Testing and learning on distributional and set inputs

Testing and learning on distributional and set inputs

<p>As machine learning gains significant attention in many disciplines and research communities, the variety of data structures has increased, with examples including distributions and sets of observations. In this thesis, we consider sets and distributions as inputs for machine learning pr...

Bibliográfalaš dieđut
Váldodahkki:	Law, H
Eará dahkkit:	Sejdinovic, D
Materiálatiipa:	Oahppočájánas
Giella:	English
Almmustuhtton:	2019
Fáttát:	Statistics Machine learning

Geahča maid

Utilizing Statistical Tests for Comparing Machine Learning Algorithms
Dahkki: Hozan Khalid Hamarashid
Almmustuhtton: (2021-07-01)

The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data
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Almmustuhtton: (2023-01-01)

Towards trustworthy machine learning with kernels
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Almmustuhtton: (2023)

Towards data-efficient deep learning with meta-learning and symmetries
Dahkki: Xu, J
Almmustuhtton: (2023)

Kernel-based hypothesis tests: large-scale approximations and Bayesian perspectives
Dahkki: Zhang, Q
Almmustuhtton: (2019)

Enabling feature-level interpretability in non-linear latent variable models: a synthesis of statistical and machine learning techniques
Dahkki: Martens, K
Almmustuhtton: (2019)

Empirical performance of simple statistical inference-type models with feature extraction in comparison to modern machine learning methods concerning regression and classification problems
Dahkki: Dittgen, M
Almmustuhtton: (2019)

Expectancy learning from probabilistic input by infants
Dahkki: Alexa R Romberg, et al.
Almmustuhtton: (2013-01-01)

Evaluation metrics and statistical tests for machine learning
Dahkki: Oona Rainio, et al.
Almmustuhtton: (2024-03-01)

Boosting any learning algorithm with Statistically Enhanced Learning
Dahkki: Florian Felice, et al.
Almmustuhtton: (2025-01-01)

Finite Sample Analysis of Minmax Variant of Offline Reinforcement Learning for General MDPs
Dahkki: Jayanth Reddy Regatti, et al.
Almmustuhtton: (2022-01-01)

Input Complexity Affects Long-Term Retention of Statistically Learned Regularities in an Artificial Language Learning Task
Dahkki: Ethan Jost, et al.
Almmustuhtton: (2019-10-01)

Machine Learning for Neuroimaging with Scikit-Learn
Dahkki: Alexandre eAbraham, et al.
Almmustuhtton: (2014-02-01)

Machine learning and statistical models to address cluster-level confounding for the study of medical devices and surgical epidemiology using observational data
Dahkki: Du, M
Almmustuhtton: (2023)

Sample Size Analysis for Machine Learning Clinical Validation Studies
Dahkki: Daniel M. Goldenholz, et al.
Almmustuhtton: (2023-02-01)

Statistical Analysis and Machine Learning Used in the Case of Two Behavioral Tests Applied in Zebrafish Exposed to Mycotoxins
Dahkki: Tigran-Lucian Mandalian, et al.
Almmustuhtton: (2022-03-01)

Generative models: theory and applications
Dahkki: Benton, J
Almmustuhtton: (2023)

On the properties of random feature methods
Dahkki: Li, Z
Almmustuhtton: (2020)

Probabilistic models and Monte Carlo objectives in generative modelling
Dahkki: Shi, Y
Almmustuhtton: (2023)

Large scale methods for kernels, causal inference and survival modelling
Dahkki: Hu, R
Almmustuhtton: (2022)

Bayesian Experimental Design for Finding Reliable Level Set Under Input Uncertainty
Dahkki: Shogo Iwazaki, et al.
Almmustuhtton: (2020-01-01)

Intelligently learning from data
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Almmustuhtton: (2019-04-01)

Bayesian reasoning and machine learning /
Dahkki: Barber, David, 1968-
Almmustuhtton: (2012)

A Computational Approach to Statistical Learning /
Dahkki: Arnold, Taylor, author, et al.
Almmustuhtton: (2019)

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions
Dahkki: Diego Rativa, et al.
Almmustuhtton: (2018-01-01)

Diversity of Remote Sensing-Based Variable Inputs Improves the Estimation of Seasonal Maximum Freezing Depth
Dahkki: Bingquan Wang, et al.
Almmustuhtton: (2021-11-01)

Robustness, structure and hierarchy in deep generative models
Dahkki: Willetts, MJF
Almmustuhtton: (2021)

Inference on Markov random fields: methods and applications
Dahkki: Lienart, T
Almmustuhtton: (2017)

Optimizing Learning: Predicting Research Competency via Statistical Proficiency
Dahkki: Tarid Wongvorachan, et al.
Almmustuhtton: (2024-07-01)

Special Issue on Statistical and Learning-Theoretic Challenges in Data Privacy
Dahkki: Aleksandra B. Slavkovic, et al.
Almmustuhtton: (2012-07-01)

Machine learning methods for commonsense reasoning processes : interactive models /
Dahkki: Naidenova, Xenia, 1940-
Almmustuhtton: (2010)

Interference effects from machine learning matched confusable object pairs in memory assessment tasks
Dahkki: Batra, D
Almmustuhtton: (2022)

The future is coming: promising perspectives regarding the use of machine learning in renal transplantation
Dahkki: Pedro Guilherme Coelho Hannun, et al.
Almmustuhtton: (2018-10-01)

Geometry and representation learning in deep generative models
Dahkki: Mathieu, E
Almmustuhtton: (2021)

A nonparametric Bayesian method of translating machine learning scores to probabilities in clinical decision support
Dahkki: Brian Connolly, et al.
Almmustuhtton: (2017-08-01)

Neural networks and statistical learning /
Dahkki: Du, Ke-Lin, author, et al.
Almmustuhtton: (2014)

Influence of the input data on learning deep representations
Dahkki: Sylvestre-Alvise Rebuffi
Almmustuhtton: (2020)

Testing the Wind Energy Data Based on Environmental Factors Predicted by Machine Learning with Analysis of Variance
Dahkki: Yasemin Ayaz Atalan, et al.
Almmustuhtton: (2024-12-01)

Comparing conventional statistical models and machine learning in a small cohort of South African cardiac patients
Dahkki: Preesha Premsagar, et al.
Almmustuhtton: (2022-01-01)

Automated data acquisition via Bayesian experimental design
Dahkki: Ivanova, DR
Almmustuhtton: (2024)