Copertina

Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Roderick, Melrose, Berkenkamp, Felix, Sheikholeslami, Fatemeh, Kolter, Zico
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Bayesian Neural Networks with Domain Knowledge Priors
di: Sam, Dylan, et al.
Pubblicazione: (2024)

MALIBO: Meta-learning for Likelihood-free Bayesian Optimization
di: Pan, Jiarong, et al.
Pubblicazione: (2023)

Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression
di: Zhai, Runtian, et al.
Pubblicazione: (2023)

Equilibrium Reasoners: Learning Attractors Enables Scalable Reasoning
di: Huang, Benhao, et al.
Pubblicazione: (2026)

Why is SAM Robust to Label Noise?
di: Baek, Christina, et al.
Pubblicazione: (2024)

Model-Based Epistemic Variance of Values for Risk-Aware Policy Optimization
di: Luis, Carlos E., et al.
Pubblicazione: (2023)

AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
di: Akinwande, Victor, et al.
Pubblicazione: (2024)

Cooperative Variance Estimation and Bayesian Neural Networks for Disentangling Aleatoric and Epistemic Uncertainties
di: Yi, Jiaxiang, et al.
Pubblicazione: (2025)

FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizers
di: Williams, Joshua Nathaniel, et al.
Pubblicazione: (2024)

Offline Bayesian Aleatoric and Epistemic Uncertainty Quantification and Posterior Value Optimisation in Finite-State MDPs
di: Valdettaro, Filippo, et al.
Pubblicazione: (2024)

Mimetic Initialization of MLPs
di: Trockman, Asher, et al.
Pubblicazione: (2026)

The Epistemic Uncertainty Hole: an issue of Bayesian Neural Networks
di: Fellaji, Mohammed, et al.
Pubblicazione: (2024)

Predicting the Performance of Black-box LLMs through Follow-up Queries
di: Sam, Dylan, et al.
Pubblicazione: (2025)

Neural Network Verification with Branch-and-Bound for General Nonlinearities
di: Shi, Zhouxing, et al.
Pubblicazione: (2024)

Information-Theoretic Safe Bayesian Optimization
di: Bottero, Alessandro G., et al.
Pubblicazione: (2024)

One-Step Diffusion Distillation via Deep Equilibrium Models
di: Geng, Zhengyang, et al.
Pubblicazione: (2023)

Diffusing Differentiable Representations
di: Savani, Yash, et al.
Pubblicazione: (2024)

Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks
di: Trivedi, Puja, et al.
Pubblicazione: (2024)

Measuring Five-Nines Reliability: Sample-Efficient LLM Evaluation in Saturated Benchmarks
di: Kim, Eungyeup, et al.
Pubblicazione: (2026)

Provably Bounding Neural Network Preimages
di: Kotha, Suhas, et al.
Pubblicazione: (2023)

Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability
di: Luis, Carlos E., et al.
Pubblicazione: (2024)

A Bayesian Perspective on the Role of Epistemic Uncertainty for Delayed Generalization in In-Context Learning
di: Qchohi, Abdessamed, et al.
Pubblicazione: (2026)

EUBRL: Epistemic Uncertainty Directed Bayesian Reinforcement Learning
di: Ma, Jianfei, et al.
Pubblicazione: (2025)

Prompt Recovery for Image Generation Models: A Comparative Study of Discrete Optimizers
di: Williams, Joshua Nathaniel, et al.
Pubblicazione: (2024)

An Axiomatic Approach to Model-Agnostic Concept Explanations
di: Feng, Zhili, et al.
Pubblicazione: (2024)

Rethinking Distance Metrics for Counterfactual Explainability
di: Williams, Joshua Nathaniel, et al.
Pubblicazione: (2024)

A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting
di: Chen, Yinsong, et al.
Pubblicazione: (2026)

The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints
di: Wang, Po-Wei, et al.
Pubblicazione: (2017)

Context-Parametric Inversion: Why Instruction Finetuning Can Worsen Context Reliance
di: Goyal, Sachin, et al.
Pubblicazione: (2024)

Massive Activations in Large Language Models
di: Sun, Mingjie, et al.
Pubblicazione: (2024)

Mean Flows for One-step Generative Modeling
di: Geng, Zhengyang, et al.
Pubblicazione: (2025)

Understanding Hallucinations in Diffusion Models through Mode Interpolation
di: Aithal, Sumukh K, et al.
Pubblicazione: (2024)

Evaluating Language Model Reasoning about Confidential Information
di: Sam, Dylan, et al.
Pubblicazione: (2025)

When Should We Introduce Safety Interventions During Pretraining?
di: Sam, Dylan, et al.
Pubblicazione: (2026)

Adaptive Data Optimization: Dynamic Sample Selection with Scaling Laws
di: Jiang, Yiding, et al.
Pubblicazione: (2024)

Robust Bayesian Inference via Variational Approximations of Generalized Rho-Posteriors
di: Khribch, EL Mahdi, et al.
Pubblicazione: (2026)

Weight Ensembling Improves Reasoning in Language Models
di: Dang, Xingyu, et al.
Pubblicazione: (2025)

Forcing Diffuse Distributions out of Language Models
di: Zhang, Yiming, et al.
Pubblicazione: (2024)

A Simple and Effective Pruning Approach for Large Language Models
di: Sun, Mingjie, et al.
Pubblicazione: (2023)

Looking beyond the next token
di: Thankaraj, Abitha, et al.
Pubblicazione: (2025)