:: Library Catalog

Image de couverture de livre

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Zhang, Xinyu, Martinelli, Julien, John, ST
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	https://arxiv.org/abs/2504.10169
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Position: Biology is the Challenge Physics-Informed ML Needs to Evolve
par: Martinelli, Julien
Publié: (2025)

PABBO: Preferential Amortized Black-Box Optimization
par: Zhang, Xinyu, et autres
Publié: (2025)

In-Context Multi-Objective Optimization
par: Zhang, Xinyu, et autres
Publié: (2025)

Non-separable Spatio-temporal Graph Kernels via SPDEs
par: Nikitin, Alexander, et autres
Publié: (2021)

Robust and Computation-Aware Gaussian Processes
par: Sinaga, Marshal Arijona, et autres
Publié: (2025)

Time-Aware Latent Space Bayesian Optimization
par: Vu, Tuan A., et autres
Publié: (2026)

Proxy-informed Bayesian transfer learning with unknown sources
par: Sloman, Sabina J., et autres
Publié: (2024)

Anchor-Based Heteroscedastic Noise for Preferential Bayesian Optimization
par: Sinaga, Marshal Arijona, et autres
Publié: (2024)

Identifying latent state transition in non-linear dynamical systems
par: Hızlı, Çağlar, et autres
Publié: (2024)

Data-driven Discovery of Digital Twins in Biomedical Research
par: Métayer, Clémence, et autres
Publié: (2025)

In-Context Black-Box Optimization with Unreliable Feedback
par: Blumer, Nicolas Samuel, et autres
Publié: (2026)

Online Sharp-Calibrated Bayesian Optimization
par: Sinaga, Marshal Arijona, et autres
Publié: (2026)

Bayesian Active Learning in the Presence of Nuisance Parameters
par: Sloman, Sabina J., et autres
Publié: (2023)

Bayesian Nonparametric Mixed-Effect ODEs with Gaussian Processes
par: Martinelli, Julien, et autres
Publié: (2026)

Learning Relevant Contextual Variables Within Bayesian Optimization
par: Martinelli, Julien, et autres
Publié: (2023)

Learning accurate and interpretable tree-based models
par: Balcan, Maria-Florina, et autres
Publié: (2024)

Self-interpreting Adversarial Images
par: Zhang, Tingwei, et autres
Publié: (2024)

Gradient-free stochastic optimization for additive models
par: Akhavan, Arya, et autres
Publié: (2025)

Discovering interpretable models of scientific image data with deep learning
par: Soelistyo, Christopher J., et autres
Publié: (2024)

Plastic tensor networks for interpretable generative modeling
par: Akamatsu, Katsuya O., et autres
Publié: (2025)

On the transferability of Sparse Autoencoders for interpreting compressed models
par: Gupte, Suchit, et autres
Publié: (2025)

Mechanistic interpretability for steering vision-language-action models
par: Häon, Bear, et autres
Publié: (2025)

Classification by sparse generalized additive models
par: Abramovich, Felix
Publié: (2022)

Advancing climate model interpretability: Feature attribution for Arctic melt anomalies
par: Ale, Tolulope, et autres
Publié: (2025)

mlr3summary: Concise and interpretable summaries for machine learning models
par: Dandl, Susanne, et autres
Publié: (2024)

Improving clinical interpretability of linear neuroimaging models through feature whitening
par: Petiton, Sara, et autres
Publié: (2026)

Towards detailed and interpretable hybrid modeling of continental-scale bird migration
par: Lippert, Fiona, et autres
Publié: (2024)

Multi-megabase scale genome interpretation with genetic language models
par: Träuble, Frederik, et autres
Publié: (2025)

Optimal ablation for interpretability
par: Li, Maximilian, et autres
Publié: (2024)

xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
par: Beaglehole, Daniel, et autres
Publié: (2025)

AudioProtoPNet: An interpretable deep learning model for bird sound classification
par: Heinrich, René, et autres
Publié: (2024)

A deep causal inference model for fully-interpretable travel behaviour analysis
par: Kamal, Kimia, et autres
Publié: (2024)

Forest-ORE: Mining Optimal Rule Ensemble to interpret Random Forest models
par: Maissae, Haddouchi, et autres
Publié: (2024)

Group selection and shrinkage: Structured sparsity for semiparametric additive models
par: Thompson, Ryan, et autres
Publié: (2021)

Uncertainty quantification in model discovery by distilling interpretable material constitutive models from Gaussian process posteriors
par: Anton, David, et autres
Publié: (2025)

A survey and taxonomy of methods interpreting random forest models
par: Haddouchi, Maissae, et autres
Publié: (2024)

Are machine learning interpretations reliable? A stability study on global interpretations
par: Gan, Luqin, et autres
Publié: (2025)

Challenging reaction prediction models to generalize to novel chemistry
par: Bradshaw, John, et autres
Publié: (2025)

Pilot selection in the era of Virtual reality: algorithms for accurate and interpretable machine learning models
par: Ke, Luoma, et autres
Publié: (2025)

Hybrid additive modeling with partial dependence for supervised regression and dynamical systems forecasting
par: Claes, Yann, et autres
Publié: (2023)