

Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution
por: Wu, Tailin, et al.
Publicado: (2024)

Inductive Moment Matching
por: Zhou, Linqi, et al.
Publicado: (2025)

Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active Learning
por: Das, Shubhomoy, et al.
Publicado: (2019)

Generative Modeling with Flux Matching
por: Pao-Huang, Peter, et al.
Publicado: (2026)

Self-Refining Diffusion Samplers: Enabling Parallelization via Parareal Iterations
por: Selvam, Nikil Roashan, et al.
Publicado: (2024)

Conformal Prediction Sets for Deep Generative Models via Reduction to Conformal Regression
por: Shahrokhi, Hooman, et al.
Publicado: (2025)

Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation
por: Manvi, Rohin, et al.
Publicado: (2024)

Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weights
por: Qing, Jixiang, et al.
Publicado: (2026)

Black-Box Optimization From Small Offline Datasets via Meta Learning with Synthetic Tasks
por: Fadhel, Azza, et al.
Publicado: (2026)

Empirical Gaussian Processes
por: Lin, Jihao Andreas, et al.
Publicado: (2026)

Global Sensitivity Analysis for Engineering Design Based on Individual Conditional Expectations
por: Palar, Pramudita Satria, et al.
Publicado: (2025)

Mixed Likelihood Variational Gaussian Processes
por: Wu, Kaiwen, et al.
Publicado: (2025)

Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures
por: Dhingra, Pratyush, et al.
Publicado: (2025)

Kernel Density Bayesian Inverse Reinforcement Learning
por: Mandyam, Aishwarya, et al.
Publicado: (2023)

CMT: Mid-Training for Efficient Learning of Consistency, Mean Flow, and Flow Map Models
por: Hu, Zheyuan, et al.
Publicado: (2025)

APRIL: Annotations for Policy evaluation with Reliable Inference from LLMs
por: Mandyam, Aishwarya, et al.
Publicado: (2025)

MADiff: Offline Multi-agent Learning with Diffusion Models
por: Zhu, Zhengbang, et al.
Publicado: (2023)

Bayesian Analysis of Combinatorial Gaussian Process Bandits
por: Sandberg, Jack, et al.
Publicado: (2023)