Image de couverture de livre

When three experiments are better than two: Avoiding intractable correlated aleatoric uncertainty by leveraging a novel bias--variance tradeoff

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Scherer, Paul, Kirsch, Andreas, Taylor-King, Jake P.
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty
par: Jalayer, Reza, et autres
Publié: (2024)

A probabilistic, data-driven closure model for RANS simulations with aleatoric, model uncertainty
par: Agrawal, Atul, et autres
Publié: (2023)

Probabilistic unifying relations for modelling epistemic and aleatoric uncertainty: semantics and automated reasoning with theorem proving
par: Ye, Kangfeng, et autres
Publié: (2023)

(Implicit) Ensembles of Ensembles: Epistemic Uncertainty Collapse in Large Models
par: Kirsch, Andreas
Publié: (2024)

Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions
par: Kirsch, Andreas
Publié: (2024)

Carré du champ flow matching: better quality-generalisation tradeoff in generative models
par: Bamberger, Jacob, et autres
Publié: (2025)

On weight and variance uncertainty in neural networks for regression tasks
par: Monemi, Moein, et autres
Publié: (2025)

Impact of aleatoric, stochastic and epistemic uncertainties on project cost contingency reserves
par: Curto, David, et autres
Publié: (2024)

LLM-driven design of physics-constrained constitutive models: two agents are better than one
par: Tacke, Marius, et autres
Publié: (2026)

When Worse is Better: Navigating the compression-generation tradeoff in visual tokenization
par: Ramanujan, Vivek, et autres
Publié: (2024)

Amortizing intractable inference in large language models
par: Hu, Edward J., et autres
Publié: (2023)

It's all in your head -- fine-tuning arguments do not require aleatoric uncertainty
par: Fowlie, Andrew
Publié: (2026)

PyRelationAL: a python library for active learning research and development
par: Scherer, Paul, et autres
Publié: (2022)

Approximating intractable short ratemodel distribution with neural network
par: Knezevic, Anna, et autres
Publié: (2019)

An integrated perspective of robustness in regression through the lens of the bias-variance trade-off
par: Okuno, Akifumi
Publié: (2024)

When unlearning is free: leveraging low influence points to reduce computational costs
par: Kleiman, Anat, et autres
Publié: (2025)

RE-SAC: Disentangling aleatoric and epistemic risks in bus fleet control: A stable and robust ensemble DRL approach
par: Zhang, Yifan, et autres
Publié: (2026)

RL makes MLLMs see better than SFT
par: Song, Junha, et autres
Publié: (2025)

Towards Understanding and Avoiding Limitations of Convolutions on Graphs
par: Roth, Andreas
Publié: (2026)

Finite-sample bias-variance tradeoff with variables related to trial participation inserted into causal forest models for ensuring generalizability
par: Hamaya, Rikuta, et autres
Publié: (2025)

Simmering: Sufficient is better than optimal for training neural networks
par: Babayan, Irina, et autres
Publié: (2024)

Two is better than one: Regularized shrinkage of large minimum variance portfolio
par: Bodnar, Taras, et autres
Publié: (2022)

Provable tradeoffs in adversarially robust classification
par: Dobriban, Edgar, et autres
Publié: (2020)

Amortizing intractable inference in diffusion models for vision, language, and control
par: Venkatraman, Siddarth, et autres
Publié: (2024)

From explained variance of correlated components to PCA without orthogonality constraints
par: Chavent, Marie, et autres
Publié: (2024)

Bound by semanticity: universal laws governing the generalization-identification tradeoff
par: Nurisso, Marco, et autres
Publié: (2025)

Language models are better than humans at next-token prediction
par: Shlegeris, Buck, et autres
Publié: (2022)

Generalization through variance: how noise shapes inductive biases in diffusion models
par: Vastola, John J.
Publié: (2025)

Semantic similarity prediction is better than other semantic similarity measures
par: Herbold, Steffen
Publié: (2023)

Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications
par: Mohammadi, Marziyeh, et autres
Publié: (2025)

The Benefits and Risks of Transductive Approaches for AI Fairness
par: Razzak, Muhammed, et autres
Publié: (2024)

Business. When eight arms are better than one
Publié: (1998)

A tale of two goals: leveraging sequentiality in multi-goal scenarios
par: Serris, Olivier, et autres
Publié: (2025)

Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection
par: Zhang, Rui, et autres
Publié: (2024)

Neural networks leverage nominally quantum and post-quantum representations
par: Riechers, Paul M., et autres
Publié: (2025)

Exact and Approximate MCMC for Doubly-intractable Probabilistic Graphical Models Leveraging the Underlying Independence Model
par: Chen, Yujie, et autres
Publié: (2025)

Sharpness-diversity tradeoff: improving flat ensembles with SharpBalance
par: Lu, Haiquan, et autres
Publié: (2024)

A correlated pseudo-marginal approach to doubly intractable problems
par: Yang, Yu, et autres
Publié: (2022)

Increasing biases can be more efficient than increasing weights
par: Metta, Carlo, et autres
Publié: (2023)

Two is better than one: A Collapse-free Multi-Reward RLIF Training Framework
par: Joarder, Shourov, et autres
Publié: (2026)